diff --git a/.gitignore b/.gitignore
index 713727a5..ce1ce4de 100644
--- a/.gitignore
+++ b/.gitignore
@@ -71,8 +71,9 @@ instance/
 # Sphinx documentation
 docs/_build/
 
-# Reference data downloaded by validation notebooks
+# Reference data downloaded by example and validation notebooks
 docs/validation/data/
+docs/examples/data/
 
 # PyBuilder
 .pybuilder/
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0078d840..b5d74636 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -27,6 +27,8 @@ Added:
  - Utility methods for the WGS 84 ellipsoid (module `tatc.utils.ellipsoid`): `geodetic_to_rectangular`, `rectangular_to_geodetic`, `compute_ellipsoid_intersection` (intersections of rays with the ellipsoid), and `compute_tangent_point` (the point of minimum geodetic altitude on a line), utility method `compute_vnb_frame` (a satellite's velocity, normal, and binormal frame), and utility method `hash_geometry` (a compact hash identifying a geometry).
  - Runtime configurations `footprint_points_radar_azimuthal`, `repeat_cycle_delta_semimajor_axis_m`, and `nutation_interpolation_minutes` (the step of a cached table from which nutation angles are interpolated, or `null` to compute them for every time), and constants `EFFECTIVE_EARTH_RADIUS_FACTOR`, `EARTH_ROTATION_RATE`, and `TROPICAL_YEAR_S`.
  - Validation notebooks in `docs/validation` comparing TAT-C with data from operational missions.
+ - Example notebook `docs/examples/RunOSSEMonsoon.ipynb`, an observing system simulation experiment (OSSE) that samples cold cloud top features of the 7-km GEOS-5 Nature Run over the American Southwest with the swaths of current (ATMS, CrIS) and notional future sounders, compares their horizontal resolution, and trades the number of satellites and orbit planes of the constellation.
+ - Example notebook `docs/examples/RunOSSEHurricane.ipynb`, an OSSE of a simulated North Atlantic hurricane in the GEOS-5 Nature Run (located with its coarsened 0.5 degree collection), which samples its cold cloud tops and heavy precipitation with the same sensors.
  - Optional dependencies `preprocess` and `validation`, and development dependency `pytest-xdist` to run tests in parallel (e.g. `pytest -n 8`).
 
 Changed:
diff --git a/docs/examples/RunOSSEHurricane.ipynb b/docs/examples/RunOSSEHurricane.ipynb
new file mode 100644
index 00000000..2e2e3574
--- /dev/null
+++ b/docs/examples/RunOSSEHurricane.ipynb
@@ -0,0 +1,2180 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e8f9a4a0",
+   "metadata": {},
+   "source": [
+    "# OSSE: North Atlantic Hurricane\n",
+    "\n",
+    "An observing system simulation experiment (OSSE) evaluates how well an observing system, real or planned, would observe a phenomenon, by sampling a high-resolution model simulation (the *nature run*) as the system would. This example is an OSSE of a **hurricane** in the 7-km [GEOS-5 Nature Run (G5NR)](https://gmao.gsfc.nasa.gov/global_mesoscale/7km-G5NR/). In late August of the Nature Run's second year, a simulated hurricane crosses the North Atlantic from near the Cape Verde Islands (about 15 N, 27 W on 20 August) to Nova Scotia (30 August). This example covers two days, 25 and 26 August, when the storm passes north of Puerto Rico and Hispaniola and begins to recurve to the north.\n",
+    "\n",
+    "The observing systems are the same as in the [monsoon OSSE](RunOSSEMonsoon.ipynb): the atmospheric sounders on the current NOAA/NASA polar-orbiting satellites ([ATMS](../validation/ValidateImagerATMS.ipynb) and [CrIS](../validation/ValidateImagerCrIS.ipynb)) and two notional future sounders with finer resolution. Unlike the monsoon, whose features are scattered convective cells over land with a strong diurnal cycle, the features here are organized around one large storm over the ocean. Two kinds of features are defined:\n",
+    "\n",
+    "* **cold cloud tops**, as in the monsoon OSSE, which outline the storm's central dense overcast and the convective cells of its rainbands; and\n",
+    "* **heavy precipitation**, which outlines its eyewall and rainbands, where microwave sounders sense the rain and ice beneath the cloud tops.\n",
+    "\n",
+    "The experiment asks the same two questions about each feature:\n",
+    "\n",
+    "1. **Is it observed?** Does a sounder's swath cover the feature?\n",
+    "2. **Is it resolved?** Is the part of the feature that is observed at least a few resolution elements (footprints or the spacing between them) across?\n",
+    "\n",
+    "The steps are:\n",
+    "\n",
+    "1. configure the scenario;\n",
+    "2. locate the storm in the coarsened (0.5 degree) Nature Run;\n",
+    "3. configure the Nature Run (7 km) around the storm;\n",
+    "4. define the features (cold cloud tops and heavy precipitation) in each 30-minute frame;\n",
+    "5. define the sensors;\n",
+    "6. compute the ground tracks (swaths) of the satellites over the study region with TAT-C;\n",
+    "7. determine whether each feature is observed and resolved by each sensor; and\n",
+    "8. generate a visualization and statistics.\n",
+    "\n",
+    "The example is designed to run in a few minutes, after a one-time download of the Nature Run around the storm (about 100 MB, which takes several minutes). It requires the optional dependencies for OSSE examples (`pip install tatc[osse]`)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30c7a886",
+   "metadata": {},
+   "source": [
+    "## Configure the Scenario\n",
+    "\n",
+    "The study region (15-34 N, 77-58.5 W) is a box around the storm's track over the two days, at least 6 degrees from the storm center at all times, so that it contains the storm's clouds and rainbands as well as some convection of its environment. It is mostly ocean, with the northern coasts of Puerto Rico and Hispaniola and the Bahamas.\n",
+    "\n",
+    "The Nature Run covers 15 May 2005 to 16 June 2007 and does not correspond to any real year. As in the monsoon OSSE, the satellite orbits (those of 25 August 2026, from operational element sets) are paired with the Nature Run on the same calendar date, here in its second year (2006), in which the storm occurs. Because the orbits are sun-synchronous, the local solar times at which they observe do not depend on the date."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "dd3cd108",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:47:33.010866Z",
+     "iopub.status.busy": "2026-10-24T21:47:33.010866Z",
+     "iopub.status.idle": "2026-10-24T21:47:33.965598Z",
+     "shell.execute_reply": "2026-10-24T21:47:33.965598Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from datetime import datetime, timedelta, timezone\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import geopandas as gpd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import shapely\n",
+    "from shapely.geometry import box\n",
+    "\n",
+    "DATA_DIR = Path(\"data\") / \"osse\"\n",
+    "DATA_DIR.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "# study region: a box around the storm's track over the two days\n",
+    "domain = box(-77, 15, -58.5, 34)\n",
+    "# the simulated orbits and the nature run (the same calendar date in the Nature Run's second year)\n",
+    "start = datetime(2026, 8, 25, tzinfo=timezone.utc)\n",
+    "nature_start = start.replace(year=2006)\n",
+    "duration = timedelta(days=2)\n",
+    "frame_duration = timedelta(minutes=30)  # the Nature Run's time-averaging interval\n",
+    "n_frames = int(duration / frame_duration)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7186d575",
+   "metadata": {},
+   "source": [
+    "## Locate the Storm\n",
+    "\n",
+    "The full-resolution (7 km) Nature Run is too large to search a whole ocean basin over a season. Its coarsened collections (0.5 degrees, hourly) are about 200 times smaller per time step and suffice to find and track storms: this storm was found as the deepest low in the sea-level pressure (`slp`) of the coarsened collection over the North Atlantic around 25 August. Here, the storm center at each hour is the minimum sea-level pressure in the study region, and the storm's strength is given by the minimum pressure and the maximum 10 m wind speed (`u10m`, `v10m`) in the region. Both are smoothed by the coarse grid, so the storm at full resolution is somewhat deeper and stronger."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bdab5b36",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:47:33.967845Z",
+     "iopub.status.busy": "2026-10-24T21:47:33.967845Z",
+     "iopub.status.idle": "2026-10-24T21:47:35.511345Z",
+     "shell.execute_reply": "2026-10-24T21:47:35.511345Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>min_slp_hpa</th>\n",
+       "      <th>max_wind_ms</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2006-08-25 00:00:00</th>\n",
+       "      <td>21.0</td>\n",
+       "      <td>-64.5</td>\n",
+       "      <td>962.900024</td>\n",
+       "      <td>39.299999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2006-08-25 06:00:00</th>\n",
+       "      <td>21.5</td>\n",
+       "      <td>-66.0</td>\n",
+       "      <td>960.099976</td>\n",
+       "      <td>41.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2006-08-25 12:00:00</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>-67.5</td>\n",
+       "      <td>962.500000</td>\n",
+       "      <td>39.200001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2006-08-25 18:00:00</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>-68.5</td>\n",
+       "      <td>963.000000</td>\n",
+       "      <td>40.200001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2006-08-26 00:00:00</th>\n",
+       "      <td>23.5</td>\n",
+       "      <td>-69.5</td>\n",
+       "      <td>958.700012</td>\n",
+       "      <td>42.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2006-08-26 06:00:00</th>\n",
+       "      <td>24.5</td>\n",
+       "      <td>-70.0</td>\n",
+       "      <td>958.299988</td>\n",
+       "      <td>40.599998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2006-08-26 12:00:00</th>\n",
+       "      <td>25.5</td>\n",
+       "      <td>-70.5</td>\n",
+       "      <td>955.700012</td>\n",
+       "      <td>41.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2006-08-26 18:00:00</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>-70.5</td>\n",
+       "      <td>956.599976</td>\n",
+       "      <td>44.799999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2006-08-27 00:00:00</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>-71.0</td>\n",
+       "      <td>951.799988</td>\n",
+       "      <td>41.799999</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      lat   lon  min_slp_hpa  max_wind_ms\n",
+       "time                                                     \n",
+       "2006-08-25 00:00:00  21.0 -64.5   962.900024    39.299999\n",
+       "2006-08-25 06:00:00  21.5 -66.0   960.099976    41.500000\n",
+       "2006-08-25 12:00:00  22.0 -67.5   962.500000    39.200001\n",
+       "2006-08-25 18:00:00  23.0 -68.5   963.000000    40.200001\n",
+       "2006-08-26 00:00:00  23.5 -69.5   958.700012    42.000000\n",
+       "2006-08-26 06:00:00  24.5 -70.0   958.299988    40.599998\n",
+       "2006-08-26 12:00:00  25.5 -70.5   955.700012    41.500000\n",
+       "2006-08-26 18:00:00  27.0 -70.5   956.599976    44.799999\n",
+       "2006-08-27 00:00:00  28.0 -71.0   951.799988    41.799999"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import xarray as xr\n",
+    "\n",
+    "G5NR_URL = \"https://opendap.nccs.nasa.gov/dods/OSSE/G5NR/Ganymed/7km\"\n",
+    "COARSE_URL = f\"{G5NR_URL}/0.5000_deg/inst/inst01hr_2d_met1_Cx\"\n",
+    "\n",
+    "\n",
+    "def naive(time):\n",
+    "    \"\"\"A time without its time zone, as the Nature Run's time coordinate.\"\"\"\n",
+    "    return time.replace(tzinfo=None)\n",
+    "\n",
+    "\n",
+    "coarse_file = DATA_DIR / f\"g5nr_hurricane_coarse_{nature_start:%Y%m%d}_{duration.days}d.nc\"\n",
+    "if not coarse_file.exists():\n",
+    "    minx, miny, maxx, maxy = domain.bounds\n",
+    "    with xr.open_dataset(COARSE_URL) as remote:\n",
+    "        remote[[\"slp\", \"u10m\", \"v10m\"]].sel(\n",
+    "            time=slice(naive(nature_start), naive(nature_start + duration)),\n",
+    "            lon=slice(minx, maxx),\n",
+    "            lat=slice(miny, maxy),\n",
+    "        ).load().to_netcdf(coarse_file)\n",
+    "coarse = xr.load_dataset(coarse_file)\n",
+    "\n",
+    "# the storm center (the minimum sea-level pressure in the study region) and strength at each hour\n",
+    "slp = coarse.slp.stack(cell=(\"lat\", \"lon\"))\n",
+    "center = slp.isel(cell=slp.argmin(\"cell\"))\n",
+    "track = pd.DataFrame(\n",
+    "    {\n",
+    "        \"lat\": center.lat.values,\n",
+    "        \"lon\": center.lon.values,\n",
+    "        \"min_slp_hpa\": center.values / 100,\n",
+    "        \"max_wind_ms\": np.hypot(coarse.u10m, coarse.v10m).max((\"lat\", \"lon\")).values,\n",
+    "    },\n",
+    "    index=pd.DatetimeIndex(coarse.time.values, name=\"time\"),\n",
+    ")\n",
+    "track.iloc[::6].round(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3eaf52bb",
+   "metadata": {},
+   "source": [
+    "## Configure the Nature Run\n",
+    "\n",
+    "The full-resolution (0.0625 degree, about 7 km) fields are needed only around the storm, so only the study region is requested. As in the monsoon OSSE, this example uses the single-level, 30-minute time-averaged diagnostics, and reads the upwelling longwave flux at the top of the atmosphere (`lwtup`), a proxy for cloud-top temperature, the total precipitation rate (`prectot`), and its convective part (`preccon`). In a hurricane, much of the rain falls from the storm's resolved circulation rather than from parameterized convection, so the features use the total rate. The fields are requested in 12-hour chunks, each checked for a field of zeros, which the server has been found to return in place of a field for some large requests, and saved in a local `data/osse` directory, so later runs are fast."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "08da3452",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:47:35.513632Z",
+     "iopub.status.busy": "2026-10-24T21:47:35.513632Z",
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+     "shell.execute_reply": "2026-10-24T21:47:35.647867Z"
+    }
+   },
+   "outputs": [
+    {
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+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 104MB\n",
+       "Dimensions:  (time: 96, lat: 305, lon: 297)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 768B 2006-08-25T00:15:00 ... 2006-08-26T23...\n",
+       "  * lat      (lat) float64 2kB 15.0 15.06 15.12 15.19 ... 33.81 33.88 33.94 34.0\n",
+       "  * lon      (lon) float64 2kB -77.0 -76.94 -76.88 ... -58.62 -58.56 -58.5\n",
+       "Data variables:\n",
+       "    lwtup    (time, lat, lon) float32 35MB 172.8 172.8 173.4 ... 291.4 290.9\n",
+       "    prectot  (time, lat, lon) float32 35MB 1.875e-05 1.425e-05 ... 1.246e-06\n",
+       "    preccon  (time, lat, lon) float32 35MB 1.785e-05 1.389e-05 ... 1.241e-06\n",
+       "Attributes:\n",
+       "    title:                2d,30-Minute,Time-Averaged,Single-Level,Full Resolu...\n",
+       "    Conventions:          COARDS\\nGrADS\n",
+       "    dataType:             Grid\n",
+       "    history:              Mon Sep 28 12:21:13 EDT 2026 : imported by GrADS Da...\n",
+       "    extra_das_attribute:  This is an example of metadata added using a supple...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e2e05cec-98e7-4158-a602-b4e3a4428f00' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e2e05cec-98e7-4158-a602-b4e3a4428f00' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 96</li><li><span class='xr-has-index'>lat</span>: 305</li><li><span class='xr-has-index'>lon</span>: 297</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-716b171c-8f9c-41dd-bf8d-f1b6dbd22c7f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-716b171c-8f9c-41dd-bf8d-f1b6dbd22c7f' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2006-08-25T00:15:00 ... 2006-08-...</div><input id='attrs-f03a39aa-b6cd-4699-bfb9-7152bdcd8cf3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f03a39aa-b6cd-4699-bfb9-7152bdcd8cf3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e27e8246-675b-4d08-868c-d5b477929151' class='xr-var-data-in' type='checkbox'><label for='data-e27e8246-675b-4d08-868c-d5b477929151' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grads_dim :</span></dt><dd>t</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>36576</dd><dt><span>grads_min :</span></dt><dd>21:15z15may2005</dd><dt><span>grads_step :</span></dt><dd>30mn</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>minimum :</span></dt><dd>21:15z15may2005</dd><dt><span>maximum :</span></dt><dd>20:45z16jun2007</dd><dt><span>resolution :</span></dt><dd>0.020833334</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2006-08-25T00:15:00.000000000&#x27;, &#x27;2006-08-25T00:45:00.000000000&#x27;,\n",
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+       "       &#x27;2006-08-25T06:15:00.000000000&#x27;, &#x27;2006-08-25T06:45:00.000000000&#x27;,\n",
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+       "       &#x27;2006-08-25T20:15:00.000000000&#x27;, &#x27;2006-08-25T20:45:00.000000000&#x27;,\n",
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+       "       &#x27;2006-08-25T22:15:00.000000000&#x27;, &#x27;2006-08-25T22:45:00.000000000&#x27;,\n",
+       "       &#x27;2006-08-25T23:15:00.000000000&#x27;, &#x27;2006-08-25T23:45:00.000000000&#x27;,\n",
+       "       &#x27;2006-08-26T00:15:00.000000000&#x27;, &#x27;2006-08-26T00:45:00.000000000&#x27;,\n",
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+       "       &#x27;2006-08-26T02:15:00.000000000&#x27;, &#x27;2006-08-26T02:45:00.000000000&#x27;,\n",
+       "       &#x27;2006-08-26T03:15:00.000000000&#x27;, &#x27;2006-08-26T03:45:00.000000000&#x27;,\n",
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+       "       &#x27;2006-08-26T05:15:00.000000000&#x27;, &#x27;2006-08-26T05:45:00.000000000&#x27;,\n",
+       "       &#x27;2006-08-26T06:15:00.000000000&#x27;, &#x27;2006-08-26T06:45:00.000000000&#x27;,\n",
+       "       &#x27;2006-08-26T07:15:00.000000000&#x27;, &#x27;2006-08-26T07:45:00.000000000&#x27;,\n",
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+       "       &#x27;2006-08-26T09:15:00.000000000&#x27;, &#x27;2006-08-26T09:45:00.000000000&#x27;,\n",
+       "       &#x27;2006-08-26T10:15:00.000000000&#x27;, &#x27;2006-08-26T10:45:00.000000000&#x27;,\n",
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+       "       &#x27;2006-08-26T14:15:00.000000000&#x27;, &#x27;2006-08-26T14:45:00.000000000&#x27;,\n",
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+       "       &#x27;2006-08-26T16:15:00.000000000&#x27;, &#x27;2006-08-26T16:45:00.000000000&#x27;,\n",
+       "       &#x27;2006-08-26T17:15:00.000000000&#x27;, &#x27;2006-08-26T17:45:00.000000000&#x27;,\n",
+       "       &#x27;2006-08-26T18:15:00.000000000&#x27;, &#x27;2006-08-26T18:45:00.000000000&#x27;,\n",
+       "       &#x27;2006-08-26T19:15:00.000000000&#x27;, &#x27;2006-08-26T19:45:00.000000000&#x27;,\n",
+       "       &#x27;2006-08-26T20:15:00.000000000&#x27;, &#x27;2006-08-26T20:45:00.000000000&#x27;,\n",
+       "       &#x27;2006-08-26T21:15:00.000000000&#x27;, &#x27;2006-08-26T21:45:00.000000000&#x27;,\n",
+       "       &#x27;2006-08-26T22:15:00.000000000&#x27;, &#x27;2006-08-26T22:45:00.000000000&#x27;,\n",
+       "       &#x27;2006-08-26T23:15:00.000000000&#x27;, &#x27;2006-08-26T23:45:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.0 15.06 15.12 ... 33.94 34.0</div><input id='attrs-08a688e0-9e3e-4088-98b9-25020544cb05' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08a688e0-9e3e-4088-98b9-25020544cb05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2317c52-f192-488b-b9e5-3e4c092ef94b' class='xr-var-data-in' type='checkbox'><label for='data-c2317c52-f192-488b-b9e5-3e4c092ef94b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grads_dim :</span></dt><dd>y</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>2881</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>minimum :</span></dt><dd>-90.0</dd><dt><span>maximum :</span></dt><dd>90.0</dd><dt><span>resolution :</span></dt><dd>0.0625</dd></dl></div><div class='xr-var-data'><pre>array([15.    , 15.0625, 15.125 , ..., 33.875 , 33.9375, 34.    ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-77.0 -76.94 ... -58.56 -58.5</div><input id='attrs-80bdace8-eabe-49f7-877f-2f911463d1ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80bdace8-eabe-49f7-877f-2f911463d1ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5f9a822-7249-4cfa-a86a-9a406405ffd5' class='xr-var-data-in' type='checkbox'><label for='data-c5f9a822-7249-4cfa-a86a-9a406405ffd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grads_dim :</span></dt><dd>x</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>5760</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>minimum :</span></dt><dd>-180.0</dd><dt><span>maximum :</span></dt><dd>179.9375</dd><dt><span>resolution :</span></dt><dd>0.0625</dd></dl></div><div class='xr-var-data'><pre>array([-77.    , -76.9375, -76.875 , ..., -58.625 , -58.5625, -58.5   ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ce569cbb-371d-430a-950c-67117f32883a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ce569cbb-371d-430a-950c-67117f32883a' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lwtup</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>172.8 172.8 173.4 ... 291.4 290.9</div><input id='attrs-98b2fbc5-e7c7-4364-8828-4ae79d505de5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98b2fbc5-e7c7-4364-8828-4ae79d505de5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df1190a6-8a37-43d0-ad29-e1653165a45d' class='xr-var-data-in' type='checkbox'><label for='data-df1190a6-8a37-43d0-ad29-e1653165a45d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>long_name :</span></dt><dd>upwelling_longwave_flux_at_toa </dd></dl></div><div class='xr-var-data'><pre>array([[[172.7667 , 172.7667 , 173.3917 , ..., 285.3917 , 287.6417 ,\n",
+       "         292.6417 ],\n",
+       "        [172.6417 , 174.0167 , 179.2667 , ..., 286.1417 , 288.8917 ,\n",
+       "         293.1417 ],\n",
+       "        [175.5167 , 179.3917 , 191.0167 , ..., 288.3917 , 292.3917 ,\n",
+       "         293.1417 ],\n",
+       "        ...,\n",
+       "        [290.8917 , 291.3917 , 291.6417 , ..., 286.6417 , 291.3917 ,\n",
+       "         291.3917 ],\n",
+       "        [290.3917 , 290.6417 , 291.1417 , ..., 290.6417 , 291.3917 ,\n",
+       "         292.6417 ],\n",
+       "        [289.8917 , 290.6417 , 290.6417 , ..., 291.1417 , 291.6417 ,\n",
+       "         292.8917 ]],\n",
+       "\n",
+       "       [[252.33968, 252.33968, 253.08968, ..., 291.83966, 291.33966,\n",
+       "         289.33966],\n",
+       "        [252.08968, 251.08968, 250.58968, ..., 289.83966, 291.33966,\n",
+       "         290.83966],\n",
+       "        [252.58968, 251.83968, 252.08968, ..., 291.83966, 293.08966,\n",
+       "         293.08966],\n",
+       "...\n",
+       "        [187.42218, 193.92218, 192.79718, ..., 291.42218, 291.92218,\n",
+       "         291.92218],\n",
+       "        [186.92218, 197.29718, 209.42218, ..., 290.92218, 291.42218,\n",
+       "         291.67218],\n",
+       "        [174.42218, 184.42218, 198.67218, ..., 290.67218, 291.17218,\n",
+       "         291.42218]],\n",
+       "\n",
+       "       [[202.41269, 203.66269, 202.41269, ..., 141.16269, 142.28769,\n",
+       "         141.78769],\n",
+       "        [215.91269, 228.66269, 223.91269, ..., 138.91269, 139.91269,\n",
+       "         139.53769],\n",
+       "        [269.4127 , 268.6627 , 267.4127 , ..., 138.03769, 138.78769,\n",
+       "         138.41269],\n",
+       "        ...,\n",
+       "        [202.66269, 185.28769, 176.28769, ..., 291.6627 , 291.4127 ,\n",
+       "         290.6627 ],\n",
+       "        [214.41269, 190.91269, 178.28769, ..., 291.6627 , 291.4127 ,\n",
+       "         290.9127 ],\n",
+       "        [218.16269, 200.41269, 187.66269, ..., 291.4127 , 291.4127 ,\n",
+       "         290.9127 ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prectot</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.875e-05 1.425e-05 ... 1.246e-06</div><input id='attrs-f808cf25-25c6-403b-b0d2-d712726fb0b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f808cf25-25c6-403b-b0d2-d712726fb0b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1c6c2c5-bbef-49bf-9e25-edcb7f5a0938' class='xr-var-data-in' type='checkbox'><label for='data-b1c6c2c5-bbef-49bf-9e25-edcb7f5a0938' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>long_name :</span></dt><dd>total_precipitation </dd></dl></div><div class='xr-var-data'><pre>array([[[1.87456608e-05, 1.42455101e-05, 1.21146441e-05, ...,\n",
+       "         1.11907721e-05, 6.42985106e-06, 4.88758087e-06],\n",
+       "        [8.22544098e-06, 7.48783350e-06, 1.01923943e-05, ...,\n",
+       "         6.16908073e-06, 4.25428152e-06, 4.21702862e-06],\n",
+       "        [7.91251659e-06, 5.62518835e-06, 7.39097595e-06, ...,\n",
+       "         2.92062759e-06, 4.13507223e-06, 7.92741776e-06],\n",
+       "        ...,\n",
+       "        [3.49245965e-07, 1.34296715e-06, 2.15694308e-06, ...,\n",
+       "         1.16042793e-06, 1.19023025e-06, 8.58679414e-07],\n",
+       "        [1.73225999e-07, 3.16184014e-07, 1.00769103e-06, ...,\n",
+       "         5.11296093e-07, 4.78699803e-07, 6.49131835e-07],\n",
+       "        [1.30617991e-07, 1.26194209e-07, 2.63098627e-07, ...,\n",
+       "         1.12690032e-07, 1.43656507e-07, 1.98138878e-07]],\n",
+       "\n",
+       "       [[1.38729811e-05, 1.51097775e-05, 1.34557486e-05, ...,\n",
+       "         2.64868140e-06, 1.14552677e-06, 9.88366082e-08],\n",
+       "        [4.95463610e-06, 1.48564577e-05, 8.79168510e-06, ...,\n",
+       "         2.53692269e-06, 9.62987542e-07, 9.12696123e-08],\n",
+       "        [5.45382500e-06, 5.56558371e-06, 4.50760126e-06, ...,\n",
+       "         2.79024243e-06, 2.43261456e-06, 1.65402889e-06],\n",
+       "...\n",
+       "        [1.95503235e-05, 6.16312027e-05, 4.41670418e-05, ...,\n",
+       "         7.19912350e-07, 8.95000994e-07, 1.27032399e-06],\n",
+       "        [4.91738319e-05, 2.35736370e-05, 2.65836716e-05, ...,\n",
+       "         5.18746674e-07, 9.98377800e-07, 5.87664545e-07],\n",
+       "        [3.67164612e-05, 3.14712524e-05, 1.52140856e-05, ...,\n",
+       "         1.08405948e-06, 1.29453838e-06, 4.99188900e-07]],\n",
+       "\n",
+       "       [[4.08291817e-06, 5.74439764e-06, 4.20212746e-06, ...,\n",
+       "         2.79843807e-05, 2.85506248e-05, 1.63316727e-05],\n",
+       "        [8.58306885e-06, 4.91738319e-06, 3.54647636e-06, ...,\n",
+       "         3.50475311e-05, 3.23057175e-05, 3.40342522e-05],\n",
+       "        [3.91900539e-06, 6.31809235e-06, 4.14997339e-06, ...,\n",
+       "         2.36928463e-05, 2.67922878e-05, 1.86562538e-05],\n",
+       "        ...,\n",
+       "        [1.49607658e-05, 2.16960907e-05, 1.54972076e-05, ...,\n",
+       "         3.14041972e-06, 1.22375786e-06, 1.09151006e-06],\n",
+       "        [7.04526901e-05, 1.14738941e-05, 1.06692314e-05, ...,\n",
+       "         1.74157321e-06, 8.07456672e-07, 8.53091478e-07],\n",
+       "        [2.33411789e-04, 1.40964985e-05, 3.92645597e-06, ...,\n",
+       "         2.35065818e-06, 9.62987542e-07, 1.24610960e-06]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>preccon</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.785e-05 1.389e-05 ... 1.241e-06</div><input id='attrs-0998c55e-2b4e-4e62-823e-9cd5d5696360' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0998c55e-2b4e-4e62-823e-9cd5d5696360' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85ce8af0-2129-42e9-808e-725dcae517d6' class='xr-var-data-in' type='checkbox'><label for='data-85ce8af0-2129-42e9-808e-725dcae517d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>long_name :</span></dt><dd>convective_precipitation </dd></dl></div><div class='xr-var-data'><pre>array([[[1.78515911e-05, 1.38878822e-05, 1.17570162e-05, ...,\n",
+       "         9.16421413e-07, 1.94087625e-06, 1.59442425e-06],\n",
+       "        [8.13603401e-06, 7.40587711e-06, 1.01029873e-05, ...,\n",
+       "         9.16421413e-07, 1.81607902e-06, 1.02072954e-06],\n",
+       "        [7.86781311e-06, 5.57303429e-06, 7.33882189e-06, ...,\n",
+       "         7.18049705e-07, 1.89617276e-06, 2.95042992e-06],\n",
+       "        ...,\n",
+       "        [8.55652615e-08, 3.31550837e-07, 4.89875674e-07, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [6.93835318e-08, 8.98726285e-08, 3.08733433e-07, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [3.16067599e-08, 5.66360541e-08, 7.19446689e-08, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],\n",
+       "\n",
+       "       [[1.37835741e-05, 1.50054693e-05, 1.33067369e-05, ...,\n",
+       "         8.63336027e-07, 5.47617674e-07, 3.50992195e-08],\n",
+       "        [4.91738319e-06, 1.48266554e-05, 8.74698162e-06, ...,\n",
+       "         1.04680657e-06, 3.49245965e-07, 2.43308023e-08],\n",
+       "        [5.43147326e-06, 5.54323196e-06, 4.49270010e-06, ...,\n",
+       "         1.53481960e-06, 1.59628689e-06, 1.37463212e-06],\n",
+       "...\n",
+       "        [1.52885914e-05, 5.14388084e-05, 3.63588333e-05, ...,\n",
+       "         7.11530447e-07, 8.87550414e-07, 1.26287341e-06],\n",
+       "        [3.98755074e-05, 1.75833702e-05, 2.26497650e-05, ...,\n",
+       "         5.10364771e-07, 9.92789865e-07, 5.81145287e-07],\n",
+       "        [3.31401825e-05, 2.65538692e-05, 1.20252371e-05, ...,\n",
+       "         1.07847154e-06, 1.28895044e-06, 4.93600965e-07]],\n",
+       "\n",
+       "       [[1.62795186e-06, 3.80352139e-06, 3.31550837e-06, ...,\n",
+       "         2.45571136e-05, 2.67028809e-05, 1.56164169e-05],\n",
+       "        [2.07871199e-06, 1.57579780e-06, 1.40443444e-06, ...,\n",
+       "         3.17096710e-05, 2.92062759e-05, 3.20076942e-05],\n",
+       "        [3.17022204e-06, 1.56834722e-06, 1.28336251e-06, ...,\n",
+       "         2.22623348e-05, 2.50637531e-05, 1.73151493e-05],\n",
+       "        ...,\n",
+       "        [1.20997429e-05, 2.04741955e-05, 1.44243240e-05, ...,\n",
+       "         7.82310963e-07, 7.49714673e-07, 1.01886690e-06],\n",
+       "        [2.91168690e-05, 1.01625919e-05, 9.95397568e-06, ...,\n",
+       "         1.18091702e-06, 7.86036253e-07, 8.26083124e-07],\n",
+       "        [2.24411488e-05, 1.18911266e-05, 3.22982669e-06, ...,\n",
+       "         2.33203173e-06, 9.48086381e-07, 1.24052167e-06]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cfe85819-6b47-4978-9961-c77f2314cc80' class='xr-section-summary-in' type='checkbox'  ><label for='section-cfe85819-6b47-4978-9961-c77f2314cc80' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-bec8c9fa-8e00-4852-bc08-53d30f7487b7' class='xr-index-data-in' type='checkbox'/><label for='index-bec8c9fa-8e00-4852-bc08-53d30f7487b7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2006-08-25 00:15:00&#x27;, &#x27;2006-08-25 00:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 01:15:00&#x27;, &#x27;2006-08-25 01:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 02:15:00&#x27;, &#x27;2006-08-25 02:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 03:15:00&#x27;, &#x27;2006-08-25 03:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 04:15:00&#x27;, &#x27;2006-08-25 04:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 05:15:00&#x27;, &#x27;2006-08-25 05:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 06:15:00&#x27;, &#x27;2006-08-25 06:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 07:15:00&#x27;, &#x27;2006-08-25 07:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 08:15:00&#x27;, &#x27;2006-08-25 08:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 09:15:00&#x27;, &#x27;2006-08-25 09:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 10:15:00&#x27;, &#x27;2006-08-25 10:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 11:15:00&#x27;, &#x27;2006-08-25 11:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 12:15:00&#x27;, &#x27;2006-08-25 12:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 13:15:00&#x27;, &#x27;2006-08-25 13:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 14:15:00&#x27;, &#x27;2006-08-25 14:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 15:15:00&#x27;, &#x27;2006-08-25 15:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 16:15:00&#x27;, &#x27;2006-08-25 16:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 17:15:00&#x27;, &#x27;2006-08-25 17:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 18:15:00&#x27;, &#x27;2006-08-25 18:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 19:15:00&#x27;, &#x27;2006-08-25 19:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 20:15:00&#x27;, &#x27;2006-08-25 20:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 21:15:00&#x27;, &#x27;2006-08-25 21:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 22:15:00&#x27;, &#x27;2006-08-25 22:45:00&#x27;,\n",
+       "               &#x27;2006-08-25 23:15:00&#x27;, &#x27;2006-08-25 23:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 00:15:00&#x27;, &#x27;2006-08-26 00:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 01:15:00&#x27;, &#x27;2006-08-26 01:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 02:15:00&#x27;, &#x27;2006-08-26 02:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 03:15:00&#x27;, &#x27;2006-08-26 03:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 04:15:00&#x27;, &#x27;2006-08-26 04:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 05:15:00&#x27;, &#x27;2006-08-26 05:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 06:15:00&#x27;, &#x27;2006-08-26 06:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 07:15:00&#x27;, &#x27;2006-08-26 07:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 08:15:00&#x27;, &#x27;2006-08-26 08:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 09:15:00&#x27;, &#x27;2006-08-26 09:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 10:15:00&#x27;, &#x27;2006-08-26 10:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 11:15:00&#x27;, &#x27;2006-08-26 11:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 12:15:00&#x27;, &#x27;2006-08-26 12:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 13:15:00&#x27;, &#x27;2006-08-26 13:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 14:15:00&#x27;, &#x27;2006-08-26 14:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 15:15:00&#x27;, &#x27;2006-08-26 15:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 16:15:00&#x27;, &#x27;2006-08-26 16:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 17:15:00&#x27;, &#x27;2006-08-26 17:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 18:15:00&#x27;, &#x27;2006-08-26 18:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 19:15:00&#x27;, &#x27;2006-08-26 19:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 20:15:00&#x27;, &#x27;2006-08-26 20:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 21:15:00&#x27;, &#x27;2006-08-26 21:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 22:15:00&#x27;, &#x27;2006-08-26 22:45:00&#x27;,\n",
+       "               &#x27;2006-08-26 23:15:00&#x27;, &#x27;2006-08-26 23:45:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-d02003fe-fb1e-4501-99b3-b5f6efb0efe4' class='xr-index-data-in' type='checkbox'/><label for='index-d02003fe-fb1e-4501-99b3-b5f6efb0efe4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   15.0, 15.0625,  15.125, 15.1875,   15.25, 15.3125,  15.375, 15.4375,\n",
+       "          15.5, 15.5625,\n",
+       "       ...\n",
+       "       33.4375,    33.5, 33.5625,  33.625, 33.6875,   33.75, 33.8125,  33.875,\n",
+       "       33.9375,    34.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=305))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-60cafb4a-1b47-4f8f-8e24-ae9df719c5e3' class='xr-index-data-in' type='checkbox'/><label for='index-60cafb4a-1b47-4f8f-8e24-ae9df719c5e3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   -77.0, -76.9375,  -76.875, -76.8125,   -76.75, -76.6875,  -76.625,\n",
+       "       -76.5625,    -76.5, -76.4375,\n",
+       "       ...\n",
+       "       -59.0625,    -59.0, -58.9375,  -58.875, -58.8125,   -58.75, -58.6875,\n",
+       "        -58.625, -58.5625,    -58.5],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=297))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-77ea7d7b-2fea-4f7f-9321-34c3732b21ff' class='xr-section-summary-in' type='checkbox'  checked><label for='section-77ea7d7b-2fea-4f7f-9321-34c3732b21ff' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>2d,30-Minute,Time-Averaged,Single-Level,Full Resolution,Single-Level Physics Diagnostics</dd><dt><span>Conventions :</span></dt><dd>COARDS\n",
+       "GrADS</dd><dt><span>dataType :</span></dt><dd>Grid</dd><dt><span>history :</span></dt><dd>Mon Sep 28 12:21:13 EDT 2026 : imported by GrADS Data Server 2.0</dd><dt><span>extra_das_attribute :</span></dt><dd>This is an example of metadata added using a supplementary DAS file. See testdata/extra.das for the source.</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 104MB\n",
+       "Dimensions:  (time: 96, lat: 305, lon: 297)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 768B 2006-08-25T00:15:00 ... 2006-08-26T23...\n",
+       "  * lat      (lat) float64 2kB 15.0 15.06 15.12 15.19 ... 33.81 33.88 33.94 34.0\n",
+       "  * lon      (lon) float64 2kB -77.0 -76.94 -76.88 ... -58.62 -58.56 -58.5\n",
+       "Data variables:\n",
+       "    lwtup    (time, lat, lon) float32 35MB 172.8 172.8 173.4 ... 291.4 290.9\n",
+       "    prectot  (time, lat, lon) float32 35MB 1.875e-05 1.425e-05 ... 1.246e-06\n",
+       "    preccon  (time, lat, lon) float32 35MB 1.785e-05 1.389e-05 ... 1.241e-06\n",
+       "Attributes:\n",
+       "    title:                2d,30-Minute,Time-Averaged,Single-Level,Full Resolu...\n",
+       "    Conventions:          COARDS\\nGrADS\n",
+       "    dataType:             Grid\n",
+       "    history:              Mon Sep 28 12:21:13 EDT 2026 : imported by GrADS Da...\n",
+       "    extra_das_attribute:  This is an example of metadata added using a supple..."
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "NATURE_URL = f\"{G5NR_URL}/0.0625_deg/tavg/tavg30mn_2d_met3_Nx\"\n",
+    "GRID_SIZE = 0.0625  # deg\n",
+    "VARIABLES = [\"lwtup\", \"prectot\", \"preccon\"]\n",
+    "CHUNK = timedelta(hours=12)\n",
+    "\n",
+    "\n",
+    "def fetch_chunk(remote, chunk_start):\n",
+    "    \"\"\"One chunk of the Nature Run over the study region, retried if the server returns a field of zeros.\"\"\"\n",
+    "    minx, miny, maxx, maxy = domain.bounds\n",
+    "    for attempt in range(4):\n",
+    "        chunk = remote[VARIABLES].sel(\n",
+    "            time=slice(naive(chunk_start), naive(chunk_start + CHUNK - timedelta(seconds=1))),\n",
+    "            lon=slice(minx, maxx),\n",
+    "            lat=slice(miny, maxy),\n",
+    "        ).load()\n",
+    "        if float((chunk[\"lwtup\"] == 0).mean()) < 0.5:\n",
+    "            return chunk\n",
+    "    raise RuntimeError(f\"the server returned a field of zeros for {chunk_start}\")\n",
+    "\n",
+    "\n",
+    "nature_file = DATA_DIR / f\"g5nr_hurricane_{nature_start:%Y%m%d}_{duration.days}d.nc\"\n",
+    "if not nature_file.exists():\n",
+    "    with xr.open_dataset(NATURE_URL) as remote:\n",
+    "        chunks = [fetch_chunk(remote, nature_start + k * CHUNK) for k in range(int(duration / CHUNK))]\n",
+    "    xr.concat(chunks, \"time\").to_netcdf(nature_file)\n",
+    "nature = xr.load_dataset(nature_file)\n",
+    "assert nature.sizes[\"time\"] == n_frames\n",
+    "assert float((nature[\"lwtup\"] == 0).mean()) == 0\n",
+    "# each time stamp is the middle of its 30-minute averaging interval\n",
+    "assert (nature.time.values[0] - np.datetime64(naive(nature_start))) == np.timedelta64(15, \"m\")\n",
+    "nature"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85a1327e",
+   "metadata": {},
+   "source": [
+    "## Define the Environmental Features\n",
+    "\n",
+    "Features are contiguous patches of grid cells of one of two kinds:\n",
+    "\n",
+    "* **cold cloud top**: an upwelling longwave flux below that of a 220 K blackbody (about 133 W m-2), as in the monsoon OSSE; and\n",
+    "* **heavy precipitation**: a total precipitation rate above `PRECIP_THRESHOLD` (10 mm/h).\n",
+    "\n",
+    "Each patch in each frame is a feature, and, as in the monsoon OSSE, patches smaller than the Nature Run's effective resolution (`EFFECTIVE_RESOLUTION_KM`, four grid spacings or 28 km) are discarded. A large patch of cold cloud tops may contain several patches of heavy precipitation, so the two kinds overlap.\n",
+    "\n",
+    "For each feature, the notebook records its area, the diameter of a circle of the same area (its size), the area-weighted centroid, its coldest flux, its highest and total precipitation rates, and its distance from the storm center at the middle of its frame (interpolated from the hourly track). Features within `STORM_RADIUS_KM` (500 km) of the center are attributed to the storm and the others to its environment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "37e47761",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:47:35.649871Z",
+     "iopub.status.busy": "2026-10-24T21:47:35.649871Z",
+     "iopub.status.idle": "2026-10-24T21:47:47.712725Z",
+     "shell.execute_reply": "2026-10-24T21:47:47.712725Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>features</th>\n",
+       "      <th>median_diameter_km</th>\n",
+       "      <th>max_diameter_km</th>\n",
+       "      <th>max_prectot_mmh</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kind</th>\n",
+       "      <th>region</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">cold cloud top</th>\n",
+       "      <th>environment</th>\n",
+       "      <td>190</td>\n",
+       "      <td>51.7</td>\n",
+       "      <td>115.3</td>\n",
+       "      <td>182.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>storm</th>\n",
+       "      <td>355</td>\n",
+       "      <td>56.4</td>\n",
+       "      <td>483.0</td>\n",
+       "      <td>102.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">heavy precipitation</th>\n",
+       "      <th>environment</th>\n",
+       "      <td>51</td>\n",
+       "      <td>31.7</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>209.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>storm</th>\n",
+       "      <td>517</td>\n",
+       "      <td>49.2</td>\n",
+       "      <td>215.1</td>\n",
+       "      <td>134.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 features  median_diameter_km  \\\n",
+       "kind                region                                      \n",
+       "cold cloud top      environment       190                51.7   \n",
+       "                    storm             355                56.4   \n",
+       "heavy precipitation environment        51                31.7   \n",
+       "                    storm             517                49.2   \n",
+       "\n",
+       "                                 max_diameter_km  max_prectot_mmh  \n",
+       "kind                region                                         \n",
+       "cold cloud top      environment            115.3            182.2  \n",
+       "                    storm                  483.0            102.0  \n",
+       "heavy precipitation environment             46.0            209.8  \n",
+       "                    storm                  215.1            134.3  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import scipy.ndimage as ndi\n",
+    "\n",
+    "from tatc.constants import EARTH_MEAN_RADIUS\n",
+    "\n",
+    "THRESHOLD = 5.67037e-8 * 220**4  # W m-2, the upwelling longwave flux of a 220 K blackbody\n",
+    "PRECIP_THRESHOLD = 10 / 3600  # kg m-2 s-1 (10 mm/h)\n",
+    "EFFECTIVE_RESOLUTION_KM = 4 * 7  # km, several grid spacings (Gelaro et al., 2015); smaller features are not resolved\n",
+    "STORM_RADIUS_KM = 500  # km, features within this distance of the storm center are attributed to the storm\n",
+    "KINDS = {\n",
+    "    \"cold cloud top\": lambda frame: frame[\"lwtup\"].values < THRESHOLD,\n",
+    "    \"heavy precipitation\": lambda frame: frame[\"prectot\"].values > PRECIP_THRESHOLD,\n",
+    "}\n",
+    "\n",
+    "\n",
+    "def find_features(nature, kind):\n",
+    "    \"\"\"The features of a kind (contiguous patches at least EFFECTIVE_RESOLUTION_KM in size) in each frame.\"\"\"\n",
+    "    lon, lat = nature.lon.values, nature.lat.values\n",
+    "    lon2d, lat2d = np.meshgrid(lon, lat)\n",
+    "    cell_area = (EARTH_MEAN_RADIUS * np.radians(GRID_SIZE)) ** 2 * np.cos(np.radians(lat2d))  # m^2\n",
+    "\n",
+    "    records, polygons = [], []\n",
+    "    for frame in range(nature.sizes[\"time\"]):\n",
+    "        fields = nature.isel(time=frame)\n",
+    "        lwtup, prectot = fields[\"lwtup\"].values, fields[\"prectot\"].values\n",
+    "        labels, n_labels = ndi.label(KINDS[kind](fields))  # contiguous cells of the kind\n",
+    "        if n_labels == 0:\n",
+    "            continue\n",
+    "        ids = np.arange(1, n_labels + 1)\n",
+    "        area = ndi.sum(cell_area, labels, ids)\n",
+    "        n_cells = ndi.sum(np.ones_like(lwtup), labels, ids).astype(int)\n",
+    "        # polygons are the unions of the cells, which are centered on the grid points\n",
+    "        j, i = np.nonzero(labels)\n",
+    "        order = np.argsort(labels[j, i], kind=\"stable\")\n",
+    "        j, i = j[order], i[order]\n",
+    "        cells = shapely.box(\n",
+    "            lon[i] - GRID_SIZE / 2, lat[j] - GRID_SIZE / 2, lon[i] + GRID_SIZE / 2, lat[j] + GRID_SIZE / 2\n",
+    "        )\n",
+    "        for k, cluster in enumerate(np.split(cells, np.cumsum(n_cells)[:-1])):\n",
+    "            records.append(\n",
+    "                {\n",
+    "                    \"frame\": frame,\n",
+    "                    \"kind\": kind,\n",
+    "                    \"n_cells\": n_cells[k],\n",
+    "                    \"area_km2\": area[k] / 1e6,\n",
+    "                    \"lon\": ndi.sum(cell_area * lon2d, labels, ids[k]) / area[k],\n",
+    "                    \"lat\": ndi.sum(cell_area * lat2d, labels, ids[k]) / area[k],\n",
+    "                    \"min_lwtup\": ndi.minimum(lwtup, labels, ids[k]),\n",
+    "                    \"max_prectot_mmh\": ndi.maximum(prectot, labels, ids[k]) * 3600,\n",
+    "                    \"tot_prectot\": ndi.sum(prectot * cell_area, labels, ids[k]),\n",
+    "                }\n",
+    "            )\n",
+    "            polygons.append(shapely.union_all(cluster))\n",
+    "\n",
+    "    features = gpd.GeoDataFrame(records, geometry=polygons, crs=\"EPSG:4326\")\n",
+    "    features[\"diameter_km\"] = np.sqrt(4 * features.area_km2 / np.pi)  # of a circle of equal area\n",
+    "    return features[features.diameter_km >= EFFECTIVE_RESOLUTION_KM]\n",
+    "\n",
+    "\n",
+    "features = pd.concat([find_features(nature, kind) for kind in KINDS], ignore_index=True)\n",
+    "features[\"time\"] = start + features.frame * frame_duration\n",
+    "\n",
+    "# distance (great circle) from the storm center at the middle of the frame, interpolated from the hourly track\n",
+    "frame_middle = (nature.time.values[features.frame] - track.index.values[0]) / np.timedelta64(1, \"h\")\n",
+    "track_hours = (track.index.values - track.index.values[0]) / np.timedelta64(1, \"h\")\n",
+    "center_lat = np.radians(np.interp(frame_middle, track_hours, track.lat))\n",
+    "center_lon = np.radians(np.interp(frame_middle, track_hours, track.lon))\n",
+    "lat, lon = np.radians(features.lat), np.radians(features.lon)\n",
+    "features[\"storm_distance_km\"] = (\n",
+    "    EARTH_MEAN_RADIUS / 1e3\n",
+    "    * np.arccos(np.clip(np.sin(lat) * np.sin(center_lat) + np.cos(lat) * np.cos(center_lat) * np.cos(lon - center_lon), -1, 1))\n",
+    ")\n",
+    "features[\"region\"] = np.where(features.storm_distance_km < STORM_RADIUS_KM, \"storm\", \"environment\")\n",
+    "features.groupby([\"kind\", \"region\"]).agg(\n",
+    "    features=(\"frame\", \"size\"),\n",
+    "    median_diameter_km=(\"diameter_km\", \"median\"),\n",
+    "    max_diameter_km=(\"diameter_km\", \"max\"),\n",
+    "    max_prectot_mmh=(\"max_prectot_mmh\", \"max\"),\n",
+    ").round(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "703f98c4",
+   "metadata": {},
+   "source": [
+    "## Define the Sensors\n",
+    "\n",
+    "The platforms, swaths, and sensors are those of the monsoon OSSE (see there for their basis): ATMS and CrIS on Suomi NPP, NOAA-20, and NOAA-21, with the scan parameters fitted to NOAA-20 data in the validation notebooks, and the notional future microwave (MW) and infrared (IR) sounders with finer resolution, sharing the ATMS and CrIS swaths. Here the element sets are those of 25 August 2026."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "069ee49e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:47:47.714730Z",
+     "iopub.status.busy": "2026-10-24T21:47:47.714730Z",
+     "iopub.status.idle": "2026-10-24T21:47:47.742987Z",
+     "shell.execute_reply": "2026-10-24T21:47:47.742987Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>swath</th>\n",
+       "      <th>horizontal_km</th>\n",
+       "      <th>sampling_km</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sensor</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ATMS</th>\n",
+       "      <td>ATMS</td>\n",
+       "      <td>32</td>\n",
+       "      <td>16.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CrIS</th>\n",
+       "      <td>CrIS</td>\n",
+       "      <td>14</td>\n",
+       "      <td>15.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future MW</th>\n",
+       "      <td>ATMS</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future IR</th>\n",
+       "      <td>CrIS</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          swath  horizontal_km  sampling_km\n",
+       "sensor                                     \n",
+       "ATMS       ATMS             32         16.1\n",
+       "CrIS       CrIS             14         15.8\n",
+       "Future MW  ATMS             10          5.0\n",
+       "Future IR  CrIS              3          3.4"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from tatc.schemas import GeneralPerturbationsOrbit, PointedInstrument, Satellite\n",
+    "from tatc.utils import VelocityFrame, compute_along_track_field_of_view\n",
+    "\n",
+    "# element sets (epoch 2026-08-25 00:00 UTC) from the JPSS TLE-AUX archive, as in the validation notebooks\n",
+    "tles = {\n",
+    "    \"Suomi NPP\": [\n",
+    "        \"1 37849U 11061A   26237.00000000  .00000048  00000-0  22799-4 0  2729\",\n",
+    "        \"2 37849 098.7980 177.4149 0002756 116.4369 275.9805 14.19529488768207\",\n",
+    "    ],\n",
+    "    \"NOAA-20\": [\n",
+    "        \"1 43013U 17073A   26237.00000000  .00000082  00000-0  38818-4 0  2185\",\n",
+    "        \"2 43013 098.7794 175.8588 0001924 083.1912 240.0420 14.19522547454239\",\n",
+    "    ],\n",
+    "    \"NOAA-21\": [\n",
+    "        \"1 54234U 22150A   26237.00000000  .00000066  00000-0  31364-4 0  9878\",\n",
+    "        \"2 54234 098.7078 174.6381 0001971 035.6178 125.9881 14.19546491196315\",\n",
+    "    ],\n",
+    "}\n",
+    "orbits = {name: GeneralPerturbationsOrbit.from_tle(tle) for name, tle in tles.items()}\n",
+    "\n",
+    "# scan parameters fitted to NOAA-20 data in the ATMS and CrIS validation notebooks: the number of scan positions,\n",
+    "# their spacing (deg), and the roll angle (deg, positive to the left); both scan perpendicular to the inertial velocity\n",
+    "scans = {\n",
+    "    \"ATMS\": {\"positions\": 96, \"spacing\": 1.112, \"roll\": 0.04},\n",
+    "    \"CrIS\": {\"positions\": 30, \"spacing\": 3.334, \"roll\": -0.093},\n",
+    "}\n",
+    "TIME_STEP = 30  # s, of the ground track\n",
+    "\n",
+    "\n",
+    "def swath_instrument(name):\n",
+    "    \"\"\"A rectangular view spanning the scan positions, tailored to the time step (as validated for ATMS and CrIS).\"\"\"\n",
+    "    scan = scans[name]\n",
+    "    field_of_regard = (scan[\"positions\"] - 1) * scan[\"spacing\"]\n",
+    "    return PointedInstrument(\n",
+    "        name=name,\n",
+    "        field_of_regard=field_of_regard,\n",
+    "        cross_track_field_of_view=field_of_regard,\n",
+    "        along_track_field_of_view=compute_along_track_field_of_view(\n",
+    "            orbits[\"NOAA-20\"].get_mean_altitude(), TIME_STEP, orbits[\"NOAA-20\"].get_inclination()\n",
+    "        ),\n",
+    "        roll_angle=scan[\"roll\"],\n",
+    "        is_rectangular=True,\n",
+    "        velocity_frame=VelocityFrame.INERTIAL,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "satellites = [\n",
+    "    Satellite(name=name, orbit=orbit, instruments=[swath_instrument(\"ATMS\"), swath_instrument(\"CrIS\")])\n",
+    "    for name, orbit in orbits.items()\n",
+    "]\n",
+    "\n",
+    "REFERENCE_RANGE_KM = 830  # nominal slant range at nadir (altitude), at which the nominal resolutions apply\n",
+    "# angular spacing (deg) of the samples of each scan, fitted to NOAA-20 data in the validation notebooks: the ATMS scan\n",
+    "# positions and the pitch of the CrIS 3 x 3 array of views\n",
+    "SAMPLING_DEG = {\"ATMS\": scans[\"ATMS\"][\"spacing\"], \"CrIS\": 1.092}\n",
+    "# sensors: the scan (swath) they share, and their horizontal resolution at nadir (km)\n",
+    "sensors = pd.DataFrame(\n",
+    "    [\n",
+    "        {\"sensor\": \"ATMS\", \"swath\": \"ATMS\", \"horizontal_km\": 32},\n",
+    "        {\"sensor\": \"CrIS\", \"swath\": \"CrIS\", \"horizontal_km\": 14},\n",
+    "        {\"sensor\": \"Future MW\", \"swath\": \"ATMS\", \"horizontal_km\": 10},\n",
+    "        {\"sensor\": \"Future IR\", \"swath\": \"CrIS\", \"horizontal_km\": 3},\n",
+    "    ]\n",
+    ").set_index(\"sensor\")\n",
+    "# sample spacing at nadir (km): that of the heritage sensor, scaled for the future sensors in proportion to their footprint\n",
+    "heritage_km = sensors.horizontal_km[sensors.swath].values\n",
+    "sensors[\"sampling_km\"] = REFERENCE_RANGE_KM * np.radians(sensors.swath.map(SAMPLING_DEG)) * sensors.horizontal_km / heritage_km\n",
+    "sensors.round(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45620f26",
+   "metadata": {},
+   "source": [
+    "## Compute the Ground Tracks\n",
+    "\n",
+    "`collect_ground_track` returns the footprint of each instrument at each time step, clipped to the study region (the `mask`), which are tabulated per frame and combined (`swaths`) to find the area each swath covers in each frame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7e009a6b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:47:47.745212Z",
+     "iopub.status.busy": "2026-10-24T21:47:47.745212Z",
+     "iopub.status.idle": "2026-10-24T21:47:48.084161Z",
+     "shell.execute_reply": "2026-10-24T21:47:48.084161Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>footprints</th>\n",
+       "      <th>first</th>\n",
+       "      <th>last</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>satellite</th>\n",
+       "      <th>instrument</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">NOAA-20</th>\n",
+       "      <th>ATMS</th>\n",
+       "      <td>96</td>\n",
+       "      <td>2026-08-25 05:55:45+00:00</td>\n",
+       "      <td>2026-08-26 18:37:15+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CrIS</th>\n",
+       "      <td>92</td>\n",
+       "      <td>2026-08-25 05:55:45+00:00</td>\n",
+       "      <td>2026-08-26 18:37:15+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">NOAA-21</th>\n",
+       "      <th>ATMS</th>\n",
+       "      <td>80</td>\n",
+       "      <td>2026-08-25 05:00:15+00:00</td>\n",
+       "      <td>2026-08-26 17:41:45+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CrIS</th>\n",
+       "      <td>69</td>\n",
+       "      <td>2026-08-25 05:00:15+00:00</td>\n",
+       "      <td>2026-08-26 17:41:45+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Suomi NPP</th>\n",
+       "      <th>ATMS</th>\n",
+       "      <td>99</td>\n",
+       "      <td>2026-08-25 05:36:45+00:00</td>\n",
+       "      <td>2026-08-26 18:18:15+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CrIS</th>\n",
+       "      <td>94</td>\n",
+       "      <td>2026-08-25 05:36:45+00:00</td>\n",
+       "      <td>2026-08-26 18:18:15+00:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      footprints                     first  \\\n",
+       "satellite instrument                                         \n",
+       "NOAA-20   ATMS                96 2026-08-25 05:55:45+00:00   \n",
+       "          CrIS                92 2026-08-25 05:55:45+00:00   \n",
+       "NOAA-21   ATMS                80 2026-08-25 05:00:15+00:00   \n",
+       "          CrIS                69 2026-08-25 05:00:15+00:00   \n",
+       "Suomi NPP ATMS                99 2026-08-25 05:36:45+00:00   \n",
+       "          CrIS                94 2026-08-25 05:36:45+00:00   \n",
+       "\n",
+       "                                          last  \n",
+       "satellite instrument                            \n",
+       "NOAA-20   ATMS       2026-08-26 18:37:15+00:00  \n",
+       "          CrIS       2026-08-26 18:37:15+00:00  \n",
+       "NOAA-21   ATMS       2026-08-26 17:41:45+00:00  \n",
+       "          CrIS       2026-08-26 17:41:45+00:00  \n",
+       "Suomi NPP ATMS       2026-08-26 18:18:15+00:00  \n",
+       "          CrIS       2026-08-26 18:18:15+00:00  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from tatc.analysis import collect_ground_track\n",
+    "\n",
+    "\n",
+    "def collect_tracks(satellites, duration):\n",
+    "    \"\"\"The footprint of every instrument of the satellites at each time step over a duration from start, clipped to the\n",
+    "    study region, with its frame.\"\"\"\n",
+    "    times = [start + timedelta(seconds=TIME_STEP * (i + 0.5)) for i in range(int(duration.total_seconds() // TIME_STEP))]\n",
+    "    tracks = collect_ground_track(satellites, times, instrument_index=None, mask=domain)\n",
+    "    tracks = tracks[tracks.valid_obs].reset_index(drop=True)\n",
+    "    tracks[\"geometry\"] = shapely.force_2d(tracks.geometry.values)\n",
+    "    tracks[\"frame\"] = ((tracks.time - start) // frame_duration).astype(int)\n",
+    "    return tracks\n",
+    "\n",
+    "\n",
+    "tracks = collect_tracks(satellites, duration)\n",
+    "# the area swept by each swath (ATMS or CrIS) in each frame\n",
+    "swaths = tracks.groupby([\"frame\", \"instrument\"]).geometry.agg(shapely.union_all)\n",
+    "tracks.groupby([\"satellite\", \"instrument\"]).agg(footprints=(\"time\", \"size\"), first=(\"time\", \"min\"), last=(\"time\", \"max\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ba74b44",
+   "metadata": {},
+   "source": [
+    "## Compute Observability and Resolution\n",
+    "\n",
+    "As in the monsoon OSSE, a feature is **observed** by a swath if the swath footprint of some time step in its frame overlaps it, a **look** is one satellite's view of the part of a feature inside its swath, and a feature is **resolved** if its viewed part spans at least `RESOLUTION_FACTOR` (two) resolution elements at its best look. The resolution element is the larger of a sensor's footprint and sample spacing at nadir, scaled by the slant range and (across track) the secant of the zenith angle, with its size measured as the diameter of a circle of the same area (`ELEMENT_AXIS`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a502a7dc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:47:48.087167Z",
+     "iopub.status.busy": "2026-10-24T21:47:48.087167Z",
+     "iopub.status.idle": "2026-10-24T21:47:48.460042Z",
+     "shell.execute_reply": "2026-10-24T21:47:48.460042Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">observed</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">resolved</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kind</th>\n",
+       "      <th>cold cloud top</th>\n",
+       "      <th>heavy precipitation</th>\n",
+       "      <th>cold cloud top</th>\n",
+       "      <th>heavy precipitation</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sensor</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ATMS</th>\n",
+       "      <td>84</td>\n",
+       "      <td>55</td>\n",
+       "      <td>20</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CrIS</th>\n",
+       "      <td>71</td>\n",
+       "      <td>45</td>\n",
+       "      <td>46</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future IR</th>\n",
+       "      <td>71</td>\n",
+       "      <td>45</td>\n",
+       "      <td>70</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future MW</th>\n",
+       "      <td>84</td>\n",
+       "      <td>55</td>\n",
+       "      <td>69</td>\n",
+       "      <td>42</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                observed                           resolved  \\\n",
+       "kind      cold cloud top heavy precipitation cold cloud top   \n",
+       "sensor                                                        \n",
+       "ATMS                  84                  55             20   \n",
+       "CrIS                  71                  45             46   \n",
+       "Future IR             71                  45             70   \n",
+       "Future MW             84                  55             69   \n",
+       "\n",
+       "                               \n",
+       "kind      heavy precipitation  \n",
+       "sensor                         \n",
+       "ATMS                       16  \n",
+       "CrIS                       27  \n",
+       "Future IR                  44  \n",
+       "Future MW                  42  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pyproj import Transformer\n",
+    "from skyfield.framelib import itrs\n",
+    "\n",
+    "RESOLUTION_FACTOR = 2  # a feature is resolved if its viewed part spans at least this many resolution elements\n",
+    "ELEMENT_AXIS = \"equivalent\"  # the size of a resolution element: \"equivalent\" (diameter of equal area) or \"cross-track\"\n",
+    "AXES = [\"equivalent\", \"cross-track\"]\n",
+    "\n",
+    "TO_ECEF = Transformer.from_crs(\"EPSG:4979\", \"EPSG:4978\", always_xy=True)\n",
+    "\n",
+    "\n",
+    "def up(lon, lat):\n",
+    "    \"\"\"Geodetic up unit vector, stacked on the last axis.\"\"\"\n",
+    "    lon, lat = np.radians(lon), np.radians(lat)\n",
+    "    return np.stack([np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1)\n",
+    "\n",
+    "\n",
+    "def element_size(slant_range, zenith, nadir_km, axis):\n",
+    "    \"\"\"Size (km) of a resolution element of size nadir_km at nadir: its angular size times the slant range (m) along\n",
+    "    track, and longer by the secant of the zenith angle (deg) cross track. The size is the diameter of a circle of the\n",
+    "    same area (axis=\"equivalent\", the geometric mean of the two) or the cross-track size (axis=\"cross-track\").\"\"\"\n",
+    "    along = slant_range / 1e3 * nadir_km / REFERENCE_RANGE_KM\n",
+    "    cross = along / np.cos(np.radians(zenith))\n",
+    "    return cross if axis == \"cross-track\" else np.sqrt(along * cross)\n",
+    "\n",
+    "\n",
+    "def collect_looks(tracks, features, orbits):\n",
+    "    \"\"\"The looks at the features from the tracks: the part of each feature inside a satellite's swath in its frame, at\n",
+    "    the time of the footprint that covers most of it, with the slant range and zenith angle at its centroid.\"\"\"\n",
+    "    # pieces: the parts of each feature inside each footprint (of a satellite's instrument, at a time) in the same frame\n",
+    "    pieces = gpd.sjoin(\n",
+    "        features[[\"frame\", \"geometry\"]],\n",
+    "        tracks[[\"frame\", \"satellite\", \"instrument\", \"time\", \"geometry\"]].rename(columns={\"frame\": \"frame_track\"}),\n",
+    "        predicate=\"intersects\",\n",
+    "    )\n",
+    "    pieces = pieces[pieces.frame == pieces.frame_track].rename_axis(\"feature\").reset_index()\n",
+    "    pieces[\"geometry\"] = shapely.intersection(pieces.geometry.values, tracks.geometry.values[pieces.index_right])\n",
+    "    pieces[\"piece_area\"] = shapely.area(pieces.geometry.values)\n",
+    "\n",
+    "    # looks: the part of each feature inside a satellite's swath, at the time of the footprint that covers most of it\n",
+    "    looks = (\n",
+    "        pieces.sort_values(\"piece_area\", ascending=False)\n",
+    "        .groupby([\"feature\", \"satellite\", \"instrument\"])\n",
+    "        .agg(time=(\"time\", \"first\"), geometry=(\"geometry\", shapely.union_all))\n",
+    "        .reset_index()\n",
+    "    )\n",
+    "    looks[\"viewed_fraction\"] = np.minimum(\n",
+    "        1, shapely.area(looks.geometry.values) / shapely.area(features.geometry.values[looks.feature])\n",
+    "    )\n",
+    "    looks[\"viewed_km\"] = np.sqrt(4 * looks.viewed_fraction * features.area_km2.values[looks.feature] / np.pi)\n",
+    "    centroids = shapely.centroid(looks.geometry.values)\n",
+    "    looks[\"lon\"], looks[\"lat\"] = shapely.get_x(centroids), shapely.get_y(centroids)\n",
+    "\n",
+    "    # the viewing geometry (slant range and zenith angle) from the satellite position at the time of the footprint\n",
+    "    looks[\"range_m\"], looks[\"zenith\"] = np.nan, np.nan\n",
+    "    for name, group in looks.groupby(\"satellite\"):\n",
+    "        sat_xyz = np.array(orbits[name].to_gp_orbit().get_orbit_track(list(group.time)).frame_xyz(itrs).m).T\n",
+    "        target = np.stack(TO_ECEF.transform(group.lon.values, group.lat.values, np.zeros(len(group))), axis=-1)\n",
+    "        line_of_sight = sat_xyz - target\n",
+    "        looks.loc[group.index, \"range_m\"] = np.linalg.norm(line_of_sight, axis=-1)\n",
+    "        looks.loc[group.index, \"zenith\"] = np.degrees(\n",
+    "            np.arccos(\n",
+    "                np.sum(line_of_sight * up(group.lon.values, group.lat.values), axis=-1)\n",
+    "                / np.linalg.norm(line_of_sight, axis=-1)\n",
+    "            )\n",
+    "        )\n",
+    "    return looks\n",
+    "\n",
+    "\n",
+    "def assess(looks, features):\n",
+    "    \"\"\"Whether each sensor observes and resolves each feature, from its best look.\"\"\"\n",
+    "    results = []\n",
+    "    for sensor, row in sensors.iterrows():\n",
+    "        sensor_looks = looks[looks.instrument == row.swath].copy()\n",
+    "        # the resolution element at nadir is the larger of the footprint and the sample spacing\n",
+    "        nadir_km = max(row.horizontal_km, row.sampling_km)\n",
+    "        for axis in AXES:\n",
+    "            element_km = element_size(sensor_looks.range_m, sensor_looks.zenith, nadir_km, axis)\n",
+    "            sensor_looks[f\"elements ({axis})\"] = sensor_looks.viewed_km / element_km\n",
+    "            if axis == ELEMENT_AXIS:\n",
+    "                sensor_looks[\"element_km\"] = element_km\n",
+    "        # the best look at each feature: the one whose viewed part spans the most resolution elements\n",
+    "        best = sensor_looks.loc[sensor_looks.groupby(\"feature\")[f\"elements ({ELEMENT_AXIS})\"].idxmax()].set_index(\"feature\")\n",
+    "        result = features[[\"frame\", \"kind\", \"region\", \"diameter_km\", \"area_km2\", \"tot_prectot\"]].copy()\n",
+    "        result[\"sensor\"] = sensor\n",
+    "        result[\"looks\"] = sensor_looks.groupby(\"feature\").size().reindex(result.index, fill_value=0)\n",
+    "        result[\"observed\"] = result.looks > 0\n",
+    "        result = result.join(best[[\"satellite\", \"zenith\", \"viewed_fraction\", \"viewed_km\", \"element_km\"]])\n",
+    "        for axis in AXES:\n",
+    "            result[f\"elements ({axis})\"] = sensor_looks.groupby(\"feature\")[f\"elements ({axis})\"].max()\n",
+    "        result[\"resolved\"] = result[f\"elements ({ELEMENT_AXIS})\"] >= RESOLUTION_FACTOR\n",
+    "        results.append(result)\n",
+    "    return pd.concat(results).reset_index().rename(columns={\"index\": \"feature\"})\n",
+    "\n",
+    "\n",
+    "looks = collect_looks(tracks, features, orbits)\n",
+    "results = assess(looks, features)\n",
+    "results.groupby([\"kind\", \"sensor\"])[[\"observed\", \"resolved\"]].sum().unstack(\"kind\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d062471",
+   "metadata": {},
+   "source": [
+    "## Generate a Visualization\n",
+    "\n",
+    "The figure shows the heavy precipitation features of one frame, the one with the most such features whose status differs between the sensors, colored by whether each sensor resolves them (blue), observes but does not resolve them (orange), or does not observe them (faded, outside the swath). The underlying image is the Nature Run's upwelling longwave flux, whose dark areas are cold cloud tops, and the line is the storm's track over the two days, with its center in the frame marked."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0e740b36",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:47:48.462047Z",
+     "iopub.status.busy": "2026-10-24T21:47:48.462047Z",
+     "iopub.status.idle": "2026-10-24T21:47:50.252601Z",
+     "shell.execute_reply": "2026-10-24T21:47:50.252601Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x1100 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "\n",
+    "KIND = \"heavy precipitation\"\n",
+    "# the frame with the most features of the kind whose status (not observed, observed, or resolved) differs between the sensors\n",
+    "of_kind = results[results.kind == KIND]\n",
+    "status = of_kind.observed.astype(int) + of_kind.resolved.astype(int)\n",
+    "example = (status.groupby([of_kind.frame, of_kind.feature]).nunique() > 1).groupby(\"frame\").sum().idxmax()\n",
+    "example_time = start + example * frame_duration\n",
+    "example_center = track.iloc[np.abs(track.index - pd.Timestamp(nature.time.values[example])).argmin()]\n",
+    "STATUS = {\"not observed\": \"#d9d8d3\", \"observed, not resolved\": \"#eb6834\", \"resolved\": \"#2a78d6\"}\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(11, 11), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
+    "for ax, sensor in zip(axes.ravel(), sensors.index):\n",
+    "    background = nature[\"lwtup\"].isel(time=example)\n",
+    "    ax.pcolormesh(background.lon, background.lat, background, cmap=\"Greys_r\", vmin=100, vmax=320, shading=\"auto\", transform=ccrs.PlateCarree())\n",
+    "    members = of_kind[(of_kind.sensor == sensor) & (of_kind.frame == example)]\n",
+    "    member_status = np.where(members.resolved, \"resolved\", np.where(members.observed, \"observed, not resolved\", \"not observed\"))\n",
+    "    for label, color in STATUS.items():\n",
+    "        selected = features.loc[members.feature[member_status == label]]\n",
+    "        if len(selected):\n",
+    "            selected.plot(ax=ax, facecolor=color, edgecolor=\"black\", linewidth=0.4, transform=ccrs.PlateCarree())\n",
+    "    # fade the part of the study region outside the swath\n",
+    "    swath = swaths.get((example, sensors.loc[sensor, \"swath\"]))\n",
+    "    outside = domain if swath is None else domain.difference(swath)\n",
+    "    ax.add_geometries([outside], crs=ccrs.PlateCarree(), facecolor=\"white\", edgecolor=\"none\", alpha=0.6)\n",
+    "    ax.add_feature(cfeature.COASTLINE, edgecolor=\"#8a8984\", linewidth=0.5)\n",
+    "    ax.plot(track.lon, track.lat, color=\"#0b0b0b\", lw=1, transform=ccrs.PlateCarree())\n",
+    "    ax.plot(example_center.lon, example_center.lat, marker=\"x\", ms=8, mew=2, color=\"#0b0b0b\", transform=ccrs.PlateCarree())\n",
+    "    ax.set_extent([domain.bounds[0], domain.bounds[2], domain.bounds[1], domain.bounds[3]])\n",
+    "    ax.set_title(f\"{sensor} ({sensors.loc[sensor, 'horizontal_km']} km)\", fontsize=10)\n",
+    "for label, color in STATUS.items():\n",
+    "    axes[0, 0].plot([], [], color=color, lw=6, label=label)\n",
+    "axes[0, 0].plot([], [], color=\"#ffffff\", lw=6, alpha=0.6, label=\"outside swath\")\n",
+    "axes[0, 0].legend(loc=\"lower left\", fontsize=8, frameon=True)\n",
+    "fig.suptitle(f\"Heavy precipitation features, {example_time:%Y-%m-%d %H:%M}-{example_time + frame_duration:%H:%M} UTC (Nature Run {nature_start:%Y})\")\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53132a21",
+   "metadata": {},
+   "source": [
+    "## Generate Statistics\n",
+    "\n",
+    "The table gives, for each kind of feature and sensor, the share of the features that are observed and resolved, the share of the total precipitation in the features of the kind that lies in resolved features, and the median resolution element at the observed features, for all features and for those of the storm (within `STORM_RADIUS_KM` of its center)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "37988d01",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:47:50.257606Z",
+     "iopub.status.busy": "2026-10-24T21:47:50.257606Z",
+     "iopub.status.idle": "2026-10-24T21:47:50.281200Z",
+     "shell.execute_reply": "2026-10-24T21:47:50.281200Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>features</th>\n",
+       "      <th>observed (%)</th>\n",
+       "      <th>resolved (%)</th>\n",
+       "      <th>resolved (% of precipitation)</th>\n",
+       "      <th>median element of observed (km)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>features</th>\n",
+       "      <th>kind</th>\n",
+       "      <th>sensor</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"8\" valign=\"top\">all</th>\n",
+       "      <th rowspan=\"4\" valign=\"top\">cold cloud top</th>\n",
+       "      <th>ATMS</th>\n",
+       "      <td>545.0</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>11.8</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CrIS</th>\n",
+       "      <td>545.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>21.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future IR</th>\n",
+       "      <td>545.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>12.8</td>\n",
+       "      <td>12.1</td>\n",
+       "      <td>4.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future MW</th>\n",
+       "      <td>545.0</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>14.1</td>\n",
+       "      <td>15.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">heavy precipitation</th>\n",
+       "      <th>ATMS</th>\n",
+       "      <td>568.0</td>\n",
+       "      <td>9.7</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>47.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CrIS</th>\n",
+       "      <td>568.0</td>\n",
+       "      <td>7.9</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>9.7</td>\n",
+       "      <td>20.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future IR</th>\n",
+       "      <td>568.0</td>\n",
+       "      <td>7.9</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>10.3</td>\n",
+       "      <td>4.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future MW</th>\n",
+       "      <td>568.0</td>\n",
+       "      <td>9.7</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>14.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"8\" valign=\"top\">storm</th>\n",
+       "      <th rowspan=\"4\" valign=\"top\">cold cloud top</th>\n",
+       "      <th>ATMS</th>\n",
+       "      <td>355.0</td>\n",
+       "      <td>13.8</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>48.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CrIS</th>\n",
+       "      <td>355.0</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>12.1</td>\n",
+       "      <td>21.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future IR</th>\n",
+       "      <td>355.0</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>12.1</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future MW</th>\n",
+       "      <td>355.0</td>\n",
+       "      <td>13.8</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>14.2</td>\n",
+       "      <td>15.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">heavy precipitation</th>\n",
+       "      <th>ATMS</th>\n",
+       "      <td>517.0</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>9.7</td>\n",
+       "      <td>45.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CrIS</th>\n",
+       "      <td>517.0</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>20.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future IR</th>\n",
+       "      <td>517.0</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>4.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future MW</th>\n",
+       "      <td>517.0</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>11.4</td>\n",
+       "      <td>14.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                        features  observed (%)  resolved (%)  \\\n",
+       "features kind                sensor                                            \n",
+       "all      cold cloud top      ATMS          545.0          15.4           3.7   \n",
+       "                             CrIS          545.0          13.0           8.4   \n",
+       "                             Future IR     545.0          13.0          12.8   \n",
+       "                             Future MW     545.0          15.4          12.7   \n",
+       "         heavy precipitation ATMS          568.0           9.7           2.8   \n",
+       "                             CrIS          568.0           7.9           4.8   \n",
+       "                             Future IR     568.0           7.9           7.7   \n",
+       "                             Future MW     568.0           9.7           7.4   \n",
+       "storm    cold cloud top      ATMS          355.0          13.8           3.9   \n",
+       "                             CrIS          355.0          12.4           8.2   \n",
+       "                             Future IR     355.0          12.4          12.1   \n",
+       "                             Future MW     355.0          13.8          11.5   \n",
+       "         heavy precipitation ATMS          517.0          10.1           3.1   \n",
+       "                             CrIS          517.0           8.1           5.2   \n",
+       "                             Future IR     517.0           8.1           8.1   \n",
+       "                             Future MW     517.0          10.1           7.7   \n",
+       "\n",
+       "                                        resolved (% of precipitation)  \\\n",
+       "features kind                sensor                                     \n",
+       "all      cold cloud top      ATMS                                11.8   \n",
+       "                             CrIS                                12.0   \n",
+       "                             Future IR                           12.1   \n",
+       "                             Future MW                           14.1   \n",
+       "         heavy precipitation ATMS                                 9.5   \n",
+       "                             CrIS                                 9.7   \n",
+       "                             Future IR                           10.3   \n",
+       "                             Future MW                           11.2   \n",
+       "storm    cold cloud top      ATMS                                11.9   \n",
+       "                             CrIS                                12.1   \n",
+       "                             Future IR                           12.2   \n",
+       "                             Future MW                           14.2   \n",
+       "         heavy precipitation ATMS                                 9.7   \n",
+       "                             CrIS                                10.0   \n",
+       "                             Future IR                           10.5   \n",
+       "                             Future MW                           11.4   \n",
+       "\n",
+       "                                        median element of observed (km)  \n",
+       "features kind                sensor                                      \n",
+       "all      cold cloud top      ATMS                                  50.0  \n",
+       "                             CrIS                                  21.9  \n",
+       "                             Future IR                              4.7  \n",
+       "                             Future MW                             15.6  \n",
+       "         heavy precipitation ATMS                                  47.0  \n",
+       "                             CrIS                                  20.6  \n",
+       "                             Future IR                              4.4  \n",
+       "                             Future MW                             14.7  \n",
+       "storm    cold cloud top      ATMS                                  48.5  \n",
+       "                             CrIS                                  21.1  \n",
+       "                             Future IR                              4.5  \n",
+       "                             Future MW                             15.2  \n",
+       "         heavy precipitation ATMS                                  45.3  \n",
+       "                             CrIS                                  20.1  \n",
+       "                             Future IR                              4.3  \n",
+       "                             Future MW                             14.2  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def summarize(r):\n",
+    "    \"\"\"Shares (%) of the features observed and resolved, and of their precipitation in resolved features.\"\"\"\n",
+    "    return pd.Series(\n",
+    "        {\n",
+    "            \"features\": len(r),\n",
+    "            \"observed (%)\": 100 * r.observed.mean(),\n",
+    "            \"resolved (%)\": 100 * r.resolved.mean(),\n",
+    "            \"resolved (% of precipitation)\": 100 * r.tot_prectot[r.resolved].sum() / r.tot_prectot.sum(),\n",
+    "            \"median element of observed (km)\": r.element_km[r.observed].median(),\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "summary = pd.concat(\n",
+    "    {\n",
+    "        \"all\": results.groupby([\"kind\", \"sensor\"]).apply(summarize, include_groups=False),\n",
+    "        \"storm\": results[results.region == \"storm\"].groupby([\"kind\", \"sensor\"]).apply(summarize, include_groups=False),\n",
+    "    },\n",
+    "    names=[\"features\"],\n",
+    ")\n",
+    "summary.round(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "facf383b",
+   "metadata": {},
+   "source": [
+    "Over the two days, the study region has 545 cold cloud top features and 568 heavy precipitation features at least 28 km in size. Most are the storm's: 355 cold cloud tops, up to 483 km across (its central dense overcast), and 517 patches of heavy precipitation, up to 215 km across (its eyewall and inner rainbands), with a median of about 50 km. The storm's environment adds 190 cold cloud tops but only 51 patches of heavy precipitation, nearly all small.\n",
+    "\n",
+    "The sounders observe 13 to 15% of the cold cloud tops and 8 to 10% of the patches of heavy precipitation (the ATMS swath, which is wider, observes more than the CrIS swath). As over the monsoon, this is limited by when the satellites pass: all three cross the region within about an hour near 05:30 UTC and again near 18:00 UTC (about 01:30 and 13:30 local time), so they observe only a few of the 48 frames of each day, whatever their resolution.\n",
+    "\n",
+    "Resolution matters more for the number of features than for the storm's precipitation. Of the features observed, ATMS resolves about a quarter (20 of 84 cold cloud tops and 16 of 55 patches of heavy precipitation), more than over the monsoon (2 of 41), because the storm's features include large ones. CrIS resolves about 60 to 65%, a future microwave sounder about 80%, and a future infrared sounder nearly all. But within a frame, the storm's largest feature holds most of the precipitation (97% of that under cold cloud tops and 72% of that in heavy precipitation), and every sensor resolves it when observed. The resolved features of every sensor therefore hold nearly all of the precipitation that it observes: ATMS resolves features with 12% of the precipitation under cold cloud tops, of the 14% it observes, and the other sensors resolve all they observe. Finer resolution adds the smaller cells of the rainbands and the storm's environment, which hold little of the precipitation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "705ebfc2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:47:50.283206Z",
+     "iopub.status.busy": "2026-10-24T21:47:50.283206Z",
+     "iopub.status.idle": "2026-10-24T21:47:50.458744Z",
+     "shell.execute_reply": "2026-10-24T21:47:50.458744Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "BLUE, ORANGE, AQUA, YELLOW, GRAY, INK = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#eda100\", \"#8a8984\", \"#0b0b0b\"\n",
+    "SENSOR_COLORS = {\"ATMS\": BLUE, \"Future MW\": ORANGE, \"CrIS\": AQUA, \"Future IR\": YELLOW}\n",
+    "SENSOR_STYLES = {\"ATMS\": \"-\", \"Future MW\": \"--\", \"CrIS\": \"-\", \"Future IR\": \"--\"}\n",
+    "plt.rcParams.update({\"axes.spines.top\": False, \"axes.spines.right\": False, \"axes.grid\": True, \"grid.color\": \"#e6e5e0\", \"grid.linewidth\": 0.6})\n",
+    "\n",
+    "edges = np.array([EFFECTIVE_RESOLUTION_KM, 40, 56, 80, 112, np.inf])\n",
+    "labels = [f\"{EFFECTIVE_RESOLUTION_KM}-40\", \"40-56\", \"56-80\", \"80-112\", \">112\"]\n",
+    "results[\"size_class\"] = pd.cut(results.diameter_km, edges, labels=labels, right=False)\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(11, 4), sharey=True)\n",
+    "for ax, kind in zip(axes, KINDS):\n",
+    "    of_kind = results[results.kind == kind]\n",
+    "    observed = of_kind[of_kind.observed]\n",
+    "    by_size = observed.groupby([\"sensor\", \"size_class\"], observed=False).resolved.mean().unstack().loc[sensors.index] * 100\n",
+    "    for sensor in sensors.index:\n",
+    "        ax.plot(labels, by_size.loc[sensor], marker=\"o\", color=SENSOR_COLORS[sensor], ls=SENSOR_STYLES[sensor], label=sensor)\n",
+    "    counts = observed[observed.sensor == \"CrIS\"].groupby(\"size_class\", observed=False).size()\n",
+    "    ax.set_xticks(range(len(labels)), [f\"{label}\\n(n = {n})\" for label, n in zip(labels, counts)])\n",
+    "    ax.set(xlabel=\"Feature diameter (km), with the number observed in the CrIS swath\", title=f\"{kind.capitalize()} features\", ylim=(0, 105))\n",
+    "    ax.title.set_fontsize(10)\n",
+    "axes[0].set_ylabel(\"Observed features resolved (%)\")\n",
+    "axes[0].legend(frameon=False, fontsize=8, loc=\"lower right\")\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df0bde85",
+   "metadata": {},
+   "source": [
+    "The figure shows, by size, the share of the observed features that each sensor resolves (in contrast to the monsoon OSSE, of the features observed rather than of all features, so that it shows resolution alone). The sensors differ most for the smallest features (28 to 56 km), which ATMS does not resolve at all and a future infrared sounder nearly always resolves, and least for those larger than 112 km, which every sensor resolves except where ATMS views only part of them at the edge of its swath. The counts per size class are small (4 to 23 features), so the shares of a class are uncertain by tens of percentage points."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f8317f5",
+   "metadata": {},
+   "source": [
+    "## Limitations\n",
+    "\n",
+    "The limitations of the monsoon OSSE apply, notably that resolution is geometric (without the response of each footprint), that the future sensors are assumed to share the swaths and platforms of ATMS and CrIS, and that features are snapshots. In addition:\n",
+    "\n",
+    "* **One storm, two days.** The results describe one simulated storm at one stage of its life, sampled by one realization of the satellites' passes. Other storms, stages, and dates (extend `start` and `duration`, or find other storms in the coarsened Nature Run) would be needed for statistics.\n",
+    "* **The study region is fitted to the storm.** Features of the storm's environment are counted only within the box around its track, so their number depends on the box.\n",
+    "* **The storm center is coarse.** The center is the minimum sea-level pressure of the 0.5 degree, hourly collection, interpolated to the frames, so it is uncertain by tens of kilometers, which matters little for the 500 km radius that attributes features to the storm.\n",
+    "* **The features are proxies.** Cold cloud tops and heavy precipitation identify where observations may be valuable; the quantities a sounder retrieves in a hurricane (the warm core, the temperature and humidity of its environment, the rain and ice of its rainbands) depend on its channels, and infrared sounders do not see beneath the cloud tops.\n",
+    "* **The Nature Run is not reality.** Its storm is simulated at 7 km, which does not resolve the eyewall of a strong hurricane, so its smallest features (and the precipitation rates within them) differ from those of a real storm."
+   ]
+  }
+ ],
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+}
diff --git a/docs/examples/RunOSSEMonsoon.ipynb b/docs/examples/RunOSSEMonsoon.ipynb
new file mode 100644
index 00000000..78f90c32
--- /dev/null
+++ b/docs/examples/RunOSSEMonsoon.ipynb
@@ -0,0 +1,2604 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "328188aa",
+   "metadata": {},
+   "source": [
+    "# OSSE: North American Monsoon Convection\n",
+    "\n",
+    "An observing system simulation experiment (OSSE) evaluates how well an observing system, real or planned, would observe a phenomenon, by sampling a high-resolution model simulation (the *nature run*) as the system would. This example is an OSSE of **convection during the North American Monsoon**. The nature run is the 7-km [GEOS-5 Nature Run (G5NR)](https://gmao.gsfc.nasa.gov/global_mesoscale/7km-G5NR/). The *features* of interest are patches of very cold cloud tops over the American Southwest. The observing systems are the atmospheric sounders on the current NOAA/NASA polar-orbiting satellites ([ATMS](ValidateImagerATMS.ipynb) and [CrIS](ValidateImagerCrIS.ipynb)) and two notional future sounders with finer resolution.\n",
+    "\n",
+    "The experiment asks two questions about each feature:\n",
+    "\n",
+    "1. **Is it observed?** Does a sounder's swath cover the feature, as in the ground-track analyses of earlier TAT-C studies?\n",
+    "2. **Is it resolved?** Is the part of the feature that is observed at least a few resolution elements (footprints or the spacing between them) across? The footprint of a cross-track scanner grows from its nadir value toward the swath edge, so this depends on where in the swath the feature falls.\n",
+    "\n",
+    "The steps are:\n",
+    "\n",
+    "1. configure the scenario and the nature run (G5NR);\n",
+    "2. define the features (contiguous cold cloud tops) in each 30-minute frame of the nature run;\n",
+    "3. define the sensors (swath parameters fitted to real data in the validation notebooks, and horizontal resolution);\n",
+    "4. compute the ground tracks (swaths) of the satellites over the study region with TAT-C;\n",
+    "5. determine whether each feature is observed and resolved by each sensor;\n",
+    "6. generate a visualization and statistics; and\n",
+    "7. trade the design of the constellation: the number of satellites, their orbit planes, and their sensors.\n",
+    "\n",
+    "The example is designed to run in a few minutes, and the constellation trade in a few more (after a one-time download of two weeks of the nature run). It requires the optional dependencies for OSSE examples (`pip install tatc[osse]`)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24d4bc38",
+   "metadata": {},
+   "source": [
+    "## Configure the Scenario\n",
+    "\n",
+    "The study region is the American Southwest (31-38 N, 115-103 W: Arizona, New Mexico, and neighboring states), the core of the North American Monsoon, whose rains arrive between mid-June and mid-September. The scenario covers two days of the season at the Nature Run's 30-minute time-averaging interval, and is easily extended to the whole season by changing `start` and `duration` (the downloaded data and the computation scale linearly).\n",
+    "\n",
+    "The Nature Run covers 15 May 2005 to 16 June 2007 and does not correspond to any real year, so the satellite orbits (those of 16 July 2026, from operational element sets) are paired with the nature run on the same calendar date of its first year. This is appropriate because the orbits are sun-synchronous, so their local solar times of observation do not depend on the date, and the Nature Run represents the diurnal cycle of the season."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d4c76230",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:16:27.746711Z",
+     "iopub.status.busy": "2026-10-24T21:16:27.745208Z",
+     "iopub.status.idle": "2026-10-24T21:16:28.704832Z",
+     "shell.execute_reply": "2026-10-24T21:16:28.704832Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from datetime import datetime, timedelta, timezone\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import geopandas as gpd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import shapely\n",
+    "from shapely.geometry import box\n",
+    "\n",
+    "DATA_DIR = Path(\"data\") / \"osse\"\n",
+    "DATA_DIR.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "# study region: the American Southwest, the core of the North American Monsoon\n",
+    "domain = box(-115, 31, -103, 38)\n",
+    "# the simulated orbits (a mid-season date) and the nature run (the same calendar date in the Nature Run's first year)\n",
+    "start = datetime(2026, 7, 16, tzinfo=timezone.utc)\n",
+    "nature_start = start.replace(year=2005)\n",
+    "duration = timedelta(days=2)\n",
+    "frame_duration = timedelta(minutes=30)  # the Nature Run's time-averaging interval\n",
+    "n_frames = int(duration / frame_duration)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e7d1592",
+   "metadata": {},
+   "source": [
+    "## Configure the Nature Run\n",
+    "\n",
+    "The G5NR is a two-year global simulation by NASA's Global Modeling and Assimilation Office (GMAO) at a 7-km horizontal resolution, made for OSSEs (Gelaro et al., 2015). NASA serves it by OPeNDAP, which streams only the subset requested. This example uses the single-level, 30-minute time-averaged physics diagnostics at 0.0625 degrees (about 7 km), and reads the upwelling longwave flux at the top of the atmosphere (`lwtup`), a proxy for cloud-top temperature, and the convective precipitation rate (`preccon`). All frames for the study region are requested at once and saved in a local `data/osse` directory, so later runs are fast."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8f96f040",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:16:28.707343Z",
+     "iopub.status.busy": "2026-10-24T21:16:28.707343Z",
+     "iopub.status.idle": "2026-10-24T21:16:30.273969Z",
+     "shell.execute_reply": "2026-10-24T21:16:30.273464Z"
+    }
+   },
+   "outputs": [
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+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 17MB\n",
+       "Dimensions:  (time: 96, lat: 113, lon: 193)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 768B 2005-07-16T00:15:00 ... 2005-07-17T23...\n",
+       "  * lat      (lat) float64 904B 31.0 31.06 31.12 31.19 ... 37.88 37.94 38.0\n",
+       "  * lon      (lon) float64 2kB -115.0 -114.9 -114.9 ... -103.1 -103.1 -103.0\n",
+       "Data variables:\n",
+       "    lwtup    (time, lat, lon) float32 8MB 314.9 315.9 314.4 ... 301.4 301.4\n",
+       "    preccon  (time, lat, lon) float32 8MB 0.0 0.0 0.0 ... 0.0 1.764e-08\n",
+       "Attributes:\n",
+       "    title:                2d,30-Minute,Time-Averaged,Single-Level,Full Resolu...\n",
+       "    Conventions:          COARDS\\nGrADS\n",
+       "    dataType:             Grid\n",
+       "    history:              Mon Sep 28 14:37:35 EDT 2026 : imported by GrADS Da...\n",
+       "    extra_das_attribute:  This is an example of metadata added using a supple...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-32394326-e88b-4e87-b485-0e21f1c01035' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-32394326-e88b-4e87-b485-0e21f1c01035' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 96</li><li><span class='xr-has-index'>lat</span>: 113</li><li><span class='xr-has-index'>lon</span>: 193</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-18eb0a95-fad7-4ed6-a1e7-f857246615e2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-18eb0a95-fad7-4ed6-a1e7-f857246615e2' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2005-07-16T00:15:00 ... 2005-07-...</div><input id='attrs-247a3c2a-a323-4b67-824d-64bf77b6df90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-247a3c2a-a323-4b67-824d-64bf77b6df90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c81cebe5-75a0-49dc-9839-43c4002bdd1f' class='xr-var-data-in' type='checkbox'><label for='data-c81cebe5-75a0-49dc-9839-43c4002bdd1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grads_dim :</span></dt><dd>t</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>36576</dd><dt><span>grads_min :</span></dt><dd>21:15z15may2005</dd><dt><span>grads_step :</span></dt><dd>30mn</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>minimum :</span></dt><dd>21:15z15may2005</dd><dt><span>maximum :</span></dt><dd>20:45z16jun2007</dd><dt><span>resolution :</span></dt><dd>0.020833334</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2005-07-16T00:15:00.000000000&#x27;, &#x27;2005-07-16T00:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T01:15:00.000000000&#x27;, &#x27;2005-07-16T01:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T02:15:00.000000000&#x27;, &#x27;2005-07-16T02:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T03:15:00.000000000&#x27;, &#x27;2005-07-16T03:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T04:15:00.000000000&#x27;, &#x27;2005-07-16T04:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T05:15:00.000000000&#x27;, &#x27;2005-07-16T05:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T06:15:00.000000000&#x27;, &#x27;2005-07-16T06:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T07:15:00.000000000&#x27;, &#x27;2005-07-16T07:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T08:15:00.000000000&#x27;, &#x27;2005-07-16T08:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T09:15:00.000000000&#x27;, &#x27;2005-07-16T09:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T10:15:00.000000000&#x27;, &#x27;2005-07-16T10:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T11:15:00.000000000&#x27;, &#x27;2005-07-16T11:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T12:15:00.000000000&#x27;, &#x27;2005-07-16T12:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T13:15:00.000000000&#x27;, &#x27;2005-07-16T13:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T14:15:00.000000000&#x27;, &#x27;2005-07-16T14:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T15:15:00.000000000&#x27;, &#x27;2005-07-16T15:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T16:15:00.000000000&#x27;, &#x27;2005-07-16T16:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T17:15:00.000000000&#x27;, &#x27;2005-07-16T17:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T18:15:00.000000000&#x27;, &#x27;2005-07-16T18:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T19:15:00.000000000&#x27;, &#x27;2005-07-16T19:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T20:15:00.000000000&#x27;, &#x27;2005-07-16T20:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T21:15:00.000000000&#x27;, &#x27;2005-07-16T21:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T22:15:00.000000000&#x27;, &#x27;2005-07-16T22:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-16T23:15:00.000000000&#x27;, &#x27;2005-07-16T23:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T00:15:00.000000000&#x27;, &#x27;2005-07-17T00:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T01:15:00.000000000&#x27;, &#x27;2005-07-17T01:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T02:15:00.000000000&#x27;, &#x27;2005-07-17T02:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T03:15:00.000000000&#x27;, &#x27;2005-07-17T03:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T04:15:00.000000000&#x27;, &#x27;2005-07-17T04:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T05:15:00.000000000&#x27;, &#x27;2005-07-17T05:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T06:15:00.000000000&#x27;, &#x27;2005-07-17T06:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T07:15:00.000000000&#x27;, &#x27;2005-07-17T07:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T08:15:00.000000000&#x27;, &#x27;2005-07-17T08:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T09:15:00.000000000&#x27;, &#x27;2005-07-17T09:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T10:15:00.000000000&#x27;, &#x27;2005-07-17T10:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T11:15:00.000000000&#x27;, &#x27;2005-07-17T11:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T12:15:00.000000000&#x27;, &#x27;2005-07-17T12:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T13:15:00.000000000&#x27;, &#x27;2005-07-17T13:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T14:15:00.000000000&#x27;, &#x27;2005-07-17T14:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T15:15:00.000000000&#x27;, &#x27;2005-07-17T15:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T16:15:00.000000000&#x27;, &#x27;2005-07-17T16:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T17:15:00.000000000&#x27;, &#x27;2005-07-17T17:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T18:15:00.000000000&#x27;, &#x27;2005-07-17T18:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T19:15:00.000000000&#x27;, &#x27;2005-07-17T19:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T20:15:00.000000000&#x27;, &#x27;2005-07-17T20:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T21:15:00.000000000&#x27;, &#x27;2005-07-17T21:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T22:15:00.000000000&#x27;, &#x27;2005-07-17T22:45:00.000000000&#x27;,\n",
+       "       &#x27;2005-07-17T23:15:00.000000000&#x27;, &#x27;2005-07-17T23:45:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>31.0 31.06 31.12 ... 37.94 38.0</div><input id='attrs-20117d5e-b6c3-4459-9031-40631e3acb7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20117d5e-b6c3-4459-9031-40631e3acb7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32070096-7dba-4608-a841-557b686a35a5' class='xr-var-data-in' type='checkbox'><label for='data-32070096-7dba-4608-a841-557b686a35a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grads_dim :</span></dt><dd>y</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>2881</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>minimum :</span></dt><dd>-90.0</dd><dt><span>maximum :</span></dt><dd>90.0</dd><dt><span>resolution :</span></dt><dd>0.0625</dd></dl></div><div class='xr-var-data'><pre>array([31.    , 31.0625, 31.125 , 31.1875, 31.25  , 31.3125, 31.375 , 31.4375,\n",
+       "       31.5   , 31.5625, 31.625 , 31.6875, 31.75  , 31.8125, 31.875 , 31.9375,\n",
+       "       32.    , 32.0625, 32.125 , 32.1875, 32.25  , 32.3125, 32.375 , 32.4375,\n",
+       "       32.5   , 32.5625, 32.625 , 32.6875, 32.75  , 32.8125, 32.875 , 32.9375,\n",
+       "       33.    , 33.0625, 33.125 , 33.1875, 33.25  , 33.3125, 33.375 , 33.4375,\n",
+       "       33.5   , 33.5625, 33.625 , 33.6875, 33.75  , 33.8125, 33.875 , 33.9375,\n",
+       "       34.    , 34.0625, 34.125 , 34.1875, 34.25  , 34.3125, 34.375 , 34.4375,\n",
+       "       34.5   , 34.5625, 34.625 , 34.6875, 34.75  , 34.8125, 34.875 , 34.9375,\n",
+       "       35.    , 35.0625, 35.125 , 35.1875, 35.25  , 35.3125, 35.375 , 35.4375,\n",
+       "       35.5   , 35.5625, 35.625 , 35.6875, 35.75  , 35.8125, 35.875 , 35.9375,\n",
+       "       36.    , 36.0625, 36.125 , 36.1875, 36.25  , 36.3125, 36.375 , 36.4375,\n",
+       "       36.5   , 36.5625, 36.625 , 36.6875, 36.75  , 36.8125, 36.875 , 36.9375,\n",
+       "       37.    , 37.0625, 37.125 , 37.1875, 37.25  , 37.3125, 37.375 , 37.4375,\n",
+       "       37.5   , 37.5625, 37.625 , 37.6875, 37.75  , 37.8125, 37.875 , 37.9375,\n",
+       "       38.    ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-115.0 -114.9 ... -103.1 -103.0</div><input id='attrs-a35a3526-61f0-4b02-8154-a38870338165' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a35a3526-61f0-4b02-8154-a38870338165' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1579fbfe-020c-41ea-b55b-d38c563b8c80' class='xr-var-data-in' type='checkbox'><label for='data-1579fbfe-020c-41ea-b55b-d38c563b8c80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grads_dim :</span></dt><dd>x</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>5760</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>minimum :</span></dt><dd>-180.0</dd><dt><span>maximum :</span></dt><dd>179.9375</dd><dt><span>resolution :</span></dt><dd>0.0625</dd></dl></div><div class='xr-var-data'><pre>array([-115.    , -114.9375, -114.875 , -114.8125, -114.75  , -114.6875,\n",
+       "       -114.625 , -114.5625, -114.5   , -114.4375, -114.375 , -114.3125,\n",
+       "       -114.25  , -114.1875, -114.125 , -114.0625, -114.    , -113.9375,\n",
+       "       -113.875 , -113.8125, -113.75  , -113.6875, -113.625 , -113.5625,\n",
+       "       -113.5   , -113.4375, -113.375 , -113.3125, -113.25  , -113.1875,\n",
+       "       -113.125 , -113.0625, -113.    , -112.9375, -112.875 , -112.8125,\n",
+       "       -112.75  , -112.6875, -112.625 , -112.5625, -112.5   , -112.4375,\n",
+       "       -112.375 , -112.3125, -112.25  , -112.1875, -112.125 , -112.0625,\n",
+       "       -112.    , -111.9375, -111.875 , -111.8125, -111.75  , -111.6875,\n",
+       "       -111.625 , -111.5625, -111.5   , -111.4375, -111.375 , -111.3125,\n",
+       "       -111.25  , -111.1875, -111.125 , -111.0625, -111.    , -110.9375,\n",
+       "       -110.875 , -110.8125, -110.75  , -110.6875, -110.625 , -110.5625,\n",
+       "       -110.5   , -110.4375, -110.375 , -110.3125, -110.25  , -110.1875,\n",
+       "       -110.125 , -110.0625, -110.    , -109.9375, -109.875 , -109.8125,\n",
+       "       -109.75  , -109.6875, -109.625 , -109.5625, -109.5   , -109.4375,\n",
+       "       -109.375 , -109.3125, -109.25  , -109.1875, -109.125 , -109.0625,\n",
+       "       -109.    , -108.9375, -108.875 , -108.8125, -108.75  , -108.6875,\n",
+       "       -108.625 , -108.5625, -108.5   , -108.4375, -108.375 , -108.3125,\n",
+       "       -108.25  , -108.1875, -108.125 , -108.0625, -108.    , -107.9375,\n",
+       "       -107.875 , -107.8125, -107.75  , -107.6875, -107.625 , -107.5625,\n",
+       "       -107.5   , -107.4375, -107.375 , -107.3125, -107.25  , -107.1875,\n",
+       "       -107.125 , -107.0625, -107.    , -106.9375, -106.875 , -106.8125,\n",
+       "       -106.75  , -106.6875, -106.625 , -106.5625, -106.5   , -106.4375,\n",
+       "       -106.375 , -106.3125, -106.25  , -106.1875, -106.125 , -106.0625,\n",
+       "       -106.    , -105.9375, -105.875 , -105.8125, -105.75  , -105.6875,\n",
+       "       -105.625 , -105.5625, -105.5   , -105.4375, -105.375 , -105.3125,\n",
+       "       -105.25  , -105.1875, -105.125 , -105.0625, -105.    , -104.9375,\n",
+       "       -104.875 , -104.8125, -104.75  , -104.6875, -104.625 , -104.5625,\n",
+       "       -104.5   , -104.4375, -104.375 , -104.3125, -104.25  , -104.1875,\n",
+       "       -104.125 , -104.0625, -104.    , -103.9375, -103.875 , -103.8125,\n",
+       "       -103.75  , -103.6875, -103.625 , -103.5625, -103.5   , -103.4375,\n",
+       "       -103.375 , -103.3125, -103.25  , -103.1875, -103.125 , -103.0625,\n",
+       "       -103.    ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-101e9b26-f0ae-4742-9428-70f9bc987d52' class='xr-section-summary-in' type='checkbox'  checked><label for='section-101e9b26-f0ae-4742-9428-70f9bc987d52' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lwtup</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>314.9 315.9 314.4 ... 301.4 301.4</div><input id='attrs-12e2ae4d-1ee6-4652-b253-290a8a121b59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12e2ae4d-1ee6-4652-b253-290a8a121b59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05029d99-7ccc-435d-a379-ccf2e8a150a7' class='xr-var-data-in' type='checkbox'><label for='data-05029d99-7ccc-435d-a379-ccf2e8a150a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>long_name :</span></dt><dd>upwelling_longwave_flux_at_toa </dd></dl></div><div class='xr-var-data'><pre>array([[[314.8598 , 315.8598 , 314.3598 , ..., 300.1098 , 299.8598 ,\n",
+       "         299.8598 ],\n",
+       "        [317.1098 , 316.8598 , 313.3598 , ..., 300.1098 , 299.8598 ,\n",
+       "         299.6098 ],\n",
+       "        [319.1098 , 316.3598 , 311.8598 , ..., 300.1098 , 300.1098 ,\n",
+       "         299.6098 ],\n",
+       "        ...,\n",
+       "        [272.8598 , 282.1098 , 273.6098 , ..., 208.3598 , 241.3598 ,\n",
+       "         276.3598 ],\n",
+       "        [284.3598 , 291.3598 , 289.6098 , ..., 226.8598 , 254.3598 ,\n",
+       "         281.6098 ],\n",
+       "        [271.3598 , 278.1098 , 280.6098 , ..., 264.1098 , 268.3598 ,\n",
+       "         271.6098 ]],\n",
+       "\n",
+       "       [[313.0243 , 314.2743 , 313.0243 , ..., 298.2743 , 298.2743 ,\n",
+       "         298.0243 ],\n",
+       "        [316.2743 , 315.5243 , 312.2743 , ..., 298.0243 , 298.0243 ,\n",
+       "         297.2743 ],\n",
+       "        [317.0243 , 314.5243 , 310.7743 , ..., 298.2743 , 297.7743 ,\n",
+       "         294.2743 ],\n",
+       "...\n",
+       "        [325.6167 , 327.6167 , 327.6167 , ..., 304.1167 , 303.1167 ,\n",
+       "         303.1167 ],\n",
+       "        [324.1167 , 326.6167 , 327.6167 , ..., 303.8667 , 303.3667 ,\n",
+       "         303.1167 ],\n",
+       "        [321.8667 , 324.6167 , 326.6167 , ..., 303.8667 , 303.6167 ,\n",
+       "         303.1167 ]],\n",
+       "\n",
+       "       [[299.8705 , 300.8705 , 299.1205 , ..., 157.12051, 152.87051,\n",
+       "         153.62051],\n",
+       "        [299.8705 , 299.3705 , 295.6205 , ..., 165.62051, 152.12051,\n",
+       "         146.12051],\n",
+       "        [297.3705 , 295.6205 , 291.8705 , ..., 188.87051, 161.62051,\n",
+       "         146.49551],\n",
+       "        ...,\n",
+       "        [321.6205 , 324.6205 , 325.1205 , ..., 301.8705 , 301.3705 ,\n",
+       "         301.3705 ],\n",
+       "        [321.1205 , 324.1205 , 325.1205 , ..., 301.6205 , 301.3705 ,\n",
+       "         301.3705 ],\n",
+       "        [320.1205 , 322.1205 , 323.6205 , ..., 301.3705 , 301.3705 ,\n",
+       "         301.3705 ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>preccon</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 0.0 1.764e-08</div><input id='attrs-b943bfed-a137-48de-aa90-b359f6800fc5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b943bfed-a137-48de-aa90-b359f6800fc5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3f4759e-71f2-44c1-a65f-63720f756b0a' class='xr-var-data-in' type='checkbox'><label for='data-f3f4759e-71f2-44c1-a65f-63720f756b0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>long_name :</span></dt><dd>convective_precipitation </dd></dl></div><div class='xr-var-data'><pre>array([[[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "        ...,\n",
+       "        [6.58445060e-07, 7.47852027e-07, 6.46337867e-07, ...,\n",
+       "         1.69686973e-06, 8.09319317e-07, 5.12227416e-08],\n",
+       "        [2.03773379e-06, 1.35786831e-06, 1.33179128e-06, ...,\n",
+       "         1.62608922e-06, 5.37373126e-07, 1.06403604e-07],\n",
+       "        [2.19792128e-06, 9.92789865e-07, 9.92789865e-07, ...,\n",
+       "         1.71549618e-06, 1.04866922e-06, 5.73694706e-07]],\n",
+       "\n",
+       "       [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "...\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         5.73345460e-08, 1.92958396e-08, 1.74914021e-08],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         3.42333806e-09, 0.00000000e+00, 7.50878826e-09],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         6.76664058e-09, 4.25643520e-09, 1.60944182e-08]],\n",
+       "\n",
+       "       [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         5.70900738e-07, 1.12690032e-06, 2.33575702e-06],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         3.15718353e-07, 2.12108716e-07, 4.86150384e-07],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         3.73926014e-07, 1.47381797e-07, 1.72061846e-07],\n",
+       "        ...,\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         1.16880983e-07, 7.98609108e-08, 1.23836799e-08],\n",
+       "        [1.00553734e-08, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         7.64848664e-08, 3.17813829e-08, 9.80799086e-09],\n",
+       "        [2.01107468e-08, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         1.35769369e-08, 0.00000000e+00, 1.76369213e-08]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c4460b99-b5f1-4972-a4da-99b37f588a2f' class='xr-section-summary-in' type='checkbox'  ><label for='section-c4460b99-b5f1-4972-a4da-99b37f588a2f' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-1e9928b0-4286-4f10-9ac9-b80b8f7b36f5' class='xr-index-data-in' type='checkbox'/><label for='index-1e9928b0-4286-4f10-9ac9-b80b8f7b36f5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2005-07-16 00:15:00&#x27;, &#x27;2005-07-16 00:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 01:15:00&#x27;, &#x27;2005-07-16 01:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 02:15:00&#x27;, &#x27;2005-07-16 02:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 03:15:00&#x27;, &#x27;2005-07-16 03:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 04:15:00&#x27;, &#x27;2005-07-16 04:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 05:15:00&#x27;, &#x27;2005-07-16 05:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 06:15:00&#x27;, &#x27;2005-07-16 06:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 07:15:00&#x27;, &#x27;2005-07-16 07:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 08:15:00&#x27;, &#x27;2005-07-16 08:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 09:15:00&#x27;, &#x27;2005-07-16 09:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 10:15:00&#x27;, &#x27;2005-07-16 10:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 11:15:00&#x27;, &#x27;2005-07-16 11:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 12:15:00&#x27;, &#x27;2005-07-16 12:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 13:15:00&#x27;, &#x27;2005-07-16 13:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 14:15:00&#x27;, &#x27;2005-07-16 14:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 15:15:00&#x27;, &#x27;2005-07-16 15:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 16:15:00&#x27;, &#x27;2005-07-16 16:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 17:15:00&#x27;, &#x27;2005-07-16 17:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 18:15:00&#x27;, &#x27;2005-07-16 18:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 19:15:00&#x27;, &#x27;2005-07-16 19:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 20:15:00&#x27;, &#x27;2005-07-16 20:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 21:15:00&#x27;, &#x27;2005-07-16 21:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 22:15:00&#x27;, &#x27;2005-07-16 22:45:00&#x27;,\n",
+       "               &#x27;2005-07-16 23:15:00&#x27;, &#x27;2005-07-16 23:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 00:15:00&#x27;, &#x27;2005-07-17 00:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 01:15:00&#x27;, &#x27;2005-07-17 01:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 02:15:00&#x27;, &#x27;2005-07-17 02:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 03:15:00&#x27;, &#x27;2005-07-17 03:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 04:15:00&#x27;, &#x27;2005-07-17 04:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 05:15:00&#x27;, &#x27;2005-07-17 05:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 06:15:00&#x27;, &#x27;2005-07-17 06:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 07:15:00&#x27;, &#x27;2005-07-17 07:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 08:15:00&#x27;, &#x27;2005-07-17 08:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 09:15:00&#x27;, &#x27;2005-07-17 09:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 10:15:00&#x27;, &#x27;2005-07-17 10:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 11:15:00&#x27;, &#x27;2005-07-17 11:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 12:15:00&#x27;, &#x27;2005-07-17 12:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 13:15:00&#x27;, &#x27;2005-07-17 13:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 14:15:00&#x27;, &#x27;2005-07-17 14:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 15:15:00&#x27;, &#x27;2005-07-17 15:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 16:15:00&#x27;, &#x27;2005-07-17 16:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 17:15:00&#x27;, &#x27;2005-07-17 17:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 18:15:00&#x27;, &#x27;2005-07-17 18:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 19:15:00&#x27;, &#x27;2005-07-17 19:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 20:15:00&#x27;, &#x27;2005-07-17 20:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 21:15:00&#x27;, &#x27;2005-07-17 21:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 22:15:00&#x27;, &#x27;2005-07-17 22:45:00&#x27;,\n",
+       "               &#x27;2005-07-17 23:15:00&#x27;, &#x27;2005-07-17 23:45:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-9cc52457-d642-45a6-986c-00ecdb358048' class='xr-index-data-in' type='checkbox'/><label for='index-9cc52457-d642-45a6-986c-00ecdb358048' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   31.0, 31.0625,  31.125, 31.1875,   31.25, 31.3125,  31.375, 31.4375,\n",
+       "          31.5, 31.5625,\n",
+       "       ...\n",
+       "       37.4375,    37.5, 37.5625,  37.625, 37.6875,   37.75, 37.8125,  37.875,\n",
+       "       37.9375,    38.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=113))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-74a7afac-0974-4131-803d-fd30b0447fe5' class='xr-index-data-in' type='checkbox'/><label for='index-74a7afac-0974-4131-803d-fd30b0447fe5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   -115.0, -114.9375,  -114.875, -114.8125,   -114.75, -114.6875,\n",
+       "        -114.625, -114.5625,    -114.5, -114.4375,\n",
+       "       ...\n",
+       "       -103.5625,    -103.5, -103.4375,  -103.375, -103.3125,   -103.25,\n",
+       "       -103.1875,  -103.125, -103.0625,    -103.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=193))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d86fbcdd-20b4-455c-8f62-41c75b1360d6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d86fbcdd-20b4-455c-8f62-41c75b1360d6' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>2d,30-Minute,Time-Averaged,Single-Level,Full Resolution,Single-Level Physics Diagnostics</dd><dt><span>Conventions :</span></dt><dd>COARDS\n",
+       "GrADS</dd><dt><span>dataType :</span></dt><dd>Grid</dd><dt><span>history :</span></dt><dd>Mon Sep 28 14:37:35 EDT 2026 : imported by GrADS Data Server 2.0</dd><dt><span>extra_das_attribute :</span></dt><dd>This is an example of metadata added using a supplementary DAS file. See testdata/extra.das for the source.</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 17MB\n",
+       "Dimensions:  (time: 96, lat: 113, lon: 193)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 768B 2005-07-16T00:15:00 ... 2005-07-17T23...\n",
+       "  * lat      (lat) float64 904B 31.0 31.06 31.12 31.19 ... 37.88 37.94 38.0\n",
+       "  * lon      (lon) float64 2kB -115.0 -114.9 -114.9 ... -103.1 -103.1 -103.0\n",
+       "Data variables:\n",
+       "    lwtup    (time, lat, lon) float32 8MB 314.9 315.9 314.4 ... 301.4 301.4\n",
+       "    preccon  (time, lat, lon) float32 8MB 0.0 0.0 0.0 ... 0.0 1.764e-08\n",
+       "Attributes:\n",
+       "    title:                2d,30-Minute,Time-Averaged,Single-Level,Full Resolu...\n",
+       "    Conventions:          COARDS\\nGrADS\n",
+       "    dataType:             Grid\n",
+       "    history:              Mon Sep 28 14:37:35 EDT 2026 : imported by GrADS Da...\n",
+       "    extra_das_attribute:  This is an example of metadata added using a supple..."
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import xarray as xr\n",
+    "\n",
+    "G5NR_URL = \"https://opendap.nccs.nasa.gov/dods/OSSE/G5NR/Ganymed/7km/0.0625_deg/tavg/tavg30mn_2d_met3_Nx\"\n",
+    "GRID_SIZE = 0.0625  # deg\n",
+    "\n",
+    "\n",
+    "def load_nature(duration):\n",
+    "    \"\"\"The Nature Run over the study region for a duration from nature_start, downloaded once to DATA_DIR.\"\"\"\n",
+    "    nature_file = DATA_DIR / f\"g5nr_{nature_start:%Y%m%d}_{duration.days}d.nc\"\n",
+    "    if not nature_file.exists():\n",
+    "        # one request for all frames of the study region (a few minutes per two weeks)\n",
+    "        minx, miny, maxx, maxy = domain.bounds\n",
+    "        with xr.open_dataset(G5NR_URL, decode_times=True) as remote:\n",
+    "            remote[[\"lwtup\", \"preccon\"]].sel(\n",
+    "                time=slice(nature_start.replace(tzinfo=None), (nature_start + duration).replace(tzinfo=None)),\n",
+    "                lon=slice(minx, maxx),\n",
+    "                lat=slice(miny, maxy),\n",
+    "            ).load().to_netcdf(nature_file)\n",
+    "    nature = xr.load_dataset(nature_file)\n",
+    "    assert nature.sizes[\"time\"] == int(duration / frame_duration)\n",
+    "    # each time stamp is the middle of its 30-minute averaging interval\n",
+    "    assert (nature.time.values[0] - np.datetime64(nature_start.replace(tzinfo=None))) == np.timedelta64(15, \"m\")\n",
+    "    return nature\n",
+    "\n",
+    "\n",
+    "nature = load_nature(duration)\n",
+    "nature"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5bbfd639",
+   "metadata": {},
+   "source": [
+    "## Define the Environmental Features\n",
+    "\n",
+    "Features are contiguous patches of grid cells whose upwelling longwave flux is below that of a 220 K blackbody (`5.67037e-8 * 220**4` W m-2, about 133 W m-2), that is, whose clouds are very cold and therefore very high, as in deep convection. Each patch in each frame is a feature. (A convective system that persists for several frames is counted in each, and a feature is a snapshot of it.) Patches smaller than the Nature Run's effective resolution are discarded, because no sensor can be evaluated against features that the Nature Run does not credibly represent. Like other models, the G5NR damps its smallest scales: its evaluation finds that its true resolution \"should be considered as several times coarser than its grid spacing of 7 km\" but gives no single value (Gelaro et al., 2015, Section 2.2). Features are therefore kept if their size is at least `EFFECTIVE_RESOLUTION_KM`, four grid spacings (28 km) by default.\n",
+    "\n",
+    "For each feature, the notebook records its area, the diameter of a circle of the same area (its size, used throughout), the area-weighted centroid, its coldest flux and highest convective precipitation rate, the total convective precipitation, and the solar hour at the centroid (the Sun's hour angle plus 12 hours, which is 12 at local solar noon)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "25deaf45",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:16:30.275971Z",
+     "iopub.status.busy": "2026-10-24T21:16:30.275971Z",
+     "iopub.status.idle": "2026-10-24T21:16:31.863266Z",
+     "shell.execute_reply": "2026-10-24T21:16:31.862756Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n_cells</th>\n",
+       "      <th>area_km2</th>\n",
+       "      <th>diameter_km</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>min_lwtup</th>\n",
+       "      <th>max_preccon</th>\n",
+       "      <th>solar_hour</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>285.00</td>\n",
+       "      <td>285.00</td>\n",
+       "      <td>285.00</td>\n",
+       "      <td>285.00</td>\n",
+       "      <td>285.00</td>\n",
+       "      <td>285.00</td>\n",
+       "      <td>285.0</td>\n",
+       "      <td>285.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>66.66</td>\n",
+       "      <td>2631.64</td>\n",
+       "      <td>53.48</td>\n",
+       "      <td>-109.70</td>\n",
+       "      <td>34.99</td>\n",
+       "      <td>125.31</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>61.26</td>\n",
+       "      <td>2415.42</td>\n",
+       "      <td>22.18</td>\n",
+       "      <td>1.91</td>\n",
+       "      <td>2.10</td>\n",
+       "      <td>3.04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>15.00</td>\n",
+       "      <td>616.22</td>\n",
+       "      <td>28.01</td>\n",
+       "      <td>-113.09</td>\n",
+       "      <td>31.05</td>\n",
+       "      <td>115.96</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>27.00</td>\n",
+       "      <td>1058.41</td>\n",
+       "      <td>36.71</td>\n",
+       "      <td>-111.30</td>\n",
+       "      <td>33.13</td>\n",
+       "      <td>123.18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>44.00</td>\n",
+       "      <td>1722.97</td>\n",
+       "      <td>46.84</td>\n",
+       "      <td>-110.07</td>\n",
+       "      <td>35.26</td>\n",
+       "      <td>125.71</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>17.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>86.00</td>\n",
+       "      <td>3472.57</td>\n",
+       "      <td>66.49</td>\n",
+       "      <td>-108.12</td>\n",
+       "      <td>37.00</td>\n",
+       "      <td>127.62</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>20.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>352.00</td>\n",
+       "      <td>14112.45</td>\n",
+       "      <td>134.05</td>\n",
+       "      <td>-105.21</td>\n",
+       "      <td>37.95</td>\n",
+       "      <td>131.43</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>23.92</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       n_cells  area_km2  diameter_km     lon     lat  min_lwtup  max_preccon  \\\n",
+       "count   285.00    285.00       285.00  285.00  285.00     285.00        285.0   \n",
+       "mean     66.66   2631.64        53.48 -109.70   34.99     125.31          0.0   \n",
+       "std      61.26   2415.42        22.18    1.91    2.10       3.04          0.0   \n",
+       "min      15.00    616.22        28.01 -113.09   31.05     115.96          0.0   \n",
+       "25%      27.00   1058.41        36.71 -111.30   33.13     123.18          0.0   \n",
+       "50%      44.00   1722.97        46.84 -110.07   35.26     125.71          0.0   \n",
+       "75%      86.00   3472.57        66.49 -108.12   37.00     127.62          0.0   \n",
+       "max     352.00  14112.45       134.05 -105.21   37.95     131.43          0.0   \n",
+       "\n",
+       "       solar_hour  \n",
+       "count      285.00  \n",
+       "mean        15.68  \n",
+       "std          6.12  \n",
+       "min          0.20  \n",
+       "25%         13.13  \n",
+       "50%         17.02  \n",
+       "75%         20.37  \n",
+       "max         23.92  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import scipy.ndimage as ndi\n",
+    "from skyfield.api import wgs84\n",
+    "\n",
+    "from tatc.constants import EARTH_MEAN_RADIUS, de421, timescale\n",
+    "\n",
+    "THRESHOLD = 5.67037e-8 * 220**4  # W m-2, the upwelling longwave flux of a 220 K blackbody\n",
+    "EFFECTIVE_RESOLUTION_KM = 4 * 7  # km, several grid spacings (Gelaro et al., 2015); smaller features are not resolved\n",
+    "\n",
+    "\n",
+    "def find_features(nature):\n",
+    "    \"\"\"The features (contiguous cold cloud tops at least EFFECTIVE_RESOLUTION_KM in size) in each frame of the nature run.\"\"\"\n",
+    "    lon, lat = nature.lon.values, nature.lat.values\n",
+    "    lon2d, lat2d = np.meshgrid(lon, lat)\n",
+    "    cell_area = (EARTH_MEAN_RADIUS * np.radians(GRID_SIZE)) ** 2 * np.cos(np.radians(lat2d))  # m^2\n",
+    "\n",
+    "    records, polygons = [], []\n",
+    "    for frame in range(nature.sizes[\"time\"]):\n",
+    "        lwtup = nature[\"lwtup\"].isel(time=frame).values\n",
+    "        preccon = nature[\"preccon\"].isel(time=frame).values\n",
+    "        labels, n_labels = ndi.label(lwtup < THRESHOLD)  # contiguous cells colder than the threshold\n",
+    "        if n_labels == 0:\n",
+    "            continue\n",
+    "        ids = np.arange(1, n_labels + 1)\n",
+    "        area = ndi.sum(cell_area, labels, ids)\n",
+    "        n_cells = ndi.sum(np.ones_like(lwtup), labels, ids).astype(int)\n",
+    "        # polygons are the unions of the cells, which are centered on the grid points\n",
+    "        j, i = np.nonzero(labels)\n",
+    "        order = np.argsort(labels[j, i], kind=\"stable\")\n",
+    "        j, i = j[order], i[order]\n",
+    "        cells = shapely.box(\n",
+    "            lon[i] - GRID_SIZE / 2, lat[j] - GRID_SIZE / 2, lon[i] + GRID_SIZE / 2, lat[j] + GRID_SIZE / 2\n",
+    "        )\n",
+    "        for k, cluster in enumerate(np.split(cells, np.cumsum(n_cells)[:-1])):\n",
+    "            records.append(\n",
+    "                {\n",
+    "                    \"frame\": frame,\n",
+    "                    \"n_cells\": n_cells[k],\n",
+    "                    \"area_km2\": area[k] / 1e6,\n",
+    "                    \"lon\": ndi.sum(cell_area * lon2d, labels, ids[k]) / area[k],\n",
+    "                    \"lat\": ndi.sum(cell_area * lat2d, labels, ids[k]) / area[k],\n",
+    "                    \"min_lwtup\": ndi.minimum(lwtup, labels, ids[k]),\n",
+    "                    \"max_preccon\": ndi.maximum(preccon, labels, ids[k]),\n",
+    "                    \"tot_preccon\": ndi.sum(preccon * cell_area, labels, ids[k]),\n",
+    "                }\n",
+    "            )\n",
+    "            polygons.append(shapely.union_all(cluster))\n",
+    "\n",
+    "    features = gpd.GeoDataFrame(records, geometry=polygons, crs=\"EPSG:4326\")\n",
+    "    features[\"diameter_km\"] = np.sqrt(4 * features.area_km2 / np.pi)  # of a circle of equal area\n",
+    "    features = features[features.diameter_km >= EFFECTIVE_RESOLUTION_KM].reset_index(drop=True)\n",
+    "    features[\"time\"] = start + features.frame * frame_duration\n",
+    "    # solar hour at the feature (hour angle of the Sun plus 12 hours) at the middle of the frame, in the Nature Run's year\n",
+    "    solar_time = timescale.from_datetimes([nature_start + (frame + 0.5) * frame_duration for frame in features.frame])\n",
+    "    features[\"solar_hour\"] = (\n",
+    "        de421[\"earth\"] + wgs84.latlon(features.lat.values, features.lon.values)\n",
+    "    ).at(solar_time).observe(de421[\"sun\"]).apparent().hadec()[0].hours + 12\n",
+    "    return features\n",
+    "\n",
+    "\n",
+    "features = find_features(nature)\n",
+    "features[[\"time\", \"n_cells\", \"area_km2\", \"diameter_km\", \"lon\", \"lat\", \"min_lwtup\", \"max_preccon\", \"solar_hour\"]].describe().round(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d44303e3",
+   "metadata": {},
+   "source": [
+    "## Define the Sensors\n",
+    "\n",
+    "**Platforms.** The current constellation of sounders is Suomi NPP, NOAA-20, and NOAA-21, three satellites in a sun-synchronous orbit with an ascending node of about 13:30 local time. Each carries both the Advanced Technology Microwave Sounder (ATMS) and the Cross-track Infrared Sounder (CrIS).\n",
+    "\n",
+    "**Swaths.** Each instrument is a rectangular `PointedInstrument` whose cross-track field of view spans its scan positions, using the scan parameters fitted to NOAA-20 data in the validation notebooks ([ATMS](ValidateImagerATMS.ipynb) and [CrIS](ValidateImagerCrIS.ipynb)): the effective spacing between scan positions and the roll angle, and a scan perpendicular to the inertial velocity (`VelocityFrame.INERTIAL`) rather than to the ground track. The along-track field of view is tailored to the 30 s time step (`compute_along_track_field_of_view`), so that consecutive footprints tile the swath. NOAA-21 and Suomi NPP are assumed identical to NOAA-20.\n",
+    "\n",
+    "**Resolution.** Each sensor has a horizontal resolution (the diameter of its footprint at nadir) and a sample spacing (the distance between the centers of neighboring footprints at nadir). A sensor cannot resolve detail finer than either its footprint or its sample spacing, so its *resolution element* is the larger of the two. ATMS oversamples (its 2.2 degree beams overlap at a 1.11 degree spacing), so its footprint limits it. CrIS undersamples slightly (its 0.963 degree views leave gaps at a 1.09 degree pitch), so its sample spacing limits it. The future sensors are assumed to sample in proportion to their footprints, in the same ratio as their heritage counterparts, so a future infrared sounder has as many more views per scan as its finer footprint requires.\n",
+    "\n",
+    "| Sensor | Horizontal | Sampling | Basis |\n",
+    "| --- | --- | --- | --- |\n",
+    "| ATMS | 32 km | 16 km | 2.2 degree beam of the temperature sounding channels and 1.112 degree spacing of scan positions at about 830 km, confirmed by the [ATMS validation](ValidateImagerATMS.ipynb) |\n",
+    "| CrIS | 14 km | 16 km | 0.963 degree field of view and 1.092 degree pitch of the 3 x 3 array of views at about 830 km, confirmed by the [CrIS validation](ValidateImagerCrIS.ipynb) |\n",
+    "| Future MW | 10 km | 5 km | target of a future microwave sounder, sampled as ATMS |\n",
+    "| Future IR | 3 km | 3.4 km | target of a future infrared sounder, sampled as CrIS |\n",
+    "\n",
+    "To isolate the effect of resolution, the future sensors are assumed to fly on the same platforms with the same scans (swaths) as their heritage counterparts: the future microwave sounder shares the ATMS swath and the future infrared sounder shares the CrIS swath."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4c6a2204",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:16:31.865273Z",
+     "iopub.status.busy": "2026-10-24T21:16:31.865273Z",
+     "iopub.status.idle": "2026-10-24T21:16:31.892545Z",
+     "shell.execute_reply": "2026-10-24T21:16:31.892545Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>swath</th>\n",
+       "      <th>horizontal_km</th>\n",
+       "      <th>sampling_km</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sensor</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ATMS</th>\n",
+       "      <td>ATMS</td>\n",
+       "      <td>32</td>\n",
+       "      <td>16.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CrIS</th>\n",
+       "      <td>CrIS</td>\n",
+       "      <td>14</td>\n",
+       "      <td>15.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future MW</th>\n",
+       "      <td>ATMS</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future IR</th>\n",
+       "      <td>CrIS</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          swath  horizontal_km  sampling_km\n",
+       "sensor                                     \n",
+       "ATMS       ATMS             32         16.1\n",
+       "CrIS       CrIS             14         15.8\n",
+       "Future MW  ATMS             10          5.0\n",
+       "Future IR  CrIS              3          3.4"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from tatc.schemas import GeneralPerturbationsOrbit, PointedInstrument, Satellite\n",
+    "from tatc.utils import VelocityFrame, compute_along_track_field_of_view\n",
+    "\n",
+    "# element sets (epoch 2026-07-16 00:00 UTC) from the JPSS TLE-AUX archive, as in the validation notebooks\n",
+    "tles = {\n",
+    "    \"Suomi NPP\": [\n",
+    "        \"1 37849U 11061A   26197.00000000  .00000095  00000-0  44942-4 0  2449\",\n",
+    "        \"2 37849  98.7951 137.6822 0001041 201.8158  13.6707 14.19522526762525\",\n",
+    "    ],\n",
+    "    \"NOAA-20\": [\n",
+    "        \"1 43013U 17073A   26197.00000000  .00000069  00000-0  32863-4 0  1908\",\n",
+    "        \"2 43013  98.7768 136.2093 0001592 131.9807  15.3167 14.19516563448569\",\n",
+    "    ],\n",
+    "    \"NOAA-21\": [\n",
+    "        \"1 54234U 22150A   26197.00000000  .00000063  00000-0  30079-4 0  9591\",\n",
+    "        \"2 54234  98.7055 135.3072 0002920 108.6153 233.8084 14.19541053190632\",\n",
+    "    ],\n",
+    "}\n",
+    "orbits = {name: GeneralPerturbationsOrbit.from_tle(tle) for name, tle in tles.items()}\n",
+    "\n",
+    "# scan parameters fitted to NOAA-20 data in the ATMS and CrIS validation notebooks: the number of scan positions,\n",
+    "# their spacing (deg), and the roll angle (deg, positive to the left); both scan perpendicular to the inertial velocity\n",
+    "scans = {\n",
+    "    \"ATMS\": {\"positions\": 96, \"spacing\": 1.112, \"roll\": 0.04},\n",
+    "    \"CrIS\": {\"positions\": 30, \"spacing\": 3.334, \"roll\": -0.093},\n",
+    "}\n",
+    "TIME_STEP = 30  # s, of the ground track\n",
+    "\n",
+    "\n",
+    "def swath_instrument(name):\n",
+    "    \"\"\"A rectangular view spanning the scan positions, tailored to the time step (as validated for ATMS and CrIS).\"\"\"\n",
+    "    scan = scans[name]\n",
+    "    field_of_regard = (scan[\"positions\"] - 1) * scan[\"spacing\"]\n",
+    "    return PointedInstrument(\n",
+    "        name=name,\n",
+    "        field_of_regard=field_of_regard,\n",
+    "        cross_track_field_of_view=field_of_regard,\n",
+    "        along_track_field_of_view=compute_along_track_field_of_view(\n",
+    "            orbits[\"NOAA-20\"].get_mean_altitude(), TIME_STEP, orbits[\"NOAA-20\"].get_inclination()\n",
+    "        ),\n",
+    "        roll_angle=scan[\"roll\"],\n",
+    "        is_rectangular=True,\n",
+    "        velocity_frame=VelocityFrame.INERTIAL,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "satellites = [\n",
+    "    Satellite(name=name, orbit=orbit, instruments=[swath_instrument(\"ATMS\"), swath_instrument(\"CrIS\")])\n",
+    "    for name, orbit in orbits.items()\n",
+    "]\n",
+    "\n",
+    "REFERENCE_RANGE_KM = 830  # nominal slant range at nadir (altitude), at which the nominal resolutions apply\n",
+    "# angular spacing (deg) of the samples of each scan, fitted to NOAA-20 data in the validation notebooks: the ATMS scan\n",
+    "# positions and the pitch of the CrIS 3 x 3 array of views\n",
+    "SAMPLING_DEG = {\"ATMS\": scans[\"ATMS\"][\"spacing\"], \"CrIS\": 1.092}\n",
+    "# sensors: the scan (swath) they share, and their horizontal resolution at nadir (km)\n",
+    "sensors = pd.DataFrame(\n",
+    "    [\n",
+    "        {\"sensor\": \"ATMS\", \"swath\": \"ATMS\", \"horizontal_km\": 32},\n",
+    "        {\"sensor\": \"CrIS\", \"swath\": \"CrIS\", \"horizontal_km\": 14},\n",
+    "        {\"sensor\": \"Future MW\", \"swath\": \"ATMS\", \"horizontal_km\": 10},\n",
+    "        {\"sensor\": \"Future IR\", \"swath\": \"CrIS\", \"horizontal_km\": 3},\n",
+    "    ]\n",
+    ").set_index(\"sensor\")\n",
+    "# sample spacing at nadir (km): that of the heritage sensor, scaled for the future sensors in proportion to their footprint\n",
+    "heritage_km = sensors.horizontal_km[sensors.swath].values\n",
+    "sensors[\"sampling_km\"] = REFERENCE_RANGE_KM * np.radians(sensors.swath.map(SAMPLING_DEG)) * sensors.horizontal_km / heritage_km\n",
+    "sensors.round(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5409bdef",
+   "metadata": {},
+   "source": [
+    "## Compute the Ground Tracks\n",
+    "\n",
+    "`collect_ground_track` returns the footprint of each instrument at each time step, clipped to the study region (the `mask`), which are tabulated per frame and combined (`swaths`) to find the area each swath covers in each frame. Each satellite crosses the region on a few passes per day, so this is quick."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "01285f14",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:16:31.894549Z",
+     "iopub.status.busy": "2026-10-24T21:16:31.894549Z",
+     "iopub.status.idle": "2026-10-24T21:16:32.183675Z",
+     "shell.execute_reply": "2026-10-24T21:16:32.183675Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>footprints</th>\n",
+       "      <th>first</th>\n",
+       "      <th>last</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>satellite</th>\n",
+       "      <th>instrument</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">NOAA-20</th>\n",
+       "      <th>ATMS</th>\n",
+       "      <td>48</td>\n",
+       "      <td>2026-07-16 08:26:15+00:00</td>\n",
+       "      <td>2026-07-17 21:09:45+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CrIS</th>\n",
+       "      <td>46</td>\n",
+       "      <td>2026-07-16 08:26:15+00:00</td>\n",
+       "      <td>2026-07-17 21:09:45+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">NOAA-21</th>\n",
+       "      <th>ATMS</th>\n",
+       "      <td>25</td>\n",
+       "      <td>2026-07-16 09:12:15+00:00</td>\n",
+       "      <td>2026-07-17 20:15:15+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CrIS</th>\n",
+       "      <td>23</td>\n",
+       "      <td>2026-07-16 09:12:15+00:00</td>\n",
+       "      <td>2026-07-17 20:15:15+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Suomi NPP</th>\n",
+       "      <th>ATMS</th>\n",
+       "      <td>40</td>\n",
+       "      <td>2026-07-16 08:06:45+00:00</td>\n",
+       "      <td>2026-07-17 20:50:45+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CrIS</th>\n",
+       "      <td>33</td>\n",
+       "      <td>2026-07-16 08:07:15+00:00</td>\n",
+       "      <td>2026-07-17 20:50:45+00:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      footprints                     first  \\\n",
+       "satellite instrument                                         \n",
+       "NOAA-20   ATMS                48 2026-07-16 08:26:15+00:00   \n",
+       "          CrIS                46 2026-07-16 08:26:15+00:00   \n",
+       "NOAA-21   ATMS                25 2026-07-16 09:12:15+00:00   \n",
+       "          CrIS                23 2026-07-16 09:12:15+00:00   \n",
+       "Suomi NPP ATMS                40 2026-07-16 08:06:45+00:00   \n",
+       "          CrIS                33 2026-07-16 08:07:15+00:00   \n",
+       "\n",
+       "                                          last  \n",
+       "satellite instrument                            \n",
+       "NOAA-20   ATMS       2026-07-17 21:09:45+00:00  \n",
+       "          CrIS       2026-07-17 21:09:45+00:00  \n",
+       "NOAA-21   ATMS       2026-07-17 20:15:15+00:00  \n",
+       "          CrIS       2026-07-17 20:15:15+00:00  \n",
+       "Suomi NPP ATMS       2026-07-17 20:50:45+00:00  \n",
+       "          CrIS       2026-07-17 20:50:45+00:00  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from tatc.analysis import collect_ground_track\n",
+    "\n",
+    "\n",
+    "def collect_tracks(satellites, duration):\n",
+    "    \"\"\"The footprint of every instrument of the satellites at each time step over a duration from start, clipped to the\n",
+    "    study region, with its frame.\"\"\"\n",
+    "    times = [start + timedelta(seconds=TIME_STEP * (i + 0.5)) for i in range(int(duration.total_seconds() // TIME_STEP))]\n",
+    "    tracks = collect_ground_track(satellites, times, instrument_index=None, mask=domain)\n",
+    "    tracks = tracks[tracks.valid_obs].reset_index(drop=True)\n",
+    "    tracks[\"geometry\"] = shapely.force_2d(tracks.geometry.values)\n",
+    "    tracks[\"frame\"] = ((tracks.time - start) // frame_duration).astype(int)\n",
+    "    return tracks\n",
+    "\n",
+    "\n",
+    "tracks = collect_tracks(satellites, duration)\n",
+    "# the area swept by each swath (ATMS or CrIS) in each frame\n",
+    "swaths = tracks.groupby([\"frame\", \"instrument\"]).geometry.agg(shapely.union_all)\n",
+    "tracks.groupby([\"satellite\", \"instrument\"]).agg(footprints=(\"time\", \"size\"), first=(\"time\", \"min\"), last=(\"time\", \"max\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "597144ec",
+   "metadata": {},
+   "source": [
+    "## Compute Observability and Resolution\n",
+    "\n",
+    "A feature is **observed** by a swath if the swath footprint of some time step in its frame overlaps it. A **look** is one satellite's view of a feature in its frame. It covers the part of the feature inside that satellite's swath, which is only part of it if the feature straddles the swath edge, and the size of this viewed part (the diameter of a circle of the same area) is what the sensor must resolve.\n",
+    "\n",
+    "For each look, the satellite's slant range and zenith angle at the centroid of the viewed part follow from the satellite position at the time of the footprint that covers most of it (TAT-C's orbit propagation), and give the size of the sensor's resolution element there: the element's angular size (the larger of the footprint and the sample spacing at nadir) times the slant range along track, and longer by the secant of the zenith angle across track. The validation notebooks confirmed this geometry against the reported viewing geometry of real data. Two measures of the size of the element are offered (`ELEMENT_AXIS`):\n",
+    "\n",
+    "* `\"equivalent\"` (the default): the diameter of a circle of the same area, the geometric mean of the along-track and cross-track sizes; and\n",
+    "* `\"cross-track\"`: the cross-track size, a stricter measure, which is up to about 2.3 times the along-track size at the swath edges of ATMS and 1.9 times at those of CrIS.\n",
+    "\n",
+    "If a feature has several looks, the best (the one whose viewed part spans the most resolution elements) is used.\n",
+    "\n",
+    "A feature is **resolved** if its viewed part spans at least `RESOLUTION_FACTOR` resolution elements. The default of two is a heuristic (a feature must span at least a couple of independent samples to be distinguished from its surroundings), and the sensitivity to it and to the measure of the element is shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d40d4f90",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:16:32.186673Z",
+     "iopub.status.busy": "2026-10-24T21:16:32.186673Z",
+     "iopub.status.idle": "2026-10-24T21:16:32.302264Z",
+     "shell.execute_reply": "2026-10-24T21:16:32.302264Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observed</th>\n",
+       "      <th>resolved</th>\n",
+       "      <th>swath</th>\n",
+       "      <th>horizontal_km</th>\n",
+       "      <th>sampling_km</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sensor</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ATMS</th>\n",
+       "      <td>41</td>\n",
+       "      <td>2</td>\n",
+       "      <td>ATMS</td>\n",
+       "      <td>32</td>\n",
+       "      <td>16.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CrIS</th>\n",
+       "      <td>35</td>\n",
+       "      <td>18</td>\n",
+       "      <td>CrIS</td>\n",
+       "      <td>14</td>\n",
+       "      <td>15.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future MW</th>\n",
+       "      <td>41</td>\n",
+       "      <td>33</td>\n",
+       "      <td>ATMS</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future IR</th>\n",
+       "      <td>35</td>\n",
+       "      <td>35</td>\n",
+       "      <td>CrIS</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           observed  resolved swath  horizontal_km  sampling_km\n",
+       "sensor                                                         \n",
+       "ATMS             41         2  ATMS             32         16.1\n",
+       "CrIS             35        18  CrIS             14         15.8\n",
+       "Future MW        41        33  ATMS             10          5.0\n",
+       "Future IR        35        35  CrIS              3          3.4"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pyproj import Transformer\n",
+    "from skyfield.framelib import itrs\n",
+    "\n",
+    "RESOLUTION_FACTOR = 2  # a feature is resolved if its viewed part spans at least this many resolution elements\n",
+    "ELEMENT_AXIS = \"equivalent\"  # the size of a resolution element: \"equivalent\" (diameter of equal area) or \"cross-track\"\n",
+    "AXES = [\"equivalent\", \"cross-track\"]\n",
+    "\n",
+    "TO_ECEF = Transformer.from_crs(\"EPSG:4979\", \"EPSG:4978\", always_xy=True)\n",
+    "\n",
+    "\n",
+    "def up(lon, lat):\n",
+    "    \"\"\"Geodetic up unit vector, stacked on the last axis.\"\"\"\n",
+    "    lon, lat = np.radians(lon), np.radians(lat)\n",
+    "    return np.stack([np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1)\n",
+    "\n",
+    "\n",
+    "def element_size(slant_range, zenith, nadir_km, axis):\n",
+    "    \"\"\"Size (km) of a resolution element of size nadir_km at nadir: its angular size times the slant range (m) along\n",
+    "    track, and longer by the secant of the zenith angle (deg) cross track. The size is the diameter of a circle of the\n",
+    "    same area (axis=\"equivalent\", the geometric mean of the two) or the cross-track size (axis=\"cross-track\").\"\"\"\n",
+    "    along = slant_range / 1e3 * nadir_km / REFERENCE_RANGE_KM\n",
+    "    cross = along / np.cos(np.radians(zenith))\n",
+    "    return cross if axis == \"cross-track\" else np.sqrt(along * cross)\n",
+    "\n",
+    "\n",
+    "def collect_looks(tracks, features, orbits):\n",
+    "    \"\"\"The looks at the features from the tracks: the part of each feature inside a satellite's swath in its frame, at\n",
+    "    the time of the footprint that covers most of it, with the slant range and zenith angle at its centroid.\"\"\"\n",
+    "    # pieces: the parts of each feature inside each footprint (of a satellite's instrument, at a time) in the same frame\n",
+    "    pieces = gpd.sjoin(\n",
+    "        features[[\"frame\", \"geometry\"]],\n",
+    "        tracks[[\"frame\", \"satellite\", \"instrument\", \"time\", \"geometry\"]].rename(columns={\"frame\": \"frame_track\"}),\n",
+    "        predicate=\"intersects\",\n",
+    "    )\n",
+    "    pieces = pieces[pieces.frame == pieces.frame_track].rename_axis(\"feature\").reset_index()\n",
+    "    pieces[\"geometry\"] = shapely.intersection(pieces.geometry.values, tracks.geometry.values[pieces.index_right])\n",
+    "    pieces[\"piece_area\"] = shapely.area(pieces.geometry.values)\n",
+    "\n",
+    "    # looks: the part of each feature inside a satellite's swath, at the time of the footprint that covers most of it\n",
+    "    looks = (\n",
+    "        pieces.sort_values(\"piece_area\", ascending=False)\n",
+    "        .groupby([\"feature\", \"satellite\", \"instrument\"])\n",
+    "        .agg(time=(\"time\", \"first\"), geometry=(\"geometry\", shapely.union_all))\n",
+    "        .reset_index()\n",
+    "    )\n",
+    "    looks[\"viewed_fraction\"] = np.minimum(\n",
+    "        1, shapely.area(looks.geometry.values) / shapely.area(features.geometry.values[looks.feature])\n",
+    "    )\n",
+    "    looks[\"viewed_km\"] = np.sqrt(4 * looks.viewed_fraction * features.area_km2.values[looks.feature] / np.pi)\n",
+    "    centroids = shapely.centroid(looks.geometry.values)\n",
+    "    looks[\"lon\"], looks[\"lat\"] = shapely.get_x(centroids), shapely.get_y(centroids)\n",
+    "\n",
+    "    # the viewing geometry (slant range and zenith angle) from the satellite position at the time of the footprint\n",
+    "    looks[\"range_m\"], looks[\"zenith\"] = np.nan, np.nan\n",
+    "    for name, group in looks.groupby(\"satellite\"):\n",
+    "        sat_xyz = np.array(orbits[name].to_gp_orbit().get_orbit_track(list(group.time)).frame_xyz(itrs).m).T\n",
+    "        target = np.stack(TO_ECEF.transform(group.lon.values, group.lat.values, np.zeros(len(group))), axis=-1)\n",
+    "        line_of_sight = sat_xyz - target\n",
+    "        looks.loc[group.index, \"range_m\"] = np.linalg.norm(line_of_sight, axis=-1)\n",
+    "        looks.loc[group.index, \"zenith\"] = np.degrees(\n",
+    "            np.arccos(\n",
+    "                np.sum(line_of_sight * up(group.lon.values, group.lat.values), axis=-1)\n",
+    "                / np.linalg.norm(line_of_sight, axis=-1)\n",
+    "            )\n",
+    "        )\n",
+    "    return looks\n",
+    "\n",
+    "\n",
+    "def assess(looks, features):\n",
+    "    \"\"\"Whether each sensor observes and resolves each feature, from its best look.\"\"\"\n",
+    "    results = []\n",
+    "    for sensor, row in sensors.iterrows():\n",
+    "        sensor_looks = looks[looks.instrument == row.swath].copy()\n",
+    "        # the resolution element at nadir is the larger of the footprint and the sample spacing\n",
+    "        nadir_km = max(row.horizontal_km, row.sampling_km)\n",
+    "        for axis in AXES:\n",
+    "            element_km = element_size(sensor_looks.range_m, sensor_looks.zenith, nadir_km, axis)\n",
+    "            sensor_looks[f\"elements ({axis})\"] = sensor_looks.viewed_km / element_km\n",
+    "            if axis == ELEMENT_AXIS:\n",
+    "                sensor_looks[\"element_km\"] = element_km\n",
+    "        # the best look at each feature: the one whose viewed part spans the most resolution elements\n",
+    "        best = sensor_looks.loc[sensor_looks.groupby(\"feature\")[f\"elements ({ELEMENT_AXIS})\"].idxmax()].set_index(\"feature\")\n",
+    "        result = features[[\"frame\", \"diameter_km\", \"area_km2\", \"tot_preccon\", \"solar_hour\"]].copy()\n",
+    "        result[\"sensor\"] = sensor\n",
+    "        result[\"looks\"] = sensor_looks.groupby(\"feature\").size().reindex(result.index, fill_value=0)\n",
+    "        result[\"observed\"] = result.looks > 0\n",
+    "        result = result.join(best[[\"satellite\", \"zenith\", \"viewed_fraction\", \"viewed_km\", \"element_km\"]])\n",
+    "        for axis in AXES:\n",
+    "            result[f\"elements ({axis})\"] = sensor_looks.groupby(\"feature\")[f\"elements ({axis})\"].max()\n",
+    "        result[\"resolved\"] = result[f\"elements ({ELEMENT_AXIS})\"] >= RESOLUTION_FACTOR\n",
+    "        results.append(result)\n",
+    "    return pd.concat(results).reset_index().rename(columns={\"index\": \"feature\"})\n",
+    "\n",
+    "\n",
+    "looks = collect_looks(tracks, features, orbits)\n",
+    "results = assess(looks, features)\n",
+    "results.groupby(\"sensor\")[[\"observed\", \"resolved\"]].sum().join(sensors).loc[sensors.index].round(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3bd55df",
+   "metadata": {},
+   "source": [
+    "## Generate a Visualization\n",
+    "\n",
+    "The figure shows one frame, the one with the most features whose status differs between the sensors. Features are colored by whether each sensor resolves them (blue), observes but does not resolve them (orange), or does not observe them (faded, outside the swath). The underlying image is the nature run's upwelling longwave flux, whose dark areas are cold cloud tops."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "aa237b59",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:16:32.304268Z",
+     "iopub.status.busy": "2026-10-24T21:16:32.304268Z",
+     "iopub.status.idle": "2026-10-24T21:16:35.105621Z",
+     "shell.execute_reply": "2026-10-24T21:16:35.105621Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "\n",
+    "# the frame with the most features whose status (not observed, observed, or resolved) differs between the sensors\n",
+    "status = results.observed.astype(int) + results.resolved.astype(int)\n",
+    "example = (status.groupby([results.frame, results.feature]).nunique() > 1).groupby(\"frame\").sum().idxmax()\n",
+    "example_time = start + example * frame_duration\n",
+    "STATUS = {\"not observed\": \"#d9d8d3\", \"observed, not resolved\": \"#eb6834\", \"resolved\": \"#2a78d6\"}\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(12, 8), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
+    "for ax, sensor in zip(axes.ravel(), sensors.index):\n",
+    "    background = nature[\"lwtup\"].isel(time=example)\n",
+    "    ax.pcolormesh(background.lon, background.lat, background, cmap=\"Greys_r\", vmin=100, vmax=450, shading=\"auto\", transform=ccrs.PlateCarree())\n",
+    "    members = results[(results.sensor == sensor) & (results.frame == example)]\n",
+    "    status = np.where(members.resolved, \"resolved\", np.where(members.observed, \"observed, not resolved\", \"not observed\"))\n",
+    "    for label, color in STATUS.items():\n",
+    "        selected = features.loc[members.feature[status == label]]\n",
+    "        if len(selected):\n",
+    "            selected.plot(ax=ax, facecolor=color, edgecolor=\"black\", linewidth=0.4, transform=ccrs.PlateCarree())\n",
+    "    # fade the part of the study region outside the swath\n",
+    "    swath = swaths.get((example, sensors.loc[sensor, \"swath\"]))\n",
+    "    outside = domain if swath is None else domain.difference(swath)\n",
+    "    ax.add_geometries([outside], crs=ccrs.PlateCarree(), facecolor=\"white\", edgecolor=\"none\", alpha=0.6)\n",
+    "    ax.add_feature(cfeature.STATES, edgecolor=\"white\", linewidth=0.5)\n",
+    "    ax.set_extent([domain.bounds[0], domain.bounds[2], domain.bounds[1], domain.bounds[3]])\n",
+    "    ax.set_title(f\"{sensor} ({sensors.loc[sensor, 'horizontal_km']} km)\", fontsize=10)\n",
+    "for label, color in STATUS.items():\n",
+    "    axes[0, 0].plot([], [], color=color, lw=6, label=label)\n",
+    "axes[0, 0].plot([], [], color=\"#ffffff\", lw=6, alpha=0.6, label=\"outside swath\")\n",
+    "axes[0, 0].legend(loc=\"lower left\", fontsize=8, frameon=True)\n",
+    "fig.suptitle(f\"Cold cloud top features, {example_time:%Y-%m-%d %H:%M}-{example_time + frame_duration:%H:%M} UTC (Nature Run {nature_start:%Y})\")\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd198458",
+   "metadata": {},
+   "source": [
+    "## Generate Statistics\n",
+    "\n",
+    "The table gives, for each sensor, the share of all features that are observed and resolved, the share of the total feature area and of the total convective precipitation that lies in resolved features, and the median resolution element at the observed features. The shares of area and precipitation count the whole of each resolved feature, even if only part of it is viewed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fd2fc711",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:16:35.109625Z",
+     "iopub.status.busy": "2026-10-24T21:16:35.108625Z",
+     "iopub.status.idle": "2026-10-24T21:16:35.121050Z",
+     "shell.execute_reply": "2026-10-24T21:16:35.121050Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observed (% of features)</th>\n",
+       "      <th>resolved (% of features)</th>\n",
+       "      <th>resolved (% of area)</th>\n",
+       "      <th>resolved (% of precipitation)</th>\n",
+       "      <th>median resolution element of observed (km)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sensor</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ATMS</th>\n",
+       "      <td>14.4</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>49.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CrIS</th>\n",
+       "      <td>12.3</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>14.8</td>\n",
+       "      <td>21.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future MW</th>\n",
+       "      <td>14.4</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>23.1</td>\n",
+       "      <td>15.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future IR</th>\n",
+       "      <td>12.3</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>10.8</td>\n",
+       "      <td>22.3</td>\n",
+       "      <td>4.7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           observed (% of features)  resolved (% of features)  \\\n",
+       "sensor                                                          \n",
+       "ATMS                           14.4                       0.7   \n",
+       "CrIS                           12.3                       6.3   \n",
+       "Future MW                      14.4                      11.6   \n",
+       "Future IR                      12.3                      12.3   \n",
+       "\n",
+       "           resolved (% of area)  resolved (% of precipitation)  \\\n",
+       "sensor                                                           \n",
+       "ATMS                        1.5                            2.8   \n",
+       "CrIS                        7.0                           14.8   \n",
+       "Future MW                  11.7                           23.1   \n",
+       "Future IR                  10.8                           22.3   \n",
+       "\n",
+       "           median resolution element of observed (km)  \n",
+       "sensor                                                 \n",
+       "ATMS                                             49.5  \n",
+       "CrIS                                             21.8  \n",
+       "Future MW                                        15.5  \n",
+       "Future IR                                         4.7  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total = {\"features\": len(features), \"area\": features.area_km2.sum(), \"precipitation\": features.tot_preccon.sum()}\n",
+    "summary = results.groupby(\"sensor\").apply(\n",
+    "    lambda r: pd.Series(\n",
+    "        {\n",
+    "            \"observed (% of features)\": 100 * r.observed.mean(),\n",
+    "            \"resolved (% of features)\": 100 * r.resolved.mean(),\n",
+    "            \"resolved (% of area)\": 100 * r.area_km2[r.resolved].sum() / total[\"area\"],\n",
+    "            \"resolved (% of precipitation)\": 100 * r.tot_preccon[r.resolved].sum() / total[\"precipitation\"],\n",
+    "            \"median resolution element of observed (km)\": r.element_km[r.observed].median(),\n",
+    "        }\n",
+    "    ),\n",
+    "    include_groups=False,\n",
+    ").loc[sensors.index]\n",
+    "summary.round(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b84770a",
+   "metadata": {},
+   "source": [
+    "Over the two days, the study region has 285 features at least 28 km in size (the median is 47 km, and three quarters are smaller than 66 km). The sounders observe 12 to 14% of them (35 in the CrIS swath and 41 in the wider ATMS swath), because the three satellites are all in the same orbit plane and cross the region within about an hour (near 13:30 local time) on their afternoon passes and again about 12 hours later (near 01:30), whereas the features concentrate in the afternoon and evening (see the solar hour below); finer resolution does not change this, because the swaths are the same. Only a few observed features straddle a swath edge (3 to 5 at their best look), so counting the viewed part rather than the centroid changes little here: requiring the centroid to be in the swath would observe one fewer feature in each swath.\n",
+    "\n",
+    "Among the features observed, resolution matters a great deal: with a criterion of two resolution elements, ATMS resolves almost none of them (2 of 41), because the median resolution element at the observed features (50 km, larger than its 32 km nadir footprint because features are observed away from nadir) exceeds the 47 km median feature. CrIS resolves half (18 of 35), with a median element of 22 km set by its sample spacing rather than its footprint. A future microwave sounder resolves four fifths (33 of 41), and a future infrared sounder, sampling in proportion to its 3 km footprint, all of them (35 of 35), with a median of about 10 elements across each.\n",
+    "\n",
+    "Because the larger features (which are resolved first) are also the wetter ones, the resolved share of convective precipitation is greater than the resolved share of features (15% for CrIS, 23% for a future microwave sounder, and 22% for a future infrared sounder). The future infrared sounder resolves a slightly smaller share of precipitation than the future microwave sounder because the CrIS swath is narrower than the ATMS swath, so it observes fewer features (35 vs. 41): once the resolution is fine enough, observability is limited by the swath. With a single two-day realization, differences of a point or two between the future sensors are within sampling noise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "985db512",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:16:35.123054Z",
+     "iopub.status.busy": "2026-10-24T21:16:35.123054Z",
+     "iopub.status.idle": "2026-10-24T21:16:35.272252Z",
+     "shell.execute_reply": "2026-10-24T21:16:35.271247Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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qpHp5eam+vr5q37591TVr1lQYX0V35P/rX/9SAbVu3bouXQ7OPUbv3r3VgIAA1Wg0qvXr11dvvvlm9a+//nLZ7qOPPlITEhJUg8GgNm7cWP3kk0/Uu++++4JHYkt88sknaqdOnVRfX1/V29tbbdiwoXrXXXepmzdvdm6zZ88etV+/fqq/v78aHBys3nLLLerx48crvIP+ueeeU2NiYlSNRqMC6rJly1RVVVWTyaQ+/fTTat26dVVvb2+1Z8+e6rZt2yrtTlDRv3d13q9Tp06pY8aMUZs2bar6+vqqfn5+aqtWrdQ33njDeY6c733asWOH2qtXL9Xb21sNCQlRH3zwQTUvL6/C58ydO1cF1G+//bbKfZdYt26deuONN6r169dXjUajGhoaqvbs2VNdsGCBy3Zl39ujR4+qQKU/Je9xybbjxo1TY2NjVb1er4aHh6tdu3ZVX3rppWrFJ4Q4P0VVz+kSLYQQQlxhSrpVHDt2zNn/VwhxdZNyAiGEEFckk8nE33//zcaNG/npp5+YOXOmJLBC/IPISKwQQogr0rFjx4iPjycgIIBRo0Yxe/ZstFrt5Q5LCHGJSBIrhBBCCCGuOJd1soOVK1cydOhQYmJiUBSFn3/+2WW9qqpMmTKFmJgYvL296dWrF7t373bZxmQy8cgjjxAWFoavry/Dhg3j5MmTl/BVCCGEEEKIS+2yJrH5+fm0bt2a2bNnV7h+xowZzJw5k9mzZ7Np0yaioqLo16+fy2wvEydO5KeffuLbb79l9erV5OXlMWTIEGw226V6GUIIIYQQ4hKrNeUEiqLw008/ccMNNwCOUdiYmBgmTpzIM888AzhGXSMjI3nttdcYP3482dnZhIeH88UXX3DrrbcCjll86taty2+//caAAQMu18sRQgghhBA16LKOxFbl6NGjpKWl0b9/f+cyo9FIz549Wbt2LQBbtmzBYrG4bBMTE0PLli2d21TEZDKRk5Pj/MnOzub06dPUknxeCCGEEEKcR61tsVUyR3ZkZKTL8sjISJKSkpzbGAyGcnOnR0ZGVjmX+SuvvFJuvmuAw4f24e/vf7GhV4vVakWnq7Vvv6hF5FwR7pDzRbhDzhdRXZf6XAmPiDnvNrX+zFUUxeWxqqrllp3rfNs899xzPPHEE87HOTk51K1bl7DwaAICAi4u4Go6nZ5SrX8gIeRcEe6Q80W4Q84XUV218VypteUEJXORnzuimp6e7hydjYqKwmw2k5mZWek2FTEajQQEBLj8CCGEEEKIK0etTWLj4+OJiopi8eLFzmVms5kVK1bQtWtXANq3b49er3fZJjU1lV27djm3EUIIIYQQV5/LWk6Ql5fHoUOHnI+PHj3Ktm3bCAkJoV69ekycOJHp06eTkJBAQkIC06dPx8fHh1GjRgEQGBjIPffcw5NPPkloaCghISFMmjSJxMRErrvuusv1soQQQgghRA27rEns5s2b6d27t/NxSZ3q3Xffzdy5c3n66acpLCxkwoQJZGZm0qlTJxYtWuRy89Ubb7yBTqdj5MiRFBYW0rdvX+bOnStTDwohhBBCXMVqTZ/YyyknJ4fAwECys7Plxi5R68i5Itwh54twh5wvorpq47lSa2tihRBCCCGEqIwksUIIIYQQ4opT6/vECiGEEEJcbja7ypZjBZzOsRIeoKN9nA9aTdV960XNkiRWCCGEEKIKi3fl8MrCU5zKtjqXRQbqeG5IJP1aeuZemtzcXKKjo7ntttt44IEHuPfeewE4e/YsOTk5xMXFATB69GjCw8MZO3Ysb7zxBhMnTnTuo2fPnqxcuZLc3Fz8/PxYuXIlzzzzDIWFhZjNZoKDg/nxxx+r7KV/JZEkVgghhBCiEot35fD4V8mcexd8eraVx79K5o3ReCSR/fbbb2nXrh0//PADs2bNYtu2bQDMnTuXhQsX8v333zu3nTt3Lu3atePTTz91JrGHDh2isLDQuY3VauXGG2/kr7/+om3btgDs378fX1/fi461tpCaWCGEEEL8I6mqSoHZXulPbpGN6b+cKpfAAqjFP6/8corcIlul+6huE6iPP/6YZ555hu7du/Pdd9+dd/v4+HhCQ0PZtGkTAJ988gljx451rs/NzXWO7pZo0qQJfn5+1YrnSiAjsUIIIYT4Ryq0qHScvP+i9nEqx0rnqQcqXb9pahN8DFXXzu7evZsTJ04wcOBArFYrM2bMYNy4cec99rhx4/jkk09o164d//vf/9i0aRMTJkwAIDg4mAkTJpCQkED37t3p0qULt956K40bN3bvBdZiMhIrhBBCCHEZffzxx9x1111otVoGDx7MkSNH2Lt373mfd9NNN/Hbb7/x008/cc011xAUFOSyftasWezatYuRI0dy4MAB2rZty+rVq2voVVx6MhIrhBBCiH8kb73CpqlNKl2/5WgBD8w9cd79zBlTl/bxPpUeoyoWi4Uvv/wSvV7PN998A0BBQQGffPIJr7/+epXP9fb2ZtCgQTz44IN8++23FW5Tv359xowZw5gxY/D19eW7776jW7du531NVwIZiRVCCCHEP5KiKPgYNJX+dE3wJTJQR2VpqAJEBeromuBb6T4Upeokdv78+TRo0IDk5GSOHTvGsWPHWLNmDZ9//jkWi+W8r+HJJ5/kmWeeoU+fPi7L8/Ly+P333501uYWFhezdu5eGDRtW5625IkgSK4QQQghRAa1G4bkhjnZU56aiJY+fHRJ5Uf1iP/74Y0aPHu2yrGXLlsTExPDLL7+c9/kJCQlMmjSpXLKsqipz5syhSZMmtG7dmvbt29O+fXseeuihC461tlHU6t42dxXLyckhMDCQ7OxsAgI80+/tfGrjHMSidpJzRbhDzhfhDjlfqqeiPrFRgTqe9WCf2NquNp4rUhMrhBBCCFGFfi0D6NPcX2bsqmUkiRVCCCGEOA+tRuGaBlfPRAFXA6mJFUIIIYQQVxxJYoUQQgghxBVHklghhBBCCHHFkSRWCCGEEEJccS7oxq4TJ05w7NgxCgoKCA8Pp0WLFhiNRk/HJoQQQghRK9hUOxvOHCPdlEuE0Z9OYXFoFRkLvJyqncQmJSUxZ84cvvnmG06cOEHZ9rIGg4Hu3btz//33c9NNN6HRyD+qEEIIIa4Ov6XsZvKOhaQW5TiXRXsFMLXVEK6PaeGRY1itVqZPn87XX3+NVqvFZrPRo0cPZsyYQVBQkMu2U6ZMIS8vj//7v//DYrHw5JNPsmTJEnQ6HRaLhXvvvZcnnnjCI3HVZtXKNh977DESExM5ePAg06ZNY/fu3WRnZ2M2m0lLS+O3336jW7du/Pvf/6ZVq1Zs2rSppuMWQgghhKhxv6XsZvzGr10SWIC0ohzGb/ya31J2e+Q499xzD5s2bWLdunXs3r2bPXv20K9fP86ePeuyndVqdXn81ltvkZaWxvbt29m+fTt///03AwYM8EhMtV21RmINBgOHDx8mPDy83LqIiAj69OlDnz59mDx5Mr/99htJSUl07NjR48EKIYQQQniKqqoU2iyVrrepdl7csZCKpjZVcUw9O3nHQrqHN6y0tMBbqy83Jey5Dh06xP/+9z+OHz9OcHAwABqNhltuuYXly5czYsQIevTowaZNm8qNsB4/fpyoqCh0OkdK5+XlRYsWnhkdru2qlcS+/vrr1d7h9ddff8HBCCGEEEJcKoU2C40XTr3g56tAalEOzX79T6XbHBgyGR+docr9/P333yQkJBAWFlbh+h07djB79mzeeustAHbvLh39vf/++xk4cCBLliyha9eu9O3bl1tuuQWtVuv+C7rCuF28WlhYSEFBgfNxUlISs2bN4s8///RoYEIIIYQQAho3bky3bt0qXNeiRQsOHz7M22+/Tf369Zk8eTLDhg27xBFeHm53Jxg+fDgjRozggQceICsri06dOqHX6zlz5gwzZ87kwQcfrIk4hRBCCCE8ylur58CQyZWu33DmGHeu/+y8+/mi8910Cour9Bjn065dOw4ePEhGRgahoaHl1vv5+VX5fIPB4CztvPfee4mOjubs2bOEhISc99hXMrdHYv/++2+6d+8OwPfff09kZCRJSUl8/vnnzmFuIYQQQojaTlEUfHSGSn96RDYi2iuAyipaFSDGO5AekY0q3cf56mEBGjVqxE033cQ999xDVlYW4KjX/fzzzzl8+HCVz125ciWpqanOx1u2bCEkJKRcR4OrkdsjsQUFBfj7+wOwaNEiRowYgUajoXPnziQlJXk8QCGEEEKIy0GraJjaagjjN36NAi43eJWkplMSB3ukX+wnn3zCSy+9RKdOndDpdKiqSo8ePRg4cGCVzzt+/DgTJ06kqKgIg8GAn58f8+fP/0e0O1XUsg1fq6FVq1bce++93HjjjbRs2ZI//viDLl26sGXLFgYPHkxaWlpNxVpjcnJyCAwMJDs7m4CAgEtyzNPpKYRHxFySY4krm5wrwh1yvgh3yPlSPRX1iY3xDmRK4mCP9Ymt7WrjueL2SOyLL77IqFGjePzxx+nbty9dunQBHKOybdu29XiAQgghhBCX0/UxLRgQ3Uxm7Kpl3E5ib775Zrp160ZqaiqtW7d2Lu/bty833nijR4MTQgghhKgNtIqGruENLncYogy3k1iAqKgooqKiAMel+KVLl9KkSROaNm3q0eCEEEIIIYSoiNvj4CNHjmT27NmAo2dshw4dGDlyJK1ateKHH37weIBCCCGEEEKcy+0kduXKlc4WWz/99BOqqpKVlcVbb73FSy+95PEAhRBCCCGEOJfbSWx2drazee4ff/zBTTfdhI+PD4MHD+bgwYMeD1AIIYQQQohzuZ3E1q1bl3Xr1pGfn88ff/xB//79AcjMzMTLy8vjAQohhBBCXG6q3Yb5wBZMm/7EfGALqt3msX3HxcXRtGlT2rRpQ5s2bXjggQeq3D4rK4sZM2Z47PhV6dWrFwaDgdOnzziXHTlyBI1Gw8033+zc5quvvnKunzJlCr6+vpjNZueyhg0bsnLlSo/G5vaNXRMnTmT06NH4+flRv359evXqBTjKDBITEz0anBBCCCHE5WbaupT87/6LPSvduUwTFIHvyCcxtu3jkWN8//33tGzZslrbliSxTz/9tNvHsVqt6HTupX+tWrXif99/z+QWrQDHxAzt27d3ru/duzfLli1j9OjRACxfvpzExEQ2btxIt27dOHnyJKmpqXTq1MnteKvi9kjshAkTWL9+PZ988gmrV692zgjRoEEDqYkVQgghxFXFtHUpuR8845LAAtiz0sn94BlMW5fWyHGXL19Ohw4dnI937dpFXFwcAA888ABZWVm0adPGuU2vXr1YuHChc/ubb76ZuXPnAjBmzBgeffRRBg4c6GyP+sUXX9CpUyfatWtHz5492bVrV6WxjB07lnnf/g8Au93OvHnzGDVqlHN97969Wb58OQAmk4mTJ09yzz33OJctW7aMrl27YjQaL+o9OdcFtdhq3769SwYOMHjwYI8EJIQQQghxKammwoqX223kf/d/VT43/7v/om96DYpGC4qCYriw0sqbb77ZWZY5efJkgoODK912zpw5dOjQgW3btlV7/6tXr2blypX4+fmxZs0avv32W1auXInRaGTVqlWMHj2a7du3V/jc+vXrExYexoYNG8jMzKRDhw4u8XXu3JmUlBROnDjB4cOH6dSpEz179uTBBx/khRdeYNmyZfTu3bvasVbXBSWxJ0+eZMGCBRw/ftyl3gFg5syZHglMCCGEEOJSyJjY44Kfa89K5+wTjgRNl9COoCfev6D9nFtOUDKK6SkjR47Ez88PgPnz57N9+3aXy/unT5/GbDZjMBgqfP6o22/j448/JjMzk/vvv5/k5GTnOoPBQNeuXVm+fDmHDx+mV69eNG7cmKSkJEwmE8uXL+fee+/16OuBC0hilyxZwrBhw4iPj2f//v20bNmSY8eOoaoq7dq183iAQgghhBD/NDqdDput9OaxoqKii9q+JIEFUFWVcePGMW3atGrHM3jwIF55dQZGo5G+ffvy+eefu6wvqYs9cuQIH3zwAQAdO3bkf//7H+np6XTs2LHax6out5PY5557jieffJJp06bh7+/PDz/8QEREBKNHj2bgwIEeD1AIIYQQoiaFzqr4rnnLwa3kvPPYeZ8f8NCb6BPagqJ4LKb4+HiOHj1KRkYGoaGhfPHFF6XHCwigoKDA5Sathg0bsmHDBoYPH87Ro0dZvXq1s3vAuYYOHcpdd93FfffdR926dbHb7fz9998uNbjn8vLy4o033sDHx8d5P1RZvXv35v3330en09G4cWPAUac7bdo0unXrhl6vv5i3o0Ju39i1d+9e7r77bsCR9RcWFuLn58e0adN47bXXPB6gEEIIIURNUozeFf7om3dCExRR5XM1wZHom3dyPOcC62ErEhsby6RJk+jQoQO9e/cmKCjIuS4kJITRo0eTmJjoTDyfeeYZFi9eTPv27Xn++eer7ATQo0cPpk+fzvDhw2ndujUtW7Zk3rx5541pxIgRlQ5YduzYkczMTJfj9uzZk4MHDzo7WXmaoqqq6s4ToqKiWLp0Kc2bN6dFixa88sorDBs2jO3bt3PttdeSl5dXI4HWpJycHAIDA8nOziYgIOCSHPN0egrhETGX5FjiyibninCHnC/CHXK+nF9Jd4LK+N//msfabNVmtfFccXsktnPnzqxZswZwdCR48sknefnllxk3bhydO3f2eIBCCCGEEJeLsW0f/O9/rdyIrCY48h+TwNZWbtfEzpw50znaOmXKFPLy8pg3bx6NGjXijTfe8HiAQgghhBCXk7FtHwyte2I5tA01+wxKYBj6Rm0cbbXEZeN2EtugQQPn3318fHj33Xc9GpAQQgghRG2jaLQYGrc//4biknG7nOBSslqtvPDCC8THx+Pt7U2DBg2YNm0adrvduY2qqkyZMoWYmBi8vb3p1asXu3fvvoxRCyGEEEKImlatkdjg4GCUaraNOHv27EUFVNZrr73GnDlz+Oyzz2jRogWbN29m7NixBAYG8thjjpYXM2bMYObMmcydO5fGjRvz0ksv0a9fP/bv34+/v7/HYhFCCCGEELVHtZLYWbNm1XAYFVu3bh3Dhw93TmkbFxfHN998w+bNmwHHKOysWbN4/vnnGTFiBACfffYZkZGRfP3114wfP/6yxC2EEEIIIWpWtZLYkr6wl1q3bt2YM2cOBw4coHHjxmzfvp3Vq1c7k+qjR4+SlpZG//79nc8xGo307NmTtWvXShIrhBBCCI9Q7Tas6auwF6ai8Y5GF9Fdbuy6zNy+seu3335Dq9UyYMAAl+WLFi3CZrMxaNAgjwX3zDPPkJ2dTdOmTdFqtdhsNl5++WVuv/12ANLS0gCIjIx0eV5kZCRJSUmV7tdkMmEymZyPc3JyPBazEEIIIa4u5qQfyd/0GGrBSecyxacOvh3fxFB/xEXvPy4uDi8vL7y8HJMldO7cmTlz5lS6fVZWFh988AFPP/30RR/7fHr16sWkSZPodE07pkyZwrvvvktMTAxms5lGjRrx4YcflsvDLhW3k9hnn32WV199tdxyu93Os88+69Ekdt68eXz55Zd8/fXXtGjRgm3btjFx4kRiYmJcRofPrddVVbXKGt5XXnmFqVOnllt+5nQqpqJLM1mD1WrhdHrKJTmWuLLJuSLcIeeLcIecL9WQ9huabfcDKmUzC3tBMrkrbsbe5gOIuv6iDmG32/jg/Xdp1qypc1lV/y7Hj5/gtddeZeyYO9w+VtmpaqvDYjGTnZWB1WohPz+Xm28ewdQpL2K323nggYd49tmnmfHaK27HcT7VmVjB7ST24MGDNG/evNzypk2bcujQIXd3V6WnnnqKZ599lttuuw2AxMREkpKSeOWVV7j77ruJiooCHCOy0dHRzuelp6dX+a3gueee44knnnA+zsnJoW7duoSFR8uMXaLWkXNFuEPOF+EOOV8cVEt+xcvtNrJXTkGl/OSmCiqgoDswlcAmIx2lBYoGReft9vE1Gi0hoREu/xbLly9n0qRJzvuAdu3axZAhQzh27Bh33jWO7Owc+vW/Hp1Ox+bNm50jpkOGDAHg5ptvZsiQIYwZM4YxY8YQEBDAgQMHOHHiBLt37+aLL75g9uzZWCwW/P39eeedd2jZsmW52PR6A4FBoeh0enx9/VFVxRln/wEDWbhw4WU7h9xOYgMDAzly5AhxcXEuyw8dOoSvr6+n4gKgoKAAjca1C5hWq3W22IqPjycqKorFixfTtm1bAMxmMytWrOC1116rdL9GoxGj0ejRWIUQQghxZcr8xu8Cn6miFpwka14gALrIngQMWH5Be7r55pud5QSTJ08mODi40m3nzJlDhw4d2LZtW7X3v3r1alauXImfnx9r1qzh22+/ZeXKlRiNRlatWsXo0aPZvn17tfdnMplYuHAht956a7Wf42luJ7HDhg1j4sSJ/PTTTzRs2BBwJLBPPvkkw4YN82hwQ4cO5eWXX6ZevXq0aNGCrVu3MnPmTMaNGwc4yggmTpzI9OnTSUhIICEhgenTp+Pj48OoUaM8GosQQgghRE35/vvvXUZCly9f7tH9jxw5Ej8/R7I+f/58tm/fTqdOnZzrT58+jdlsxmAwVLmfzz//nL/++ovDhw/TsmVLRo4c6dE43eF2Evv6668zcOBAmjZtSp06dQA4efIk3bt35//+7/88Gtzbb7/Nv//9byZMmEB6ejoxMTGMHz+eF1980bnN008/TWFhIRMmTCAzM5NOnTqxaNEi6RErhBBCiGoJvr3i+2Esp1aSt/T89a5+fX5DH9kDFM/NIaXT6bDZbM7HRUVFF7V9SQILjnuHxo0bx7Rp09yO66677uL//u//OHv2LP369WPy5MlVXv2uSW6/24GBgaxdu5Zff/2VCRMm8OSTT7JkyRKWLl1KUFCQR4Pz9/dn1qxZJCUlUVhYyOHDh3nppZdcviUoisKUKVNITU2lqKiIFStWVFjTIYQQQghREUXvW+GPPqY/ik8doLKbxRUUn7qO7fS+F1QPW5n4+HiOHj1KRkYGAF988YVzXUBAAAUFBVitVueyhg0bsmHDBsDRgnT16tWV7nvo0KF8/vnnnDhxAnDcnF9Se1tdISEhfPTRR8yePZvU1FS3nuspbo/EgiNx7N+/v0t/ViGEEEKIq4mi0eLb8U3yVtyMI5Ete4OXI7H17TirRvrFxsbGMmnSJDp06EBcXBw9evRwrgsJCWH06NEkJibi6+vL5s2beeaZZ7j11lv5888/adKkiUupwLl69OjB9OnTGT58ODabDYvFwuDBg+nQoYNbMbZt25aRI0cyffp03n777Qt+rRdKUVW1/C13/zA5OTkEBgaSnZ0t3QlErSPninCHnC/CHXK+VE/FfWLr4ttxlkf6xF4JauO5ckEjsUIIIYQQ/xSG+iPQ1x0uM3bVMpLECiGEEEKch6LRoo/qdbnDEGV47jY6IYQQQgghLpELSmIPHz7MCy+8wO233056ejoAf/zxB7t37/ZocEIIIYQQQlTE7SR2xYoVJCYmsmHDBn788Ufy8hy91Xbs2MHkyZM9HqAQQgghhBDncjuJffbZZ3nppZdYvHixS7/W3r17s27dOo8GJ4QQQgghREXcTmJ37tzJjTfeWG55eHi4syGvEEIIIYQQNcntJDYoKKjCmRm2bt1KbGysR4ISQgghhBCiKm4nsaNGjeKZZ54hLS0NRVGw2+2sWbOGSZMmcdddd9VEjEIIIYQQQrhwO4l9+eWXqVevHrGxseTl5dG8eXN69OhB165deeGFF2oiRiGEEEIIIVy4PdmBXq/nq6++Ytq0aWzduhW73U7btm1JSEioifiEEEIIIYQox+0kduXKlTRt2pSGDRvSsGFD53KLxcK6devo0aOHRwMUQgghhBDiXG6XE/Tq1YvWrVuXa6d19uxZevfu7bHAhBBCCCGEqMwFzdh122230bdvX+bOneuyXFVVT8QkhBBCCCFEldxOYhVF4bnnnuPLL7/kkUce4YknnnAmr4qieDxAIYQQQgghzuV2EluSsI4YMYKVK1fy/fffM2jQILKysjwdmxBCCCGEEBW6oHKCEm3btmXjxo1kZWXRt29fT8UkhBBCCCFEldxOYu+++268vb2dj6OiolixYgV9+/alXr16Hg1OCCGEEEKIirjdYuvTTz8tt8xoNPLZZ595JCAhhBBCCCHOp1pJ7I4dO2jZsiUajYYdO3ZUuW2rVq08EpgQQgghhBCVqVYS26ZNG9LS0oiIiKBNmzYoiuLSTqvksaIo2Gy2GgtWCCGEEEIIqGYSe/ToUcLDw51/F0IIIYQQ4nKqVhJbv379Cv8uhBBCCCHE5eB2d4LPPvuMX3/91fn46aefJigoiK5du5KUlOTR4IQQQgghhKiI20ns9OnTnS221q1bx+zZs5kxYwZhYWE8/vjjHg9QCCGEEEKIc7ndYuvEiRM0atQIgJ9//pmbb76Z+++/n2uvvZZevXp5Oj4hhBBCCCHKcXsk1s/Pj4yMDAAWLVrEddddB4CXlxeFhYWejU4IIYQQQogKuD0S269fP+69917atm3LgQMHGDx4MAC7d+8mLi7O0/EJIYQQQghRjtsjse+88w5dunTh9OnT/PDDD4SGhgKwZcsWbr/9do8HKIQQQgghxLncHokNCgpi9uzZ5ZZPnTrVIwEJIYQQQghxPm6PxAohhBBCCHG5SRIrhBBCCCGuOJLECiGEEEKIK061ktgFCxZgsVhqOhYhhBBCCCGqpVpJ7I033khWVhYAWq2W9PT0moxJCCGEEEKIKlUriQ0PD2f9+vUAqKqKoig1GpQQQgghhBBVqVaLrQceeIDhw4ejKAqKohAVFVXptjabzWPBCSGEEEIIUZFqJbFTpkzhtttu49ChQwwbNoxPP/2UoKCgGg5NCCGEEEKIilV7soOmTZvStGlTJk+ezC233IKPj09NxiWEEEIIIUSl3J6xa/LkyQCcPn2a/fv3oygKjRs3Jjw83OPBCSGEEEIIURG3+8QWFBQwbtw4YmJi6NGjB927dycmJoZ77rmHgoKCmohRCCGEEEIIF24nsY8//jgrVqxgwYIFZGVlkZWVxfz581mxYgVPPvlkTcQohBBCCCGEC7fLCX744Qe+//57evXq5Vx2/fXX4+3tzciRI3nvvfc8GZ8QQgghhBDlXFA5QWRkZLnlERERUk4ghBBCCCEuCbeT2C5dujB58mSKioqcywoLC5k6dSpdunTxaHAAycnJ3HHHHYSGhuLj40ObNm3YsmWLc72qqkyZMoWYmBi8vb3p1asXu3fv9ngcQgghhBCi9nC7nODNN99k4MCB1KlTh9atW6MoCtu2bcPLy4s///zTo8FlZmZy7bXX0rt3b37//XciIiI4fPiwS4/aGTNmMHPmTObOnUvjxo156aWX6NevH/v378ff39+j8QghhBBCiNrB7SS2ZcuWHDx4kC+//JJ9+/ahqiq33XYbo0ePxtvb26PBvfbaa9StW5dPP/3UuSwuLs75d1VVmTVrFs8//zwjRowA4LPPPiMyMpKvv/6a8ePHezQeIYQQQghRO7idxAJ4e3tz3333eTqWchYsWMCAAQO45ZZbWLFiBbGxsUyYMMF57KNHj5KWlkb//v2dzzEajfTs2ZO1a9dKEiuEEEIIcZVyuyb2Ujpy5AjvvfceCQkJ/PnnnzzwwAM8+uijfP755wCkpaUBlLvRLDIy0rmuIiaTiZycHJcfIYQQQghx5bigkdhLxW6306FDB6ZPnw5A27Zt2b17N++99x533XWXcztFUVyep6pquWVlvfLKK0ydOrXc8jOnUzEV5Xko+qpZrRZOp6dckmOJK5ucK8Idcr4Id8j5IqrrUp8r4REx592mViex0dHRNG/e3GVZs2bN+OGHHwCIiooCHCOy0dHRzm3S09MrbANW4rnnnuOJJ55wPs7JyaFu3bqEhUcTEBDgyZdQqdPpKdX6BxJCzhXhDjlfhDvkfBHVVRvPlVpdTnDttdeyf/9+l2UHDhygfv36AMTHxxMVFcXixYud681mMytWrKBr166V7tdoNBIQEODyI4QQQgghrhwXNRK7a9cuVqxYgc1mo2vXrnTo0MFTcQGOKW67du3K9OnTGTlyJBs3buSDDz7ggw8+ABxlBBMnTmT69OkkJCSQkJDA9OnT8fHxYdSoUR6NRQghhBBC1B4XPBL7zjvv0LdvX1asWMGyZcvo27cvL7/8sidjo2PHjvz000988803tGzZkv/85z/MmjWL0aNHO7d5+umnmThxIhMmTKBDhw4kJyezaNEi6RErhBBCCHEVU1RVVauz4cmTJ6lTp47zcbNmzVi1ahVhYWEArFu3jmHDhnH69OmaibQG5eTkEBgYSHZ2ttTEilpHzhXhDjlfhDvkfBHVVRvPlWqPxPbt25c333yTkpw3NDSUP//8E5PJRG5uLn/99Rfh4eE1FqgQQgghhBAlqp3Ebtq0iX379tGpUye2bt3KBx98wMyZM/H29iYoKIh58+bx2Wef1WSsQgghhBBCAG7c2BUQEMB7773HmjVrGDNmDNdddx2rVq3CZrNhs9kICgqqwTCFEEIIIYQo5faNXddeey2bN28mMDCQtm3bsnLlSklghRBCCCHEJVXtkVir1cqHH37Inj17aN26Nc8//zy33XYb48ePZ+7cubz99tvOyQeEEEIIIYSoSdUeib3vvvt4++238fX15dNPP+Xxxx+ncePGLFu2jAEDBtClSxfee++9moxVCCGEEEIIwI0k9ueff+aHH37g1Vdf5a+//uLXX391rrv33nvZsGEDq1atqpEghRBCCCGEKKvaSWxERASLFi3CbDazZMkSQkNDy63/+uuvPR6gEEIIIYQQ56p2Tezs2bO54447eOKJJ4iOjua7776rybiEEEIIIYSoVLWT2H79+pGWlsaZM2dkUgMhhBBCCHFZudViS1EUSWCFEEIIIcRlV60kduDAgaxdu/a82+Xm5vLaa6/xzjvvXHRgQgghhBBCVKZa5QS33HILI0eOxN/fn2HDhtGhQwdiYmLw8vIiMzOTPXv2sHr1an777TeGDBnC66+/XtNxCyGEEEKIf7BqJbH33HMPd955J99//z3z5s3jww8/JCsrC3CUGDRv3pwBAwawZcsWmjRpUpPxCiGEEEIIUf0buwwGA6NGjWLUqFEAZGdnU1hYSGhoKHq9vsYCFEIIIYQQ4lzVTmLPFRgYSGBgoCdjEUIIIYQQolrc6k4ghBBCCCFEbSBJrBBCCCGEuOJIEiuEEEIIIa44ksQKIYQQQogrjiSxQgghhBDiilOt7gTBwcEoilKtHZ49e/aiAhJCCCGEEOJ8qpXEzpo1y/n3jIwMXnrpJQYMGECXLl0AWLduHX/++Sf//ve/ayRIIYQQQgghylJUVVXdecJNN91E7969efjhh12Wz549m7/++ouff/7Zk/FdEjk5OQQGBpKdnU1AQMAlOebp9BTCI2IuybHElU3OFeEOOV+EO+R8EdVVG88Vt2ti//zzTwYOHFhu+YABA/jrr788EpQQQgghhBBVcTuJDQ0N5aeffiq3/OeffyY0NNQjQQkhhBBCCFEVt6ednTp1Kvfccw/Lly931sSuX7+eP/74g48++sjjAQohhBBCCHEut5PYMWPG0KxZM9566y1+/PFHVFWlefPmrFmzhk6dOtVEjEIIIYQQQrhwO4kF6NSpE1999ZWnYxFCCCGEEKJaLmiyg8OHD/PCCy8watQo0tPTAfjjjz/YvXu3R4MTQgghhBCiIm4nsStWrCAxMZENGzbwww8/kJeXB8COHTuYPHmyxwMUQgghhBDiXG4nsc8++ywvvfQSixcvxmAwOJf37t2bdevWeTQ4IYQQQgghKuJ2Ertz505uvPHGcsvDw8PJyMjwSFBCCCGEEEJUxe0kNigoiNTU1HLLt27dSmxsrEeCEkIIIYQQoipuJ7GjRo3imWeeIS0tDUVRsNvtrFmzhkmTJnHXXXfVRIxCCCGEEEK4cDuJffnll6lXrx6xsbHk5eXRvHlzevToQdeuXXnhhRdqIkYhhBBCCCFcuN0nVq/X89VXXzFt2jS2bt2K3W6nbdu2JCQk1ER8QgghhBBClON2ErtixQp69uxJw4YNadiwYU3EJIQQQgghRJXcLifo168f9erV49lnn2XXrl01EZMQQgghhBBVcjuJTUlJ4emnn2bVqlW0atWKVq1aMWPGDE6ePFkT8QkhhBBCCFGO20lsWFgYDz/8MGvWrOHw4cPceuutfP7558TFxdGnT5+aiFEIIYQQQggXbiexZcXHx/Pss8/y6quvkpiYyIoVKzwVlxBCCCGEEJW64CR2zZo1TJgwgejoaEaNGkWLFi1YuHChJ2MTQgghhBCiQm53J/jXv/7FN998Q0pKCtdddx2zZs3ihhtuwMfHpybiE0IIIYQQohy3k9jly5czadIkbr31VsLCwmoiJiGEEEIIIarkdhK7du3amohDCCGEEEKIaqtWErtgwYJq73DYsGEXHIwQQgghhBDVUa0k9oYbbqjWzhRFwWazXUw8VXrllVf417/+xWOPPcasWbMAUFWVqVOn8sEHH5CZmUmnTp145513aNGiRY3FIYQQQgghLq9qdSew2+3V+qnJBHbTpk188MEHtGrVymX5jBkzmDlzJrNnz2bTpk1ERUXRr18/cnNzaywWIYQQQghxeV1Un9hLJS8vj9GjR/Phhx8SHBzsXK6qKrNmzeL5559nxIgRtGzZks8++4yCggK+/vrryxixEEIIIYSoSReUxK5YsYKhQ4fSqFEjEhISGDZsGKtWrfJ0bE4PPfQQgwcP5rrrrnNZfvToUdLS0ujfv79zmdFopGfPnlXegGYymcjJyXH5EUIIIYQQVw63uxN8+eWXjB07lhEjRvDoo4+iqipr166lb9++zJ07l1GjRnk0wG+//Za///6bTZs2lVuXlpYGQGRkpMvyyMhIkpKSKt3nK6+8wtSpU8stP3M6FVNR3kVGXD1Wq4XT6SmX5FjiyibninCHnC/CHXK+iOq61OdKeETMebdxO4l9+eWXmTFjBo8//rhz2WOPPcbMmTP5z3/+49Ek9sSJEzz22GMsWrQILy+vSrdTFMXlsaqq5ZaV9dxzz/HEE084H+fk5FC3bl3CwqMJCAi4+MCr4XR6SrX+gYSQc0W4Q84X4Q45X0R11cZzxe1ygiNHjjB06NByy4cNG8bRo0c9ElSJLVu2kJ6eTvv27dHpdOh0OlasWMFbb72FTqdzjsCWjMiWSE9PLzc6W5bRaCQgIMDlRwghhBBCXDncTmLr1q3LkiVLyi1fsmQJdevW9UhQJfr27cvOnTvZtm2b86dDhw6MHj2abdu20aBBA6Kioli8eLHzOWazmRUrVtC1a1ePxiKEEEIIIWoPt8sJnnzySR599FG2bdtG165dURSF1atXM3fuXN58802PBufv70/Lli1dlvn6+hIaGupcPnHiRKZPn05CQgIJCQlMnz4dHx8fj9fmCiGEEEKI2sPtJPbBBx8kKiqK//73v3z33XcANGvWjHnz5jF8+HCPB3g+Tz/9NIWFhUyYMME52cGiRYvw9/e/5LEIIYQQQohLQ1FVVb3cQVxuOTk5BAYGkp2dLTd2iVpHzhXhDjlfhDvkfBHVVRvPFbdrYk+cOMHJkyedjzdu3MjEiRP54IMPPBqYEEIIIYQQlXE7iR01ahTLli0DHF0BrrvuOjZu3Mi//vUvpk2b5vEAhRBCCCGEOJfbSeyuXbu45pprAPjuu+9ITExk7dq1fP3118ydO9fT8QkhhBBCCFGO20msxWLBaDQC8NdffzFs2DAAmjZtSmpqqmejE0IIIYQQogJuJ7EtWrRgzpw5rFq1isWLFzNw4EAAUlJSCA0N9XiAQgghhBBCnMvtJPa1117j/fffp1evXtx+++20bt0agAULFjjLDIQQQgghhKhJbveJ7dWrF2fOnCEnJ4fg4GDn8vvvvx8fHx+PBieEEEIIIURF3B6JBVBVlS1btvD++++Tm5sLgMFgkCRWCCGEEEJcEm6PxCYlJTFw4ECOHz+OyWSiX79++Pv7M2PGDIqKipgzZ05NxCmEEEIIIYST2yOxjz32GB06dCAzMxNvb2/n8htvvJElS5Z4NDghhBBCCCEq4vZI7OrVq1mzZg0Gg8Flef369UlOTvZYYEIIIYQQQlTG7ZFYu92OzWYrt/zkyZP4+/t7JCghhBBCCCGq4nYS269fP2bNmuV8rCgKeXl5TJ48meuvv96TsQkhhBBCCFEht8sJZs6cSZ8+fWjevDlFRUWMGjWKgwcPEhYWxjfffFMTMQohhBBCCOHC7SQ2NjaWbdu28e2337Jlyxbsdjv33HMPo0ePdrnRSwghhBBCiJriVhJrsVho0qQJCxcuZOzYsYwdO7am4hJCCCGEEKJSbtXE6vV6TCYTiqLUVDxCCCGEEEKcl9s3dj3yyCO89tprWK3WmohHCCGEEEKI83K7JnbDhg0sWbKERYsWkZiYiK+vr8v6H3/80WPBCSGEEEIIURG3k9igoCBuuummmohFCCGEEEKIanE7if30009rIg4hhBBCCCGqze2aWCGEEEIIIS43SWKFEEIIIcQVR5JYIYQQQghxxZEkVgghhBBCXHE8ksRmZWV5YjdCCCGEEEJUi9tJ7Guvvca8efOcj0eOHEloaCixsbFs377do8EJIYQQQghREbeT2Pfff5+6desCsHjxYhYvXszvv//OoEGDeOqppzweoBBCCCGEEOdyu09samqqM4lduHAhI0eOpH///sTFxdGpUyePByiEEEIIIcS53B6JDQ4O5sSJEwD88ccfXHfddQCoqorNZvNsdEIIIYQQQlTA7ZHYESNGMGrUKBISEsjIyGDQoEEAbNu2jUaNGnk8QCGEEEIIIc7ldhL7xhtvEBcXx4kTJ5gxYwZ+fn6Ao8xgwoQJHg9QCCGEEJ6l2m1YDm2DEwcx101A36gNikZ7ucMSwi1uJ7F6vZ5JkyaVWz5x4kRPxCOEEEKIGmTaupT87/6LPSsdBcgBNEER+I58EmPbPpc7PCGq7YL6xH7xxRd069aNmJgYkpKSAJg1axbz58/3aHBCCCGE8BzT1qXkfvAM9qx0l+X2rHRyP3gG09allykyIdzndhL73nvv8cQTTzBo0CCysrKcN3MFBQUxa9YsT8cnhBBCCA9Q7Tbyv/tvldvk/28mql1u0hZXBreT2LfffpsPP/yQ559/Hq22tH6mQ4cO7Ny506PBCSGEEMIzLIe2lRuBPZc985SjVlaIK4DbNbFHjx6lbdu25ZYbjUby8/M9EpQQQgghPEMtKsC8ew0Ff31dve2zz9RwREJ4httJbHx8PNu2baN+/fouy3///XeaN2/uscCEEEIIcXFyP5+GafMisJiq/yQv35oLSAgPcjuJfeqpp3jooYcoKipCVVU2btzIN998wyuvvMJHH31UEzEKIYQQ4jzs+dlY9mzA0KEfiqIUL7SDxYQmvA6GNr0wrf8VNTezyv3kzp2Mrf9dePcaiWL0vgSRC3Fh3E5ix44di9Vq5emnn6agoIBRo0YRGxvLm2++yW233VYTMQohhBCiAvacs5i3L8e0dRmW/ZvAbiMoqj66uk0A8B5wF97XjUIbm4CiKOjjE8n94JlK96cJCseedZqCn2dTuORrfAbcjVf3ESgGr0v1koSoNreSWKvVyldffcXQoUO57777OHPmDHa7nYiIiJqKTwghhBBl2HMzMW1ZjHnrUiwHt4Jqd67TxjbCnp/jfKyLbuDyXGPbPnD/a84+sSU0wZH43vIEhtY9MW36k4KFH2A/k0z+929g2racoCc/qPkXJoSb3EpidTodDz74IHv37gUgLCysRoISQgghRCnVbkfROBoKWU8eIH/e6851unrNMLTrg7FtH7QR9c67L2PbPhha98RyaBvZJw4SeM6MXV6drsfYoT+mdQsp+P1jvLoOK43DZgVA0bp9IVcIj3P7LOzUqRNbt24td2OXEEIIITzHdvokpq1LMW9diq5ha/xufhwAfeP26Jt3Rt+sE8Y2vdGGxbq9b0WjxdC4PQRFY4iIKb9eq8Or2w0YO10PmtJunKa1CyhY/AU+g+/D2HGATFUrLiu3k9gJEybw5JNPcvLkSdq3b4+vr+tdjK1atfJYcEIIIcQ/iTXtGOatSzH9vQTbyQPO5fbcTNSbJqIoCopWR+Ajb1/UcVS7DWv6KpS0PVjszdFFdK8wIVX0htLnqCpFq3/GfvokeXMnU/jHXHyG3IehbV/nKLEQl5KiqqrqzhM0FZyoiqKgqiqKojhn8LqS5OTkEBgYSHZ2NgEBAZfkmKfTUwiv4NuvEOeSc0W4Q86XK1f2Ww9j2buhdIFGiz6hHYZ2fTG27okm0DMlfOakH8nf9BhqwUnnMsWnDr4d38RQf0SVz1WLCihc8R2Fi75ALXDU3mpjE/AZOh5Dqx6lXRHEVac2frZc0GQHQgghhLgwqqpiO7Ef885VeA8a5xwB1UbWx3JgC/omHTG264uhdU80fkEePbY56UfyVtwMuI5fqQXJ5K24Gb+e31eZyCpePvgMGINXj5spWvoNhX99hS35ILlzJuHV61b8bp3k0XiFqIrbSeylrIV95ZVX+PHHH9m3bx/e3t507dqV1157jSZNmji3UVWVqVOn8sEHH5CZmUmnTp145513aNGixSWLUwghhKiKardjPbbbUSqwdSn2jBQA9Ant0DduD4DPwLH4DBmPxrdmrgiqdhv5mx7j3AS2eC2gkL9pIvq6w89b66rx9sNn8H149RpJ4V9fUbRsHsZrBpbuzWaVm79EjXP7DPv888+rXH/XXXddcDDnWrFiBQ899BAdO3bEarXy/PPP079/f/bs2eOsxZ0xYwYzZ85k7ty5NG7cmJdeeol+/fqxf/9+/P39PRaLEEII4S5b+gkKl3+HedtS7JmlLa3QGzG0vBbKTCbgqXKByljSlruUEJSnohacwJq+Cn1Ur2rtU+MbiO/wCXj3vwuNt59zef5Pb2M7sR+foQ+gb9TmouIWojJu18QGBwe7PLZYLBQUFGAwGPDx8eHs2bMeDbCs06dPExERwYoVK+jRoweqqhITE8PEiRN55hlH82aTyURkZCSvvfYa48ePr9Z+pSZW1GZyrgh3yPlyeak2K2pRgXM01XJsN9mvjQFAMfqgT+yGsW1fDC26XNLZsAr+fo6ifbPBmnfebX27f40x/vYLPpZqKuTsc9ejFjqOpW/e2ZHMxskV0itZbfxscXskNjOz/HR1Bw8e5MEHH+Spp57ySFCVyc7OBiAkJARw1OempaXRv39/5zZGo5GePXuydu3aSpNYk8mEyVQ6j3ROTk6F2wkhhBDno9qsWPZvwvT3EszbV2Bo1RP/O18AQFe/OV69b0XfpCOG5p1R9MaajcWSh+XUciypf+HT7lUUrVdxjKZqJbAA1lOr0Ud0R+Nb54JiUIzeBL3wDYW/f0LR2gVY9qwne896DInd8Rk63jmbmBAXy+2R2Mps3ryZO+64g3379nlid+Woqsrw4cPJzMxk1apVAKxdu5Zrr72W5ORkYmJKvx3cf//9JCUl8eeff1a4rylTpjB16tRyyw8f2nfJShCsVgs6nf6SHEtc2eRcEe6Q8+USsVrg8DbYsxb2b0QpLE0Q1bBYePgduBR36qs2yNmJcmYFypmVkLUFRbUAYOvwDYT1cGyXfxhMp9FsfxhMaSgV1MWqKGAIQTFnoCpa1MhBqPXGQfA1F/5azqbBinmwfTlK8cxi6rCHoX2/C9ufuGwu9WdLdUZ9PVZ1rdVqSUlJ8dTuynn44YfZsWMHq1evLrfu3JYeJe2+KvPcc8/xxBNPOB/n5ORQt25dwsKjpZxA1Dpyrgh3yPlyaWS9ehfWpL3Ox0pACMbWvTG064M+od0luanJfGIB+WvGoJpdr5Bq/Buij+6HMbo5uuCSc8Hxp9mb4u4ECq43eCkogFeLJ7GmLMJ6ajlK2kJIW4g2uDVeTR/FEH87is7NEoiIGGjaDmvaMQp+/RDzjpWEdh3srP8tOxOZqN1q42eL2//LFixY4PJYVVVSU1OZPXs21157rccCK+uRRx5hwYIFrFy5kjp1Si9vREVFAZCWlkZ0dLRzeXp6OpGRkZXuz2g0YjTW7CUdIYQQVz57UT6WXWsw71iJ353/dpYD6Jt1xp6dgaFNb4zt+qBr2LrGZq9SzTmOEoGURehj+mOo65gGVuMXj2rORNEHoovuiz66H/qYfmj9G1a6L0P9Efj1/L6SPrGzHO21Ep/DmrkD077ZmI58iS1zO/nr7qHg76cJGLQebUAjt1+DLiqOgHtexp6bica/9N6a3DmTUPyD8bn+HrShtStBErWf20nsDTfc4PJYURTCw8Pp06cP//3vfz0VF+BIkB955BF++uknli9fTnx8vMv6+Ph4oqKiWLx4MW3btgXAbDazYsUKXnvtNY/GIoQQ4p/BXpiHecdKzFuXYt69DqxmAAwd+mNs5bg87zNoHD5DH6iRUUTVbsWWsRlL6mIsKYuwnl4PqtWxzpzlTGK1QS0JGLQObWgHFE31f50b6o9AX3c41vRVZKftITCq/IxduuBW6Lp8gHe7VzEd/BjT/ndAY0Dj38C5jS3vOBrfum5NcFA2gbWmHsW801EeaNrwG15dh+M9aCza4MoHoYQoy+0k1m6310QcFXrooYf4+uuvmT9/Pv7+/qSlpQEQGBiIt7c3iqIwceJEpk+fTkJCAgkJCUyfPh0fHx9GjRp1yeIUQghx5bOe2E/+gvccs2bZrM7lmvC6jtHWqNKBFMXgVSMxqJY8sn6oj2p27fSj8U9AH9MPQ93hpTEoCrrwzhd0HEWjRR/VC1XTGH0Vl4g1xhC8Wz6FV/MnsOcfR1EcSbtqLSTn13ZofGIxNn0EY/woFJ2PWzHoouMJfOoTCha+j2XvBopW/UDRul/w6j4CnwF313jLMXHlc/vGrmnTpjFp0iR8fFxP1sLCQl5//XVefPFFzwVXybe7Tz/9lDFjxgClkx28//77LpMdtGzZstrHkRZbojaTc0W4Q86X6rPnnEU1FaANd5SpWdOOkTX1FgC00fEY2vbF2LYP2thGHp9O1W7Oxpq2DEvKIlRbIX7Xfupcl72wHfa8o+iir0Mf3d9RIuAX59Hj21Q7G84c41D6cRpF1KNTWBxapfqjypb0teQuvg5shQAohhCMCfdibDIBrZ/7kyJZDv5N/oI5WA9tdSzQGwl87B30DVu7vS9RM2rjZ4vbSaxWqyU1NZWIiAiX5RkZGURERGCz2Twa4KUgSayozeRcEe6Q86VqtqzTmLctw7x1KZaDWzG07UPAfa841xcu/w59kw7oohtUsRf3qXYr1jMbsZaUCJzZ4OgsAKDRE3xrJoreMYmPvSAZxSuqxmpsf0vZzeQdC0ktKm0vGe0VwNRWQ7g+pvq9XO2mTEyHPsG0fzb2vGOOhYoGfZ1h+LSdjjaomVtxqaqKZd9GChbMwZ59muCpP6LoDc51nv4iIdxTGz9b3C4nqOxE2r59u7N/qxBCCFFb2DJSMW9bhunvJViP7HBZp+Zluvxe8+41skZiyFt1O5ak712WaQIaF4+09ocyNa0an9gaiQEcCez4jV+Xa7CVVpTD+I1f8/41o6qdyGqMwXi3eBKvZhOxJP9K0b63sab+heXEz9DulfM+/1yKomBo1gl902uwZ58uTWBtVrL/714Mid3x6nMbGi9ft/ctrk7VTmKDg4NRFAVFUWjcuLFLImuz2cjLy+OBBx6okSCFEEKIC5X74bNYk/Y4H+satMLYtg+Gtr09eke83ZyFNXUpltRFWFIW499/GVq/egDoI7pjTV2KPvo6dDH90Ef3u6DL7hfDptqZvGNhBR1iHc22FGDKzl8ZEN3MrdICRaPFUHcYhrrDsGXtwZK6GG1gU+f6/PUTUHS+GJs+VK2yCEVR0AaVXu01b1uG9dhurMd2U7j0G7z734V3z1su6YxnonaqdhI7a9YsVFVl3LhxTJ06lcDAQOc6g8FAXFwcXbp0qZEghRBCiPOxph3D/PcSzDtWEvDYO2i8/QAwtLsODF4Y2/XF0KaXS4J0MVS7BeuZjVhSFmFJWYQtYyOopTc/W1MXo024BwBj4/sxNnmoxkoEqmNRyl6XEoJzqUBKYTYbzhyja/iFlVNog5qjDWrufGwvSMF08ENQrRTtnYm+zlC8mj6CLqpPtcsDDG374j/uZQp+/QDbqSQKfnqbwiVf4TNgDF7dR9T4LGii9nK7JnbFihV07doVvf7qmRFGamJFbSbninDHP+l8UVUVW/IhTFuXYt66BFvqUec6v7HT8LpmkHM7T9RTqqoKqhVF4/j9Zz72P/JWupYfaAKbFfdr7Y8+sieK3u+ij3uh0otyWX/mKOvOHGV9xlEO5p6u1vNeaT2MO+M7eSQG1W7DkvI7RXvfxpq6yLlcG9QCY5OHMTa401kLfN592ayYNv5Bwa8fYs9wTK6kCYog6NnPpJPBJVAbP1suatrZwsJCLBaLy7JLlQR6kiSxojaTc0W4459yvliO7CD3s6nY04+XLtTq0De9xjHi2qoHGr+giz6O3XQWa9pSx2hr6mKMjcbh3erfznXZ85uhj+qDProfuph+aH3rXvQxL9SpohzWnznGujNHWHfmKIfzzlzQfjQodAtvyLA6iQyMbkGQwTOX7W1ZeynaPxvT4c/Amg+AT4eZeDV/3K39qDYrpnW/UPDbx2jCYgl8fI7c9HUJ1MbPFreT2IKCAp5++mm+++47MjIyyq2X7gTVUxtPBlE7ybki3HE1ni+q3Y712C5AQd8gEQBbVjqZzw0GnQFDiy4Y2vbBkNgdjY//RR7LhvX02uKkdRG2jM0uJQK6yJ4EDFheuv1lvGs+tTCb9WeOsT7DMdp65JykVUGhRWAUncPi6RIWT/uQegxa9g5pRTkV1sUC6BUtFtXm8rhnRCOG1mnFgKhm+Hng0r3dnI358FxMhz7Bv/9yNEbHBAiWtBVgt6CL7lut91S1mLHnZTonR7DnZZHz9qN49b0dY4f+l7V042pUGz9b3E5iH3roIZYtW8a0adO46667eOedd0hOTub999/n1VdfZfTo0TUVa42RJFbUZnKuCHdcLeeLardhPby9uFRgGfasdPTNOxP4yNvObcx71qFr0Oqi7lZXVRXVlIHGK6z4uBay5oWhWkprR7WBzdHFOLoI6CN6VPvyt6elFGaXlgecOcrRfNeBJAWFloHRdAmLp3NYPNeExpUbRS3pTgC4JLIlKeP714yiWUAUC5N3Mj95B/tyTjm3MWp09I1qwrDYRPpGNsFbZ/Do68v+rTO2MxvQBDbDq8nDGBve5VY5Rv4v71P420eAo8+vz5DxGNr0rpFZ1f6JauNni9tJbL169fj888/p1asXAQEB/P333zRq1IgvvviCb775ht9++62mYq0xlzKJVe22Kqf6E6KE3WqmaOunFKTtwieqJV5tx6Lx8C8NcfWpjb9o3GHevxnzlr8wbV+GmlM6a5Xi5YuhdS/87p580SOf9qIMLGlLsKYsxpK6CDR6gm485Fyfv/Y+VGu+I2mN6VejLa+qklKQxbqM0vKApPxzZvFCoWVQNF3CGtA5NI5rQuMIrMal/4r6xMZ4BzIlcXC59loHctJZkLyDBck7XUZ6fbQG+kU3ZXhsK3pGJGDUut2x04VqM1OwZRKmQ5+CNQ8ARR+IodFYvJo8hDag0Xn3YS/Kp2jZdxQu/gK1MBcAbZ0EfIY+gCGxu5QcXKTa+NnidhLr5+fH7t27qV+/PnXq1OHHH3/kmmuu4ejRoyQmJpKXl1dTsdaYS5XEmpN+JH/TY6gFJ53LFJ86+HZ8E0P9ETV2XHHlyV8+FdPhV0FfVLrQ4oWx4bP49pp8+QITtV5t/EVTFdVuc/kinz1rApb9mwBQvP0xtO7hKBVo1umi7kK3ntmI+cT84i4CW3AZh9QYCBpxDI1P9AXv3xOSC7JYV2akNamgfNKaGBRD57B4uobF0zE0jgD9hU1/6+6MXaqqsic7lQXJO1mQvJMTBZnOdQE6LwbGNGdobCLdwhuiv4iBGdWcg+nwZxTtext77sHipQpeif/Cp+1L1dqHvTCPwiVfU7Tka9QiR+2trmFrAp94XwaNLkJt/Gxx+6tTgwYNOHbsGPXr16d58+Z89913XHPNNfzyyy8EBQXVQIhXB3PSj+StuBnOqURSC5LJW3Ezfj2/l0RWAI4EtihpCuhKL/EBqLoix/LlSCIrrmiqxYR57wbMfy/FvGs1wf/+1nl3ubHLEDRhMRjb9UXfuAOKzv1OOKqqYs/ehyagkbOTgOnQXEwH3nNuow1qiT6mP7ro/ugju6PofCrbXY05WZDpkrQeL5MYAmgVjSNpDXXUtHYMrX/BSeu5tIqGruENSFC9CA8/f2KiKAotgmJoERTDs837sy3rJAtO7mRB8g5OFeXy3fG/+e743wQbfLg+pgXDYlvR2c2pbAEUQwBezR7B2PQhLCmLMO17C0vy7+hC2jm3Uc05oCgo+orrnzXefvgOuR/vXiMp/OtLCpfNQxfTUBLYq5DbI7FvvPEGWq2WRx99lGXLljF48GBsNhtWq5WZM2fy2GOP1VSsNaamR2JVu42sH+NcRmBdKSg+dQgacfSq/k9WcqqVvaSj2syAHVQVUItvoFAdjxWNSz2U3XQW7FbH+pJtSv6u6NB4Rzq3teWfALu54v1q9C6Xpmw5B1CtBa7bOPerRxfa1rmt9ex2VEu26zZqcfwaHfqoXqXbntmIvej0Ofst3lZRMNS70bmt5dQq7AUnUW0WClbei6q1UNGVL1UFxepF0N3ZUlogKlQbR0sAVHMR5l1rMW9dinnnKlRTgXOd3+h/4dXtxiqefX72ojNYUv/CkroYa8oi7AUn8R+wCn1kNwBHQnT4c0eJQPR1aHwu/Xt0Ir8kaXWUB5wszHJZr1U0tAqKLa5pjaNjSH38PZS0VuZizxe7amdTxnEWJO/g15RdnDHlO9dFGP0YHJvI8NhE2oXUReNmQlvClnMQjV88SvGsZoU7XqJw9wyMDcfi1fRhtAEJVcdYXJaiCXDMKmo9eZD879/AZ+h49A1bX1BM/0S18bPlolpsARw/fpzNmzfTsGFDWre+Mk+Gmk5iLWnLyV3U+7zbaYLboTEGYYwfhbG4QbYt/wT5q0Y5kya1JCEqTooMcbfi3eJJAOyF6eT+1e+cBEtFLU60DPVvwaftfwDHN9nshW1wSdpUe/G2KoZ6N+Db6R3HtjYzWd/HlNtvSTyGOoPx6/Gt83Wc/doXbOZy2wLooq8joN9i57aZ3wS63EBRli68CwGD1pZu+78Y1MLUCrfVBrcmcOg25+OsnxKw5x6qcFuNfyOCbjzofJz9S2tsmTsq3Fbxjib4lhTn45zfu2I9va7ibfWBBN+eVbrt4n5YU/+qcFsUHSF3OtrTWY/vI+fPAWA8XvG2FVALfVHskSjaMDReMWi866IJaIihSR/0cY67tyv60iCufrXxF415/2Zy3n0czKXlMZrgCAxt+mBs2wddw1YX9AXelnsU08EPsKQuxpbxN64lAkZ8O7+HsdFYD7wC96mqyvGCTOeNWOvOHCX5nKRVVyZp7RLWgA4h9Txy9787PHm+WO021p05yoLknfyWsptsS6FzXYx3IENjExkWm0iroNiL+lzKWXQd1rQlzsf62EEYmz6KPqY/SjUS5ZwPnsG8danjuc274DPsAfT1m5/nWaI2frZcVCV2UVER9erVo169ep6K56pkryTxKrdd5t/YAV3YNaULbUVY01dX/pzwMrOkqdZKkzHAJQFUsWPPO1r5tqYyl7UUBdVUvp1a6WELXRfYzKBaK9n43O9M7nyQKa5/V5TiZYrLvOMAis4XdH6OS04l2xRvrxgCXbf1ikDxjql42zKjuwAa3/poTGcd26gq2FWw21DtdjReIc7trEl7sR46CdogVKvV9XWroAmt4/KeqKct4O8POguKb5k62MreCe984AgqR7ABtkKgEEynNATHZqPo/bClHCbrvaFo/I0ohig0PnXR+NVHExiBxj8EfcNW6Oo0Pu+xhHCHvSAX846VKN5+GFv3BEBXJwGsFjShMcXTvfZBF9fCrbvGVVXFlr0HRaNHG+A4b1XTaYp2vercRhvcCn2042YsXUR3FN2lm5ZUVVWSCs6y7vRRZ8urlMJsl210ioY2wXWcLa86hNTDV3f1zDal02jpHtGI7hGNeLn1UFalH2JB8k7+TN1LSmE27x9azfuHVlPfN4Thsa0YGptI04BItxNa/36LsKb+RdG+t7Gc/BVL8u9Ykn9H45+AV4sn8Wo8vsrn+940EcXHH9O6hVj2rCN7zzoMrXviM2S841wVVwy3R2JtNhvTp09nzpw5nDp1igMHDtCgQQP+/e9/ExcXxz333FNTsdaY2jIS69XyebTBLRztXEIco9qqJQ9Lyp+US9oUDSgKGr94dEGOu0lVmwnrqRWl68s+R9Gg8Y4q/fC3Wx39D53baFy2VQzBznm9HfVle8/ZX+lzFJ0vGu8o5+uw5Z9wJIMV7BeNAU2ZJFI1F4/Cury2kvg1KNrSS+aOUWilxkYWVbsdNT8be04G9tyz2HPOoquTgC6mIeBITPO+fgV7bgb23Eywuk704XPjI/j0v8u5bdard7kewOCFxj8ETUAoXt1uwKvrMMBxE4Jlz3o0ASGYTvyJ+eS/zhurNugmtD6h2PKOoRYlY7ecAnsm6HwJGe34xWneu4Hcxf1RgkpHulUVMBvAZEAb3orAW5ajaPRYTx4ke9YYFN8INAHhaAJCy/yEoItv6XwfRO12OUZL7HlZmLevwLR1KZZ9G8FmRRefSNDTnzi3saUfRxNe163/v/bCdGeJgCVlEWphCsZG9+Lb9UPAUapVsOFBdJE9HCUCZT6HapqqqhzLP+ssDVifcYzUc5JWvaItTlrjnCOtPrWsDOhSnC9FNgvLTh1gQfJOFqfto8hW+tnZ2D/COULb0D/c7X3bcg9TtO8dzIc+RrXkYGg4Br9rP63ec0+fpODXjzBt/N3ZC9ir5y343fa023H8E9TGkVi3k9hp06bx2WefMW3aNO677z527dpFgwYN+O6773jjjTdYt67iS6212aWriU3m3Bu7HP4ZNbGXg2qzouZluSamdZugi3XUxFqT9pL75X+w55xFzcsCu+tkHT43PIzPgLsd257YT9b0O1zWK16+KAGhaPxD8Lp2OF5dhgCOVi+WfRudSavGPwTF6/w3jtitZrI+C0TVFbldE6uqdlTT2dJ+l1YLBZuew3p6HfbCE6imNKBM4q31J2S0I8E171lP7uIBEJQDZj2YjGAygMmIajJgbH8zfkNfR1EUR8L75kNoAkJckl0l0PGnrl4zdFFx532tomZcyl80Ret+wbTxDywHtrj839FGN8DY7jq8B9/r9pdO1W6jcOu/HCUCZ7e6rtR6YYi7Hb9rP6n4yTVIVVWO5mc4SwPWnTnCqaJcl230ipa2IXXoEuro09q+Fiat57rUiUm+1cRfaftZkLyDZacOYC5z3rQIjGZYbCuGxSZS1zfYrf2qljxMR75AF9ENXbCjpMp6dhuFfz/rKDWIHVhpqYE17RgFCz/AvGUxvjc/jnffURf+Aq9iV0US26hRI95//3369u2Lv78/27dvp0GDBuzbt48uXbqQmZl5/p3UMpeixVZpdwKoqMW0dCeoPtVidiSkuWdRcxx/6uo1c14GsiTtIe+zqY71+dmcW8Lgc8ND+AwYA1SSmPoGOhI0/1CMXYfi1el6x3GLCrAc3ILiTEyDL6rlT2Wc3QnAJZEteRle9adcUHcCVbWjFqVjzzuGLe8YWPOdtdeqxUT2wrbYc/dW/ORzE94FN4DeAiYDapmEF5MBn+GPlL6/yYfImf2YI8k9J+l1JLxN0YbXqfiY4oLU5C8ae04GmoBQ5+OcOU9h3r4cAG2dxo7pXtv2qfaXGFVVsWXtxp6zH0P9m5zLs+Y3w569z7Hf4DbF/Vr7o4u4FkVbszc6lY3tSN4ZR8Ka4egecG7SatBoaRtc19nyql1wXY9PAFDTLmdikmMpYlHqXuYn72BV+iGsZWZGaxtcl2GxiQyJbUm0d2AVe6lc3tp7MB9yfOHR+DfCq+nDGBqOcbkaWJY1+RDa8DooBsc5Ztq6FPOutfhcfw/a0Mvbfq02uCqSWG9vb/bt20f9+vVdktg9e/ZwzTXXSJ/YKlTcJ7Yuvh1n/eMTWNVU6DJaas89iz6uBbq6TQCwHNtN3qcvOhLTwvLnmM/wCfgMdNzAYT15gKyXy8wcp2hQ/IPQ+DsujRs7XV+amJoKsRza6kiy/EMcielFNu32hPzlUyk6/CpKmT6xqsULrxrsE6uqqkuSa88/hj0vCXv+MdB649/rB8d25iKyF7TEXnC4wv0oxjiCb3XUW5v3rCPni7sd39tKkl21dDTEZaQ7+RA5cya5lDE4R3kDQtHVaSy/SKrB079obBmpmLcuxbR1Cdajuwie8gPaiLqA49/XevIgxrZ9qv1lxF54qrg8YDGW1MWOWn2dD8G3nkXROr4Umo44ZpRylAhEeOy1VEVVVQ7nnSktDzhzlHST62eNQaOlXXA9Z3lAu5C6eGvdbwFWm9SWxCTTXMBvKbtZcHIH684cxV482KOgcE1ofYbFJjI4tiVhxurP4GXLPYJp/zuYDn7s6CoDoPPF2PBuvJo8jDaoWaXPVe12sl66DVvqUdDq8Lp2ON6DxqENujTnY21UW86Vstz+bd2iRQtWrVpF/fr1XZb/73//o23btpU8SwAY6o9AX3f4P2LGLkdClI+ak+FMSu05GejjE9HVd3xwWI7tJvfj57HnngVTYbl9+Ayb4ExiFZ0eW3qZu/e1Osclev8QR7ITUprcaMPrEvDo7NLL+H6Blb7HitEbQ4uuHnzlnrGi8c38p0BPj1PrqWvJ4YQ+gJXRnfl34+FcX0PHVBTHjWwa70h04Z0q387ghc81/4ct52Bxouv4seUfA2sBGp8g57b6Bq3Rtbefk/D6g90fzF7YWAk4klh7Vjr2jBPYzyRXeFyXmuPkQ+R+9JwzwdX4h6AJLB3h1cY0dM6nLtxnSz9ePN3rUqxJrqPzlsPbnUmsoXkXDM27VLSLcooOfoRp32xsmdtdV2i90Uf2RDWdQSmeGcvYoOYv56qqysHc086bsNafOcrpc5JWo0ZHu5C6jpZXofG0vQqS1toq2ODD6LiOjI7rSHpRLr+m7OKX5J1szEhiQ8YxNmQc4987FnJteAOGxbZiYExzgg1Vl2hp/Rvg0+G/eLeeiunIl5j2vY0tew+m/e9iPv4zQTcdr/x3g0aD3x0vULBgDpb9myha+QNFa3/Bq8dN+Ay42+WKhLh83E5iJ0+ezJ133klycjJ2u50ff/yR/fv38/nnn7Nw4cKaiPGqomi06KN6oWoao69l32jOR1VVx41PxaOljkv5GegatEIf57i5zHJsN7kfPuvoy2c1l9uHz7AJziRW0RlcExa9sbiGNASNfwjasNKpHrUR9Qh4fI5zveITUGmtnWL0xtCs8iSstiuZ21wFviqu7QJQzPmM3/g1718zqtzUkJeaod4N5ZY55qA/g2ouvblF8fJBF94eW7Y3tryjYM0HckGTC15gN5de+dA1aIWuTw6qOQM0YShqAFh9oFCPmq+gBJReNLJnnsKWdgxb2rEK4/O58VF8+t8JFCe8c18sV8rgHOGNinM22heOcpGctx8pXaBo0Ce0dcya1abXeUeiVFXFlrkDS+pijA3vRuPluFlHNWU4E1htSLvifq39iksEav4OfVVVOZCb7kxY12ccdelpCo6ktX1IveKWV/G0Ca6DlyStl1yElz9jG3RhbIMupBRk8UvyLhYk72B7VjKrTh9m1enD/Gv7AnpENGJobCIDoptV2U9X0fvh1eQBjI3HY01bRtG+t9CFdnQmsKrdiunABxgajEJjCHI+T9+gFYET38V8YAsFC+ZgPbyNoqXfULT6J0ftbPd/9hXU2sDtJHbo0KHMmzeP6dOnoygKL774Iu3ateOXX36hX79+NRHjVUW127Ac2gYnDmKum4C+UZvLOhKr2m3FNz6Vjpbac8+ib9gGfXxLoDgxnfOUY8T0nBufAHyGPehMYhW9EfvZNOc6xcvXeZleExCCNqL0kqM2oi6Bkz4qHlENdmxbWWJq8MLQuL0nX3qtZFPtTN6xsMLb/1QcFdRTdv7KgOhmbs+EU9MURUHxCgcv1zuM/XrOA0qS3LPO0VtbfhKKrnTGHcXLF7s5tTjRzSx9D7wcP+bTZ/AuHrXVxSdivLExmM1g80M1G6FAiz3HhpqT4/IFyJ55CtvJg9go7Q1cls+IR/HpV5rw5n3xH5cb1cr+aCPqofF374aT2kpVVWzJBzFtXYomMBzvHo6aVH2jNijefujiWjjaYbXu5WwSXxl7QaqjRKC4TEAtOgWAxjvGOapqqH8LGt/66KP7OhPbmmRX7RzIPe0sD9hw5hgZZtek1Uurp0NIPTqHOsoD2gTXwVgLyolEqRifIMYndGN8QjeO5WewMHkX80/uYG9OGktO7WfJqf0YNTp6RzZmeJ1WXBfZpNK6ZEVR0Ef3QR/dh7KVlJaTv1Cw8SEK/n4KY4O78Gr6CNqg0r6xhsbt0T/5AZa96x3JbNIeudJTS1S7JvbIkSPEx8dflY3TL1VNrGnrUvK/+y/2rHTnMk1QBL4jn8TYto/HjqNaLdjzMh0jpSV1prmZ6Bu1Qd+gFeBITHPeeRw1P6uC3q3gM3Q8PtffCzh+sWe9dLtzneIT4BgN9Q923PzU/jqM7fo6jm0xYT150DmaWlIgL6pn7ekjjFzz8Xm3+6rLGHpGXl39DFVVRTVnOssT7PlJjtrc4vpcbXAr/Lp94dw28xv/4oTXlWIMQx/TH7/uXwGO9k9Fu76EIlALdah5BdhzMopLXTLwGTIeY/vrADDvXO1oyl8Jn5sew+c6x42A1uRD5H3zmksZQ+kIbwjasDpofGvu8+RcJV+Qs08cJLCSL8iqqmI9vhfz30swbV2G/fQJALTR8QS/+F3pduaiav3ftWZsIX/NWGxZO11X6HzQR/bGq5mjAf2lYFft7M9JL53GNeMomeYCl228tHo6Fo+0dg6Lp3WQJK21sc6xOg7mpjunvT2cd8a53EdroF9UU4bVSaRXRONq/fuaT/5G4d/PYMva5Vymi+rrOH9jB7v8P1JVFcvBv9EntHPmQ4VLv0U1FeLd+9ZqdaG5UtXGc6XaSaxWqyU1NZWICMelpFtvvZW33nqLyMgr/9vIpUhiTVuXkvvBM5Wu97//tSoTWdVcVHrTU04Gam6mY8S0cXvntHmWo7vImf0YakHFM2C5JKYph8n6z22OFYqC4hvkHC3V+IdiaNsHYztHPKrFhC3liOPucv+QC5rLXJRnU+2cKMjkQE46B3LT2Z9zik0ZSeWmoqxMuNGPGO9AYn2CiPUOItY7kBif4j+9gwgzVj6yfaVT7VZHfWWZmlx7fhKqOQsAfZ1h+PeZ79hWVcn8JgCsjnpHxRiKxi8OjW8cWr84tKEdMcbfCjimp7Qc3elMcO0lNd3Ff/e94SGM7R1XnMw7V5Hz7hOVxuh700S8r3PcYGhNPkT+/2Y6vvwFhKIJCHNNfkOi0HhX/4aVc1XnC3LBrx9StPYX7GfLTL6iM2Bo0cXx/73jwEonH1BVu6NEIGURWr94DHG3ON6vglTHbH4oaEPbl5YIhHep8RIBu2pnX84p1paUB5w5SpbFtbbeW6unY0h9Z3lAq+BYDJp/dtJ6rtqYmLhDVVX25qQx/+QOfkneyfGC0g5J/jojA6ObM7ROIt3DG6Gv4qqnqqpYTy13TKBwYr6zb6zGL56A6zdUevXAXpBL5gvDUQtzUfyC8O5/F949b7kqB3Bq47lS7SRWo9GQlpbmTGLLdia40l2KPrGZzw9z+QVzLsU3EO8h4yEvC32T9ugT2gHFielbD6MWlR9xAvAZcj8+g+8DwJp6hKxpjl/GaLSOO+3L1Jga2vTC2MYx6YJqMWM7daz4xqegWnFH/tXKrto5UZDFgZxT7M9NL05aT3Eo74xL029PM2p0RHsHEluc6MZ4B7okvTHegbW+h6W77OYs7HlJoGicvSJVSz45f3bHnncM1Vy+BeC5CW/2j/EoxhCXRNfx9/qOP4vb89izz2A5tK1MsptRPMLrSHp9b3oMYwfHKKRpx0py33uy0rjL9qa0Jh8i/+fSGxM1AaGuCW9wpMsvyOp+Qc79bAqm9b+CwQtDy24Y2/ZG3/JaNF6+Fb+XBSnOSQYsqYtRi04D5aeONp/8DV1YJzReNXuji121szc7rcxI6zGXaU3BMQrXMbQeXcIa0CUsnsSgGElaK2Gzq2w5VsCRk6dpUCec9nE+aDVX9pdeVVXZlnWSX07uZEHyTtKKSgd0gg0+DIpuzvA6regcFl9lOZYtLwnT/ncxHfwIjX9DAgdvdK6zF55CU2YmR9Vuw7R5MQW/foi9+OZjJSAUnwFj8Op+Y420YbxcJImtpWo6iTUf2ELOGw9Ue3ufwffjM6QkMT1K1rSRjhU6femNTcXtogytejindlStFmynTzgSU58At6Z0FBfPrto5WZBVPKqazsFcR7J6MPc0hZUkq0aNjkb+4TT2j6BJQCQN/UL51/ZfOGPKq2RaDIj2DuTXng9yqiiX5MIskguySS7MIqUwm+QCx5+ninJRK9yDq2CDjyPJ9Q5yJLzFI7klSW+El3+tq729GHZzNvb8JEfrsJIyhcAWGBPGOdYXZZD1XeU3eenrDMW/zwLA8Quz8O9n0PjEovGNc0zp6xfncmNICVtWOpYDWxxJbnbGOYnvWfxGTsLYsTjh3b6C3DmTKo2hbMJrOXmQ7BljwWKqdHtNcCTBL83HduIAtsw0DM27VDlKpKoqOb91wpaxyXWFzg99VC/0dYacd1pPT7C5JK1H2JCRVC5p9dUZikdaG9A5LI5WQbFVjrYJh8W7cnhl4SlOZZdODx4ZqOO5IZH0a3npSmBqkl21s/nsceaf3MGvKbtcbuILN/oxOKYlw+u0on1IXTSVfMap1gLsBSloAxyT49jNWWR9Xwdd2DV4NX0EfZ1hpTeH2ayYNvxGwW8fY89IAUATHIHfXVMwNO1Yw6/20qiNSWy1v6IqSvnpPq/WS5WepmafOf9GgLZeM/T1mqKr37R0WXgdgqd8jxIQWuWNT+BoQ6WLvvK/VNR2dtVOSmE2+4vLAA7knOJAbjoHc09TYCvfkQEc/SUb+oXTOCCCJv4RNPaPpHFABPV9Q8oliSoK4zd+jUJF02LAlMTBhHv5E+7lT8ugij9QzHYrpwqLk9zCbFKKk9uSx8kFWeRZTWSaC8g0F7ArO7XC/egUDVFeAc6kNrZMuULJsoAq7gqubTSGQDSGVhDcqsL1iiGQgKE7XVuHOXvmHkPjF+fcVjVnUrT79fL70Aei8YvDUP8WvFs97zhuYDi6RtFofDujGIKq/H+sq9MYvztecCljKB3lzUATUJpkm3esLJPAqhCQ65iEwqKHHH9AwZ55CsuhbRgat3d2BoHiEoGz27CkLMKWc8A5C5aiKGiModhQ0IZ1RB/dH31MP3RhnV2mgfY0m2pnd3Yq64tHWjecOUaOtchlGz+dkY6hpeUBiYEx6CRpdcviXTk8/lVyua+46dlWHv8qmTdGc1UkshpFwzWhcVwTGsfUxMGszzjG/JM7+D1lN6dNecw9up65R9cT7R3IsNhEhsYm0joo1uX/pqLzcSawANZTK8FWiDVtGXlpy9D41sfYZALGhHvRGEPw6joM4zWDKFr3C4W/fYw9OwNtyJVfclmbuTUSO2jQIIxGx9D4L7/8Qp8+ffD1db0U9eOPP3o+yhpWW0ZiAx6f84+4A/9KoaqqI1nNPeVSt3oo7zT5FbQPA0ey2sAvjCbFSWrJCGs9n2C3ftn+lrKbyTsWklrmcliMdyBTEgd7rL1WjqWoeOS2JMEtHclNLsgitSgHW5kZdCrjrzM6a3Fji5NbZxmDdxBR3gFXzeiYarc5R17sRWco2j3DJdEtueQOYGzyML6d3nZsa8oka57jDn9FH+AsVSgZvdWFdUYfUb1+xaqqlrmh5Bvy/zcTQjJR4o6jGEtH/FWTHvVYPTgbjP+4lzB2HIAt/wTW4kkGLKl/oZpKv2AH3XwSTXGfVlvOQUftsLHqrgQXw2q3sTs71dnyamNGUoVJ6zWh9Z3lAS0DoyVpvQg2u0q/GYdcRmDLUnCMyC56utEVX1pQGbPdyqr0w/ySvJM/UveQZy29ilHfJ4RhdRIZGtuKZgGRFX7ZtOUdx3TgPUwHP0Q1ZTgWar0xxo/Gq/WLaH0dPZRVi8nx5bFMu8f8+e+hjY7H2KHfFdkfvjaOxFY7iR07dmy1dvjpp59eVECXQ22oiS255HclnthXOlVVSS3MLlOvml48spru8gFXll7R0sA/rHhUNYKE4mQ1zjfEY79kbaqdDWeOcSj9OI0i6tEpLO6SXtq3qXZOFeWSWpzUlia5xaO7hdnl7v6uiIJCpJe/cxQ32pnslia9QXrvq+LKjmrJd3ZV0PjEoAtpA4Atez85f/ZALar4M+DchDf3zx5lEt3iutzimlzFGOp8r8wHtpDz2a0ojR2TSVQ0TbF6oCEBd8/Dmvc9RbtedT2w3h99ZG/0Mf0xxI9CY6y59mFWu41dxUnrujNH2JSRRO45/7/8dUY6hcbRuXiktYUkrRfNbFU5dsbEwTQTK/fnsXBbxTf+ljVtRBRD2wZi0F09pUQVKbJZWH7qIAuSd7A4bZ9L2VeCfzhDYxMZFtuKRv7lb+pSrYWYj35D0b63sWVuA0VL0E1Jzi+C57KmHnHcTK2qaKMb4DNkPIY2va6osr8rOom9ml0J3QnExVNVlbSinOISgHTnCOvB3PRyv0xL6BQNDfzCaOwfUTyyGkmTgAjifEMv2ehibfzgKFFgNZeWKRScW7qQTUphFuYKegufy1urd5QreAcRU5LcFie6Md5BRHsHXBVN51VrAfb846Wtw4q7KujrDsMY72hjZ83YSs6v7Srdh7HZY/h2nAWArTCD7G+jUDVWKvoOoKqgWL0Iujsby4kfyV89Gm3oNWW6CHRC0dTM+2q129iRleKcEWtTRlK5L4UBOi86hcU5W161CIy+qmqwLyWbXeVEhpmDp0wcPGXiUPFP0hkz1vNfUClHq4H6oQYaRRpJiDTSsPjPeqEGdNor/wvnuQqsZv5K28f85B0sP3UQk710tLp5QBTD6rRiWGwi9Xxdr06oqoo1fQ22s1vwavaYc3n+2nvR+DdylBp4hWEvyqdo2TwKF3+JWpgLgLZOY3yHPYC+Zbcr4kt8bfxdJEksl7lPbHAkvrc8IQmsB6mqyqmiXGe96v6SkdWc9HKXK0voFA3xfqGOWlX/CJoUJ6zxfpcuWa1MbfzgqC67aifDVOBMclNLyhaKH6cUZpeb6rMyYUbf4q4KpaO4Md6BzqQ3zOhb6Q0aVxLVnIPl9DqXutyS0V21MBXv9q/j3cJx41fRgY8oWH/feffp338ZurBrUG2mGhtttdht7MhKdpYHbDqbVK7sJlDvRafiiQW6hMXTLDBKklY32e0qqdkWDpUkq2mOP4+cNmO2Vvzr3M+ooVGUkQAvDSv3V9zppixvvUKhpeJ96bUKDSIMJEQaaVT8kxBpJCZIj+YqKUHIsRSxKHUvC5J3sDL9ENYyZVVtguswLDaRIbGJxHgHVvh8W/Z+sucX39ui9cIQPwqvpo+gC2mDvSCXwiVfUbT0W2fXIV1cS/zHTkUbUa/GX9vFqI2/iySJ5dIlsVC9huSielRVJd2UWzyqWtwNoLh9Vbal4mRVq2iI9w111qs6RlgjaeAXWmtb8dTGDw5PKrJZSCvKIbnAMXLrHNEt03mhsu4OZRk02tJSheJ2YjE+ZUd0A/HVXdntblRbEdhtKHrHvQiFu16n8O+nz/s83+5fO0d6PaVs0uooDzhe7sbGQL03ncPi6BLqKA9oKklrtamqyplca/GoqplDp4qcfy8wVzy06qVXaBhRmliWJJlRgToURXHWxKZnWyvtfhIZqOPPpxqSkWdzjuiW/Hn4lKnS5Nbb4Dh2QpnENiHKSLi/7ooYZaxMprmA31N2syB5J2tPH8Fe5p3rFBrH0NhEBse0JNyrtM+zaivCfPRbiva9he3sVudyXUR3xwQKdW9ALcijcPEXFC6bh2LwIvil+ZW2u6stauPvIkliubRJbInaeDLUVqqqcsaUz4HcU8UdAU4521id23KnhAaFOL9Qx6hqcaLa2D+CBn5hV9wMPf/0c0VVVbIshaSU1OUWJ7epZbotnCrMcfnlUpkgvXeFPXNLJoqIvMJailnSlpO7qPd5t/Pvvwx9VK+LOpbZbmV7ZrKze8Cms0nlvlwE6b2d9axdwuJpGhB5VYyO17SsfCuH0s0cTHMkqofTzRxMM5FdWHEpjk4L8WHFyWJU8eX+CCN1QvTnvSGrpDsBVNz95I3RsZV2J7DbVZKzLBxMM3E43VFne/CUiaOnzVhsFf//C/ByjAKfO3Ib7HtlfQ4DnC7K49eUXfySvJMNGcecyzUodA1vwLDYRAbFtCDY4Ji1S1VVrKfXYtr3Nuak70F1/Hv6XvsZxoZ3AWDPycCacsTZhktVVfK/+z+MHQc4Z9isLWrj7yJJYpEktjY5Y8orTVTL3GRV2Q1EGhTq+4YUt66KdNatNrwCk9XKyLlyfha7jVPFo7klPXPLjuSmFGRXWkpSltbZUqy4jZjLRBGOxwF6r1ozsqTabWT9GIdakAyVjK0pPnUIGnHU7Ss+JpuV7Vkny5QHHC83OUewwYfOoaVJa5OACElaq5BvsnH4VPm61dO5FXcL0ChQr7gutezoav0wA/qLqEutqE9sVKCOZy+wT6zVpnK8uB637Mht0hkz9koyjFA/bbnEtlGkET+vK+PKZEpBFgtTdrEgeSfbMk86l+sUDT0iGjEsthUDopvhX9yC0F6QTNH+OZiPf0/g4L9RdN4AWFKXohiC0YW2BVz7ROtbXovv0AfQ1WtKbVAbfxdJEosksZdDhimf/TmniicESHcmrmcrSVaV4mS1SfENVgnFI6wN/cOviht+qvJPP1c8JddS5LwJLaUg22WiiNTibgvWarQU89MZz5nit7Rvbqx3IFHeAZe0NMWc9CN5K24uflR+bM2v5/cY6o84735MNivbMk+y7swR1mccY3MFSWuIwcc5sUCXsAY09g+XpLUCRRY7R0+by9WtpmRVXhYTE6R3JHJlRi3jww146Wvm/b0UM3aZLHaOnjE7X/+hdMd7cTKz8vchOkhHo4jS9yEh0kiDCGONvQ+ekJR/ll+Sd7Lg5A725KQ5lxs1OnpHNmZYbCLXRTXFR2dwaZGnqnay5zfHnrMfXUQ3jE0fQevbmcI/PnPMrld8U6yhTS98hoxHF9uowuNfKrXxd5EksUgSW5MyzQXszznl7LFa0hkgw1zxzQUKCvV8gktrVgMincmq91WerFbmn3KuXG421c7pojyX2c9KanNLkt7KvmSVpaAQ4eXnvPHMOVFEmRvSgg0+Hh3NNSf9SP6mx1ALSkeEFJ+6+HacVWkCW2SzsDXzpLM8YMvZ4y53ZAOEGnydo6ydw+Jp7B9Ra0ahawOLTSXpjNll9PHQKRPHMyofgQz317mMPjru/Dfga7w8I5CX4/Ml32TncHrp+1Xy3qXnVNK/VoG6IfrSetsoLxpFGKgfZsSgq13n46Hc0yxI3sGCkzs5lFfaN9pbq6dfVFOGxbaiV2QCXlo9dnM2BesfxJz0P1Adr13xicWr8YPoQq6naPFPmDb9UdxmRMHQvh9+o55D4+1X2eFrVG38XSRJLJLEekKmucB5U1XJTFYHc9OrvPO8nk+wM1Et6QjQyC8cb13NzQp0JbrazpUrWaGzpVhpuUJKYZbLRBHnJoIV8XK2FAt06bZQkvBGewe69aXtt5TdTN2+gOj83YSreZxW/Ej1bcHk1sOck2MU2Sz8ffaEozwg4yh/nz1RLtYwo6+zc0CXsHga+YVL0oqjFvRkZmlHgJKa0COnTVgr6SAX4K2hcZQXDYvv5C9JvIJqWS1obfp8yS60lSa2aaUJblZBJbXBGogLL1tvayAh0qtatcE1TVVV9uacYkHyDn45uZOkgrPOdf46IwOimzM0NpEeEY3QFp2i6MD7mA7MKe0lrTHg0+G/6AKvp2Dhh5j//gttnQSCnvvysvWWrU3nSglJYpEk1h1Z5kLnjVUHyky7ml5FslrXmayWdgRI8I/AR5LVarlSz5V/IlVVOWsuKNc3t+SGtNTCbE4V5VZrX6EG3zK1uSWjuqVJb7jRD42i4beU3Yzf+HW5itiSaYuHxLTkjCmfrZnlk9YIo1+ZG7Ea0NAv7B+dtKqqSlq2tdwI4eF0E0WV3JXvY9AUJ09GGkV6Of8edoXclV/bP19UVSUjz1b6BaK4S8LBUybyTRWX/xh1pV0anPW2UUaiAy/Pv4mqqmzPSnaUHCTvJLUw27kuSO/NoJgWDK/Tis7BMdiO/0DR3rewZWzCv98S9NGO9puWY3+jWsHQyNFDWjUVkj//XbyvG402JOqSvI7aeK5IEosksRXJNhdyMDe9zCxWjsS1ql/Asd5BpVOtFt9kleAffsW3Nbrcavu5ItxjslmLW4pluUwUkVJcl3uyIKtcq6qK6BUt0V4BpJlyqjWhBECkl78jaS2+GavBPzhpzcizOutVD6WX1q7mVZIYGXQKDcIN5epWowOv7P6oV+rni6qqpGZbS+tty3zhMFXSL9fXqClXb9sw0kiYn/aS/T+wq3a2nD3B/JM7+DVll8vVynCjH9cXJ7Rt1NPoQzs44yrYPAnT0W/wavIAxoT7KVr5BwU/zwadHq9rb8B74Fi0QeVnFvOk2niuSBJL7Uli8/PzWLzoDzKzMrHbbLRu044OHa4BYMP6tezdt9u5bXZWFomt2tCnT7+LiiPHUuRIVnNcR1fTiiqfmjDGO7D48n+kc4Q1wS8CP70kqzWhsg+OwsIC/vj9V7Kys7DbbERHx9Cv/yD0ej1Wq5VFi37nVFoqAIGBQQwcNAQfH59LHb5wk6qqZFuKnD1zU8pMEFFSm5tWzZZiJe5veC297WEc2rKNko/8m26+lcDAIABOHE9i+fIlWCwWVFVl4KAhxMbWqYmXd8nllL1EXSbZOZtfceKv1UBcmMFllqqESCN1Qq7Omaoq+nzZsmUTO7ZvBUVBUaBzp2tp2qy5yzYFBfl8+smH1KlTl+E33HQpQ66Sza5y8qzF5d/74CkTx06bKp25LMhHW6be1uhMdAO9a7ZO2abaWX/mKPNP7uC3lN1klWkZGe0dyNCYlgytk0jrwBhyfknEnr3XsVJjQBfaH/tBA9a9xx3L9Ea8etyET/+70QSEVHC0i1fRuXL48EHWrF7JmTOnaduuA717X+eyft3a1ezctR2A5s1a0K17L4/GJEkstSeJXfjLzwQFB9OtW0/MZjNfffUZAwcOJjradTubzcZ7777JzbfcTlRUdLWOl2spctaplkwOcCA33eWyxrmii5PV0kkBHH+WtAwRl0ZlSezSJYtBgT59+mG32/nhh3k0apRA27Yd2LJ5IydPnmDY8BEoisKff/yK0cuLXr36XoZXIDzNardxqiiXeUlbmLl/6Xm3/78GA8jfuIdbbx2Nn78/JpMJjUaDXq8nLzeXr776jJtvuY3Q0DCsVitWqxUvryvr/3mB2c6R9DKJS3FN5akqbhaqE6x3JC2RjsQlIcpIXJgBg6723gnvaRV9viQlHSUqKhqj0YucnBw+/+xj7rxrHIGBpTNUzZ//Awa9AbPZXKuS2MqYraVtwA6mFRV/mTFz/KyZyrKgiACdy7nRqLgfr6/R8+eHxW5j1elD/HJyJ3+k7nGZCr2eTzDDo5tyiyaJ0KQvsJ3Z4Fyn8W2JmhyBbW/xwJPRG+/et+Ez7EGPjy5XdK6cPZuB1Wpl//69WK1WlyT2xInjLF70O3fcORaNRsPXX31G9x69iI9v6LGYaleF+RXg9Rkv06Nnbw4e2E9BQT5dunYnMbG1R/Z9+nQ67Ts4Gh4bDAbq1q3Hnt07yyWxBw/ux8/fv8IENt9qchlRLRlhTakiWY308i/TY9UxwprgH0GAJKsXrSbPFwCL2YyqqthsNqwWC/5+pV/CLFYLdrsdRVEwm82EhUd47LjC89w5V3QaLbE+QXQOi4f959930YHjdOzYCT9/fwCMxtKrJlu3baF5i5aEhoY59q3TodPV3l8NZqujfVXZ2shDpxxtmypLRqICdS69SBOijDQIN+JtuHKT1Zr8bKlfP97594CAAHx9fcnNzXEmsXt278LXx5eoqGgOHz7kkWPWNINOcdbIDmpV+jlZaLZz9LSp3MxoqVlW0nMcP2sPunbTqROsdxmlbxRlJD7MgPEi2oDpNVr6RDahT2QTXrFZWJF+kAUnd7IobS/HCzJ5+/A63gYa+d3G2GZ30j9vOcbk+djzd2HsMhF931so+GUO1qS92NJPuCSwNXmuhISEAnDwQPkPon379tCyZSsMBsf9L4mtWrN37x5JYi83nVbHHXeOJSPjDF98/iktWiSiOeduwTNnTrNw4c/ln6yCEuiL0iSGRkoRncLinDMERUVFs3fPbqKiYigoKODY0SPOE6SsnTu206RFS7ZlniweVXUkqgdz0jlZmFVp3JFe/sWjqpHFkwM4brAKNHhfzNshzuOizhcgMCCAG0fcWm55l67dmP/zD7z7ziwsFivNmregUUJjAFq3aUdKSjLvzH4DRdEQHRNDu3YdPP7ahGe5fa6ocFeuj7O/7WmjncVRpfW0Co4rKkpqETk5OXzz9ReYzSYaNmxE12t7oNFoyDhzhsDAIObN+4rCwgLq1KlHz5590Osvb0s7q03lxFmzS5/VQ+mOBvq2Si4Lh/ppnSNnjcrMZBVQw5eFL5eL/WyJjIiiQ4f2VR7j2LGjFBUVEhnpuHkoLzeXzZs3cNvtd3Jg/16PvI7LydugoXmsN81jXX8P5hbZOFx8zpXtlJCRZ+NkpoWTmRZW7CutZ9UoUD/MUG7K3/qh7peheGn1DIhuzoDo5hRYzSw5tZ/5J3ew7NQBDuWd5vk8eJ7GdI6czMPaIzSodwf1Itujb96FwrVvYytYivXMRnRh12A745idTavaa/xcOVduTjZ169ZzPg4MCOLA/n1u7eN8JIm9AM2atwQgNDQMjUZDfn4e/v6uZQhhYeGMGXOfy7LfUnYzecdCUouOwcHdcBCivQKY2moI18e0oFfv61i+/C8+++wjfH39qFevPrn5eWzPPMnB3NPszz3F0dMpNDyeyrPaXfx/e3ceFlX1/wH8PTMwMwzgENuwCm4gAgrigiuWC6YoRpZfrVzTzChcSrPNLSX1ixn6NdMSTUu03MpMxQRS01JAxeWHCKiEICgGCDIwzOf3B3J1ZBGRUcY+r+eZ53HOPffce2aOZz6ce+656qyaz89WZga3O6Op7sI0ABUsOFh9IhraXqrk5V6tMT0l5TxsbG3x8ohR0JSXY/v2H3Am+RS8vDvg8qUMAMCUt6ZCJBLh1z0/448jh9CjZ+9GrBlrbA1pK1WrEwA1P0Z0rvdgXMs6gmvXsjH8pf8AALZv34pTpxLh69sJWm0FMjMv4+URoyCVyvDrr7tx5Mjvj23qiVZLuPpP9TmMGXllKKvlBh1zubhasNBaJYOV2b/rJ+1R+xag9v4FqLw6uPfXnzFkaIjwR82+fb8goM9zwuja08pcLoGPiwI+Lrr3Edws1lRrqxdz1CgsrbxCkJFXhpgzd2+ANpaI0KLqhsB7rgQ4WtTvhkCFkRRDHL0xxNEbReWl2Jd9Hj9nJSM+NxXHiktwDHbA0e3oYPEnhjq2R8jtGEiyd6M8exck1l2A604AOsJ++yKU3BoFy97D9dJWaiPC3TrSQ8zjr69/1//4RnLvpTaxWASttvqwwP1/0RSVlyKz5B88CyBPJhVGS3JKC/HGX99jhd/LaGVujWJPJ1xtLsWFwmtQnDuFPFLjaPwpoZwuN4wAUzHUkso1Hd3M766xWjXCWvXcZtY0NKS93Ku2kdikxBMY+PxgiMViSGUyuLm3xZUrl+Hl3QEnTybC08tbOLZHO0/89ecx9GicKjE9aWhbmVluh+zbhciWaoS+xd5EibnegzHIwRPbmp2Bm5u7EIi4ubkjOzsbvr5As2ZK2NraQS6v/CPXo207/PXX0UavGxEhr0hTOaqVe8+81Vw1bpfV/OMmN65cKqmNnW7AqmpmGMtX6duj9i11ja5dv56H7du2YODzQXBychbSr17Nwt5ffwEAlJeXQaPR4Ietm/HSyyMfoSaG4xlTI3RuaYTOLU2FtHvbduqdFRLubdsXctS4kKPWKcfEWIRW9z1yt41KBts62ra5sRzDm/tieHNf3Cwrwd6r5/BT1mkcyUvHqX+ycOqfLPxYYYN3zLrBv/gEcP0vAH8B6AiJ8ixKflqK0gPfQWTbGxVqNWCuW35jj8SaN1OioOAf4X1hYQHMmylr36EBOIjVk3v/oqkgLfz3LUV2afVnt1d13aEJWyGvAMrEgFYE2JSKEPyPHAdcymElNa0cTTWzgVVWGjx6dsOStu1hKTOtVh4zTA0ZiVUqLZCengZ7e0dUVFTgUkYanJwqL91YWFjgUkY63N09AABpaRdhbaPf5VfY41FbW6kgLf68fgnP5l5Ba9vmOlOVPNp5Iu1iKry8O4CIcOlSBpzvtBWPdl6Ijz8IjUYDIyMjZGSkwcZW9Ujn+E+xRljT896VAQpv1zwPwEgCtLSRVZu3Wt/RKla7hoyu3bhxHdt+3IIBgYPg6tpSZ9vb78wQ/n0m+RTS0i4axI1d+iQSiWDbzBi2zYzRw+3u07SqrjLc//8gPbcMt8sJZ/4uxZm/deOC+68yVK2WYHnfVYZnpAqMdO2Eka6dkFd6C3uunsFPWcn464YIb8IelqZdMLz8JF7VVA6CiRxzILa9CW2iMUh5GwXLJ0M+ZALkXQcJZTb2SKy7uwd+O7APPr5+EIvFSD59Cr0aeXUCDmIfgz+vX0J2HUtWVWlRJkPvXCmMjYwgk8rQdkBnvO3mDas7werlyxnYZ5SJge39eRSC4bm+AxCz/1dErVsDIoKjoxM6+lXeGNi9R2/s37cHUevWAACsrK0xYMCguopjBk4iEqO7TUu0ITlsbHRvBvXw8EROTjbWffMVxGIxnJyc4XtnjrSjoxNatWqDDeu/hlgshrW1DQYEPl+vY94qrUBabhlSr5UK8wYv5qpxvajm5atqmzfY3EoK46dw+SpD9duB/VCr1YiPP4j4+MrVLwICnmvUG3L+DcRiEZwspXCylOJZj7vDnnXN9y4q1SLp8m0kXb6tU1bVfO97l35rrZLBXC6BjdwMY1r6Y0xLf1y9XYBfsioD2jU3TbFO6o+3AJySOOCKTXe0s+4EpBRAW5iLsswfIOvUFyJJw5fHvHLlMn7ZvQtlZWoQAf93/hz69x+I1m3c0Ly5C9zdPbA+ai0AoK1HO7Ro2bhtiJfYgv6X2Nr59ymEntj6wHwrO72MYU6Nd+c6ezo0xQWmWdOlj/ZSWl65fNXFa3d+eO+5g7s2js8YCz+6jXUHN2t83L80HfeuvHHxnldmfnmt+9gpjarNt61aeeNKcT5+zjqDn7JO42xBNkREIJEI5gREFiTAT7wPkNtC3mYSJLJeQDEg9etX62Ntm2JbeWpGYletWoWlS5ciOzsbnp6eWL58OXr16vWkTwsAYCszf3Cmh8jH/h0qtISESyVI/7sMLW8Vw89V8cSfB86arsZoL+UVhMvX766lmZZbhtQcNTLzy6BtAmtpMvY0kxqJ4W4vh7u97vKWxeo7f0TmVl8DOaeg8nXowt1lwHTXQG6LMFUHyFyLkHQ7Bb9kJyO1KA/bFVI4qs1hV5qL0uRPQSQCblhAFOsHRf/ZkPk8K1zxLVXfxrG9S1CWfwFSSzf4D5wJuaxp3Cj+VIzEbtmyBa+99hpWrVqFHj164KuvvsLXX3+Nc+fOoXnz5g/cX98jsXfnxNb9FKyjA94V5rCxf7eYM4UI330N1wrujnSplEaYHaRCf6/H80AOZjgetr3U9FSji9fUyLiuhqaWJ9gqTSRws7tnxOfOZU0LxdO5fNW/RVMcXWP1c//T6Kr+L9+s5Wl0RmKgubUxbO2KUaTMQLr2/+B9+y+MUp+AnzZTyEe3FBD9MwRmQ0JxKHkT2pdEQSK9u3RfRZkUpxXj0HfUar3X8UGeiiC2a9eu6NixI7788kshzcPDA8OGDUN4ePgD938cT+wKP3Yc/8veWfnm3sGRO5/+W/bDMNu/s16OzQxLzJlCTPsuq9piJFXN5vNXHDmQZYIHtZePh9nBwcL4zujqg58vr5CKhcv/916mfJzPl2ePDwexT58btzTV5ttezFHjllr3xkoCocI0H2R9GS0sj2FUeTye15wFCs0hPtcGsLwJkVsagMrRXWG/O13HSeM3nngga/BBbFlZGRQKBX744Qe88MILQnpYWBhOnjyJ+Pj4B5ah95FYLaH/kov4W5yO2y4nQLISYZtIrYDJ5U6wUbfAJ8EqvhP3X06rJczflYN/SmpZzR2Vz/nmtsKA+rWX2siMRGhpK0UblfzujVZ2MtgrefmqfxMOYv8diAg5BRqdkduqP2hLy6kyoDXLg8LqLFSmZzAg6xrGWP0MSMtRU3dABFSUy9DstZtPdGqBwc+JvX79OioqKqBS6S4Jo1KpkJOTU+M+arUaavXdNdsKCiofyVpY+OAVBBriREYxrub+AzEsocjphwqzPJC0FKIyOSS3bCCCGDdQgLD1tT8alrEqN9TgtsIeir2FEbwcTdDCRopWKila2crh+IzxfXNmtQBuo6iotlLY06ioqAgyuX5++1jTYioGOtgDHeyNARgDMEOFlpB1sxwZeWqk5Zog7ZoKaVndkF3xPxRpy4HqK4PeQ43j2+ej1+DZejtnc3PzOv+oNvggtsr9lSSiWiseHh6OefPmVUt3dnauITdjjBm2FABxT/okGGMG41cA/61Xzs/uvPTjQVfIDT6Itba2hkQiqTbqmpubW210tsrs2bMxffp04b1Wq0V+fj6srKwey2W0wsJCODs7IzMzU29zcNnTgdsKexjcXtjD4PbC6utJtRVz87pXbTL4IFYqlcLPzw8xMTE6c2JjYmIQHBxc4z4ymQwyme7ivhYWFvo8zRo1a9aMOw5WL9xW2MPg9sIeBrcXVl9Nra0YfBALANOnT8drr72GTp06oVu3blizZg2uXLmCyZMnP+lTY4wxxhhjevBUBLEjRozAjRs3MH/+fGRnZ8PLywt79uyBi4vLkz41xhhjjDGmB09FEAsAU6ZMwZQpU570adSLTCbDnDlzqk1pYOx+3FbYw+D2wh4GtxdWX021rRj8OrGMMcYYY+zfh59xyhhjjDHGDA4HsYwxxhhjzOBwEMsYY4wxxgwOB7EPITw8HJ07d4a5uTlsbW0xbNgwpKSk6OS5desWQkND4eTkBBMTE3h4eODLL7+s9zHUajV8fHwgEolw8uRJnW1XrlzBkCFDYGpqCmtra7zzzjsoKytrjKoxPQsPD4dIJMLUqVOFNCLC3Llz4eDgABMTE/Tp0wdnz559YFmurq4QiUQ6r/fff79avvXr16N9+/aQy+Wws7NDaGhoY1aJNaK5c+dW+07t7Ox08pw/fx5Dhw6FUqmEubk5/P39ceXKlTrLvXDhAoKDg2FtbY1mzZqhR48eiI2N1cnD/UrTptFo8NFHH6FFixYwMTFBy5YtMX/+fGi1WiFPQ/uShQsXonv37lAoFLWulR4WFgY/Pz/IZDL4+PhU2x4XF4fg4GDY29vD1NQUPj4++O677xpaXfaEGVqb4CD2IcTHx+Ott97CsWPHEBMTA41GgwEDBqC4uFjIM23aNOzduxebNm3C+fPnMW3aNLz99tvYtWtXvY4xc+ZMODg4VEuvqKjA4MGDUVxcjMOHDyM6Ohrbtm3DjBkzGq1+TD+OHz+ONWvWoH379jrpS5YswbJly7By5UocP34cdnZ26N+/P4rq8fD6quXkql4fffSRzvZly5bhww8/xPvvv4+zZ8/it99+Q2BgYKPWizUuT09Pne80OTlZ2JaWloaePXuibdu2iIuLw6lTp/Dxxx9DLpfXWebgwYOh0Whw8OBBJCQkwMfHB0FBQcITDrlfafoWL16M1atXY+XKlTh//jyWLFmCpUuXYsWKFUKehvYlZWVleOmll/Dmm2/WmoeIMH78eIwYMaLG7X/88Qfat2+Pbdu24fTp0xg/fjxGjx6Nn3/+uWEVZo3u6tWr0Gg09cprcG2CWIPl5uYSAIqPjxfSPD09af78+Tr5OnbsSB999NEDy9uzZw+1bduWzp49SwAoKSlJZ5tYLKasrCwhbfPmzSSTyaigoODRK8P0oqioiNq0aUMxMTEUEBBAYWFhRESk1WrJzs6OPvvsMyFvaWkpKZVKWr16dZ1luri40Oeff17r9vz8fDIxMaEDBw40RhXYYzBnzhzq0KFDrdtHjBhBr7766kOVmZeXRwDo999/F9IKCwsJgNA2uF9p+gYPHkzjx4/XSQsJCRHaw6P0JVWioqJIqVTWmedBbfRegwYNonHjxtUrL9O/uXPnkkqlounTp9Pp06frtY+htAkeiX0EBQUFAABLS0shrWfPnvjpp5+QlZUFIkJsbCwuXLjwwFGwa9euYeLEidi4cSMUCkW17UePHoWXl5fOKG1gYCDUajUSEhIaqUassb311lsYPHgw+vXrp5OekZGBnJwcDBgwQEiTyWQICAjAH3/88cByFy9eDCsrK/j4+GDhwoU6l39jYmKg1WqRlZUFDw8PODk54eWXX0ZmZmbjVYw1utTUVDg4OKBFixb4z3/+g/T0dACAVqvFL7/8Ajc3NwQGBsLW1hZdu3bFzp076yzPysoKHh4e+Pbbb1FcXAyNRoOvvvoKKpUKfn5+ALhfMQQ9e/bEb7/9hgsXLgAATp06hcOHD2PQoEEAHr0v0YeCggKd30X2ZM2aNQuRkZFISUlBx44d0bFjR3zxxRfIy8t7bOegrzbBQWwDERGmT5+Onj17wsvLS0iPjIxEu3bt4OTkBKlUioEDB2LVqlXo2bNnnWWNHTsWkydPRqdOnWrMk5OTA5VKpZP2zDPPQCqVCpcGWdMSHR2NxMREhIeHV9tW9Z3d/52qVKoHfp9hYWGIjo5GbGwsQkNDsXz5cp0HfaSnp0Or1WLRokVYvnw5fvzxR+Tn56N///4817GJ6tq1K7799lvs27cPa9euRU5ODrp3744bN24gNzcXt27dwmeffYaBAwdi//79eOGFFxASEoL4+PhayxSJRIiJiUFSUhLMzc0hl8vx+eefY+/evcJcN+5Xmr5Zs2Zh5MiRaNu2LYyNjeHr64upU6di5MiRAB6tL9GHH3/8EcePH8e4ceMe+7FZzeRyOV5++WXs3r0bWVlZGD16NDZs2ABHR0cMGzYMO3bsqPd0g4bQZ5vgILaBQkNDcfr0aWzevFknPTIyEseOHcNPP/2EhIQEREREYMqUKThw4AAAYPLkyTAzMxNeALBixQoUFhZi9uzZdR5TJBJVSyOiGtPZk5WZmYmwsDBs2rSpznmL9393936fNbUVoHLedUBAANq3b4/XX38dq1evxjfffIMbN24AqBy5Ky8vR2RkJAIDA+Hv74/NmzcjNTW12k09rGl4/vnn8eKLL8Lb2xv9+vXDL7/8AgDYsGGDcANPcHAwpk2bBh8fH7z//vsICgrC6tWrAdTcVogIU6ZMga2tLQ4dOoS//voLwcHBCAoKQnZ2tnBs7leati1btmDTpk34/vvvkZiYiA0bNuC///0vNmzYoJOvIX1JY4uLi8PYsWOxdu1aeHp66u04rOFsbW0xdepUJCYmYteuXTh69ChCQkJw5swZvRxP722i0Sco/AuEhoaSk5MTpaen66SXlJSQsbEx7d69Wyd9woQJFBgYSERE165do9TUVOFFRBQcHExisZgkEonwAkASiYRGjx5NREQff/wxtW/fXqfc/Px8AkAHDx7UV1VZA+3YsUP4Du/9TkUiEUkkErp48SIBoMTERJ39hg4dKnznNbWVmvz9998EgI4dO0ZEROvWrSMAlJmZqZPP1taW1qxZ08g1ZfrSr18/mjx5MqnVajIyMqIFCxbobJ85cyZ1796diGpuKwcOHCCxWFxtbmvr1q0pPDyciLhfMQROTk60cuVKnbQFCxaQu7s7ERGlpaU9cl/SGPMf4+LiyMzMjL766qt61Io9KYWFhbRu3Tp69tlnSSKR0HPPPUcbNmwgtVqtk89Q2oRR44fFTy8iwttvv40dO3YgLi4OLVq00NleXl6O8vJyiMW6A9wSiUQYTbG1tYWtra3O9sjISHz66afC+6tXryIwMBBbtmxB165dAQDdunXDwoULkZ2dDXt7ewDA/v37IZPJhPltrOno27evzt3lADBu3Di0bdsWs2bNQsuWLWFnZ4eYmBj4+voCqLwrND4+HosXLwZQc1upSVJSEgAI7aJHjx4AgJSUFDg5OQEA8vPzcf36dbi4uDROBZleqdVqnD9/Hr169YJUKkXnzp2rLed34cIF4fusqa2UlJQAQLX+SCwWC/0R9ytNX0lJSZ2/KS1atGi0vqSh4uLiEBQUhMWLF2PSpEl6Ow5rmIqKCuzfvx8bN27Ezp074eTkhNGjR2P9+vVo3ry5Xo752NqE3sLjp9Cbb75JSqWS4uLiKDs7W3iVlJQIeQICAsjT05NiY2MpPT2doqKiSC6X06pVq+p9nIyMjGqrE2g0GvLy8qK+fftSYmIiHThwgJycnCg0NLQxq8j06N7VCYiIPvvsM1IqlbR9+3ZKTk6mkSNHkr29PRUWFtZaxh9//EHLli2jpKQkSk9Ppy1btpCDgwMNHTpUJ19wcDB5enrSkSNHKDk5mYKCgqhdu3ZUVlamr+qxRzBjxgyKi4uj9PR0OnbsGAUFBZG5uTldunSJiIi2b99OxsbGtGbNGkpNTaUVK1aQRCKhQ4cO1VpmXl4eWVlZUUhICJ08eZJSUlLo3XffJWNjYzp58iQRcb9iCMaMGUOOjo60e/duysjIoO3bt5O1tTXNnDlTyNOQvoSI6PLly5SUlETz5s0jMzMzSkpKoqSkJCoqKhLypKamUlJSEr3xxhvk5uYm5KkauYuNjSWFQkGzZ8/W+V28ceOGfj4Q9tDmz59PSqWSJk6cSEeOHKkzr6G1CQ5iHwKAGl9RUVFCnuzsbBo7diw5ODiQXC4nd3d3ioiIIK1WW+/j1BTEElU2rsGDB5OJiQlZWlpSaGgolZaWNlLtmL7dH8RqtVqaM2cO2dnZkUwmo969e1NycnKdZSQkJFDXrl1JqVQK7WvOnDlUXFysk6+goIDGjx9PFhYWZGlpSS+88AJduXJFH9VijWDEiBFkb29PxsbG5ODgQCEhIXT27FmdPN988w21bt2a5HI5dejQgXbu3PnAco8fP04DBgwgS0tLMjc3J39/f9qzZ49OHu5XmrbCwkIKCwuj5s2bk1wup5YtW9KHH36oc/m3IX0JUWWAXNNvWmxsrJAnICCgxjwZGRl1lhEQENDInwRrqIyMDLp9+3a98hpamxAREelvnJcxxhhjjLHGx6sTMMYYY4wxg8NBLGOMMcYYMzgcxDLGGGOMMYPDQSxjjDHGGDM4HMQyxhhjjDGDw0EsY4wxxhgzOBzEMsYYY4wxg8NBLGOMMcYYMzgcxDLGGGOMMYPDQSxjjDUhffr0wdSpU4X3rq6uWL58+RM7n8ft448/xqRJk4T3938ejSE3Nxc2NjbIyspq1HIZY48XB7GMsSZp7NixEIlE1V4XL15slPLXr18PCwuLRilLn44fP64T1D0pj+PzunbtGr744gt88MEHej2Ora0tXnvtNcyZM0evx2GM6RcHsYyxJmvgwIHIzs7WebVo0eJJn1Y15eXleivbxsYGCoVCb+U/bhUVFdBqtTVu++abb9CtWze4urrq/TzGjRuH7777Djdv3tT7sRhj+sFBLGOsyZLJZLCzs9N5SSQSAMDPP/8MPz8/yOVytGzZEvPmzYNGoxH2XbZsGby9vWFqagpnZ2dMmTIFt27dAgDExcVh3LhxKCgoEEZ4586dCwAQiUTYuXOnznlYWFhg/fr1AIBLly5BJBJh69at6NOnD+RyOTZt2gQAiIqKgoeHB+RyOdq2bYtVq1bVWb/i4mKMHj0aZmZmsLe3R0RERLU8908nqKtewN0R0927d8Pd3R0KhQLDhw9HcXExNmzYAFdXVzzzzDN4++23UVFRIexXVlaGmTNnwtHREaampujatSvi4uIe+HnVtd/959OuXTvIZDJcvny5xs8jOjoaQ4cOrfMz27t3L5RKJb799lsAlSP2w4YNw6JFi6BSqWBhYSG0hffeew+WlpZwcnLCunXrdMrx9vaGnZ0dduzYUefxGGNNl9GTPgHGGHtY+/btw6uvvorIyEj06tULaWlpwiX3qkvEYrEYkZGRcHV1RUZGBqZMmYKZM2di1apV6N69O5YvX45PPvkEKSkpAAAzM7OHOodZs2YhIiICUVFRkMlkWLt2LebMmYOVK1fC19cXSUlJmDhxIkxNTTFmzJgay3jvvfcQGxuLHTt2wM7ODh988AESEhLg4+NT63HrqleVkpISREZGIjo6GkVFRQgJCUFISAgsLCywZ88epKen48UXX0TPnj0xYsQIAJUjk5cuXUJ0dDQcHBywY8cODBw4EMnJyXV+XnXt16ZNG+F8wsPD8fXXX8PKygq2trbV6nXz5k2cOXMGnTp1qrXu0dHRmDRpEjZu3Ijg4GAh/eDBg3BycsLvv/+OI0eOYMKECTh69Ch69+6NP//8E1u2bMHkyZPRv39/ODs7C/t16dIFhw4dwvjx42s9JmOsCSPGGGuCxowZQxKJhExNTYXX8OHDiYioV69etGjRIp38GzduJHt7+1rL27p1K1lZWQnvo6KiSKlUVssHgHbs2KGTplQqKSoqioiIMjIyCAAtX75cJ4+zszN9//33OmkLFiygbt261Xg+RUVFJJVKKTo6Wki7ceMGmZiYUFhYmJDm4uJCn3/++UPVCwBdvHhRSHvjjTdIoVBQUVGRkBYYGEhvvPEGERFdvHiRRCIRZWVl6ZTdt29fmj17tlDu/Z9XffcDQCdPnqy1DkRESUlJBICuXLmikx4QEEBhYWH0v//9j5RKJR08eFBn+5gxY8jFxYUqKiqENHd3d+rVq5fwXqPRkKmpKW3evFln32nTplGfPn3qPC/GWNPFI7GMsSbr2WefxZdffim8NzU1BQAkJCTg+PHjWLhwobCtoqICpaWlKCkpgUKhQGxsLBYtWoRz586hsLAQGo0GpaWlKC4uFsp5FPeOGObl5SEzMxMTJkzAxIkThXSNRgOlUlnj/mlpaSgrK0O3bt2ENEtLS7i7u9d53PrUS6FQoFWrVsI+KpUKrq6uOqPNKpUKubm5AIDExEQQEdzc3HSOpVarYWVlVeu51Hc/qVSK9u3b11mv27dvAwDkcnm1bdu2bcO1a9dw+PBhdOnSpdp2T09PiMV3Z8epVCp4eXkJ7yUSCaysrIT6VjExMUFJSUmd58UYa7o4iGWMNVmmpqZo3bp1tXStVot58+YhJCSk2ja5XI7Lly9j0KBBmDx5MhYsWABLS0scPnwYEyZMeOBNWCKRCESkk1bTPvcGwlU3Kq1duxZdu3bVyVc1h/d+9x+jPupbL2NjY539RCJRjWlV563VaiGRSJCQkFDtfOuaZlHf/UxMTCASieqsm7W1NYDKaQU2NjY623x8fJCYmIioqCh07ty5WlkPW98q+fn51Y7FGDMcHMQyxgxOx44dkZKSUmOACwAnTpyARqNBRESEMEK3detWnTxSqVTnxqYqNjY2yM7OFt6npqY+cLROpVLB0dER6enpeOWVV+pVh9atW8PY2BjHjh1D8+bNAVQGcBcuXEBAQECD69UQvr6+qKioQG5uLnr16lVjnpo+r/rsV1+tWrVCs2bNcO7cuWoju61atUJERAT69OkDiUSClStXPtKxqpw5cwZ9+vRplLIYY48fB7GMMYPzySefICgoCM7OznjppZcgFotx+vRpJCcn49NPP0WrVq2g0WiwYsUKDBkyBEeOHMHq1at1ynB1dcWtW7fw22+/oUOHDlAoFFAoFHjuueewcuVK+Pv7Q6vVYtasWdVG9Woyd+5cvPPOO2jWrBmef/55qNVqnDhxAjdv3sT06dOr5TczM8OECRPw3nvvwcrKCiqVCh9++KHOZfH71adeDeHm5oZXXnkFo0ePRkREBHx9fXH9+nUcPHgQ3t7eGDRoUI2fV332qy+xWIx+/frh8OHDGDZsWI3nGBsbiz59+sDIyOiRHwBRUlKChIQELFq06JHKYYw9ObzEFmPM4AQGBmL37t2IiYlB586d4e/vj2XLlsHFxQVA5eXnZcuWYfHixfDy8sJ3332H8PBwnTK6d++OyZMnY8SIEbCxscGSJUsAABEREXB2dkbv3r0xatQovPvuu/Vap/X111/H119/jfXr18Pb2xsBAQFYv359nevaLl26FL1798bQoUPRr18/9OzZE35+frXmr0+9GioqKgqjR4/GjBkz4O7ujqFDh+LPP/8U7uav7fN60H4PY9KkSYiOjq51HVl3d3ccPHgQmzdvxowZMxpeWQC7du1C8+bNH3kEmTH25IioIROzGGOMsUZGRPD398fUqVMxcuRIvR6rS5cumDp1KkaNGqXX4zDG9IdHYhljjDUJIpEIa9as0XlohT7k5uZi+PDheg+UGWP6xSOxjDHGGGPM4PBILGOMMcYYMzgcxDLGGGOMMYPDQSxjjDHGGDM4HMQyxhhjjDGDw0EsY4wxxhgzOBzEMsYYY4wxg8NBLGOMMcYYMzgcxDLGGGOMMYPDQSxjjDHGGDM4/w8h+dqRaqJ7gwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 700x380 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>size_class</th>\n",
+       "      <th>28-40</th>\n",
+       "      <th>40-56</th>\n",
+       "      <th>56-80</th>\n",
+       "      <th>80-112</th>\n",
+       "      <th>&gt;112</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sensor</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ATMS</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CrIS</th>\n",
+       "      <td>2.1</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>16.7</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future MW</th>\n",
+       "      <td>9.3</td>\n",
+       "      <td>13.6</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future IR</th>\n",
+       "      <td>13.4</td>\n",
+       "      <td>13.6</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>20.8</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "size_class  28-40  40-56  56-80  80-112  >112\n",
+       "sensor                                       \n",
+       "ATMS          0.0    0.0    0.0     8.3   0.0\n",
+       "CrIS          2.1   10.2    4.5    16.7   0.0\n",
+       "Future MW     9.3   13.6    9.1    25.0   0.0\n",
+       "Future IR    13.4   13.6    7.6    20.8   0.0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "BLUE, ORANGE, AQUA, YELLOW, GRAY, INK = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#eda100\", \"#8a8984\", \"#0b0b0b\"\n",
+    "SENSOR_COLORS = {\"ATMS\": BLUE, \"Future MW\": ORANGE, \"CrIS\": AQUA, \"Future IR\": YELLOW}\n",
+    "SENSOR_STYLES = {\"ATMS\": \"-\", \"Future MW\": \"--\", \"CrIS\": \"-\", \"Future IR\": \"--\"}\n",
+    "plt.rcParams.update({\"axes.spines.top\": False, \"axes.spines.right\": False, \"axes.grid\": True, \"grid.color\": \"#e6e5e0\", \"grid.linewidth\": 0.6})\n",
+    "\n",
+    "edges = np.array([EFFECTIVE_RESOLUTION_KM, 40, 56, 80, 112, np.inf])\n",
+    "labels = [f\"{EFFECTIVE_RESOLUTION_KM}-40\", \"40-56\", \"56-80\", \"80-112\", \">112\"]\n",
+    "results[\"size_class\"] = pd.cut(results.diameter_km, edges, labels=labels, right=False)\n",
+    "by_size = results.groupby([\"sensor\", \"size_class\"], observed=False).resolved.mean().unstack().loc[sensors.index] * 100\n",
+    "counts = results[results.sensor == \"ATMS\"].groupby(\"size_class\", observed=False).size()\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(7, 3.8))\n",
+    "for sensor in sensors.index:\n",
+    "    ax.plot(labels, by_size.loc[sensor], marker=\"o\", color=SENSOR_COLORS[sensor], ls=SENSOR_STYLES[sensor], label=sensor)\n",
+    "ax.set(xlabel=\"Feature diameter (km)\", ylabel=\"Features resolved (% of size class)\", title=\"Resolved features by size\", ylim=(0, 100))\n",
+    "ax.legend(frameon=False, fontsize=8)\n",
+    "for x, n in zip(labels, counts):\n",
+    "    ax.annotate(f\"n = {n}\", (x, 2), ha=\"center\", fontsize=8, color=GRAY)\n",
+    "fig.tight_layout()\n",
+    "plt.show()\n",
+    "by_size.round(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "576f79bc",
+   "metadata": {},
+   "source": [
+    "Resolution matters most for the smaller features. The shares are of all features in each size class, so they also reflect how many of them are observed (none of the 10 features larger than 112 km happens to be observed), and those of a future infrared sounder, which resolves every feature it observes, are its observed shares. The sensors differ most for the smallest features (28 to 40 km): a future infrared sounder resolves 13% of them, a future microwave sounder 9%, CrIS 2%, and ATMS none. ATMS resolves only features larger than 80 km (8% of them)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d03edac1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:16:35.274251Z",
+     "iopub.status.busy": "2026-10-24T21:16:35.273251Z",
+     "iopub.status.idle": "2026-10-24T21:16:35.441615Z",
+     "shell.execute_reply": "2026-10-24T21:16:35.441111Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(9, 4))\n",
+    "bins = np.arange(0, 25, 1)\n",
+    "nature_hist, _ = np.histogram(features.solar_hour, bins)\n",
+    "ax.bar(bins[:-1] + 0.5, nature_hist, width=1, color=\"#e6e5e0\", label=\"Nature Run (all features)\")\n",
+    "for swath, style, color in [(\"ATMS\", \":\", BLUE), (\"CrIS\", \":\", AQUA)]:\n",
+    "    r = results[results.sensor == swath]\n",
+    "    hist, _ = np.histogram(r.solar_hour[r.observed], bins)\n",
+    "    ax.plot(bins[:-1] + 0.5, hist, ls=\":\", color=color, label=f\"observed ({swath} swath)\")\n",
+    "for sensor in sensors.index:\n",
+    "    r = results[results.sensor == sensor]\n",
+    "    hist, _ = np.histogram(r.solar_hour[r.resolved], bins)\n",
+    "    ax.plot(bins[:-1] + 0.5, hist, ls=\"-\", marker=\"o\", ms=3, color=SENSOR_COLORS[sensor], label=f\"resolved ({sensor})\")\n",
+    "ax.set(xlabel=\"Solar hour at feature\", ylabel=\"Features\", title=\"Features by solar hour\", xlim=(0, 24))\n",
+    "ax.set_xticks(range(0, 25, 3))\n",
+    "ax.legend(frameon=False, fontsize=8, loc=\"upper left\")\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "965f1d13",
+   "metadata": {},
+   "source": [
+    "The solar hour of the features shows when the sampling gap arises. Features are most frequent in the late afternoon and evening (about 17 to 21 solar hours), after the satellites' afternoon pass (near 13:30), which samples the developing convection, and well before their next pass (near 01:30), which samples the decaying convection. A constellation with a different distribution of local times (for example, satellites in other orbit planes) would observe more of the features, independently of resolution, as the constellation trade below shows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "b480d8db",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:16:35.443623Z",
+     "iopub.status.busy": "2026-10-24T21:16:35.443623Z",
+     "iopub.status.idle": "2026-10-24T21:16:35.458511Z",
+     "shell.execute_reply": "2026-10-24T21:16:35.457959Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"3\" halign=\"left\">equivalent</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">cross-track</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>N = 1</th>\n",
+       "      <th>N = 2</th>\n",
+       "      <th>N = 3</th>\n",
+       "      <th>N = 1</th>\n",
+       "      <th>N = 2</th>\n",
+       "      <th>N = 3</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sensor</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ATMS</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CrIS</th>\n",
+       "      <td>11.6</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>1.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future MW</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Future IR</th>\n",
+       "      <td>12.3</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>12.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          equivalent             cross-track            \n",
+       "               N = 1 N = 2 N = 3       N = 1 N = 2 N = 3\n",
+       "sensor                                                  \n",
+       "ATMS             6.7   0.7   0.0         5.3   0.7   0.0\n",
+       "CrIS            11.6   6.3   1.8        10.5   5.3   1.4\n",
+       "Future MW       14.0  11.6   7.7        13.7   9.5   6.0\n",
+       "Future IR       12.3  12.3  12.3        12.3  12.3  12.3"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sensitivity to the resolution criterion: the share of features resolved if their viewed part must span at least N\n",
+    "# resolution elements, of either size\n",
+    "sensitivity = pd.DataFrame(\n",
+    "    {\n",
+    "        (axis, f\"N = {n}\"): results.groupby(\"sensor\")[f\"elements ({axis})\"].apply(lambda r: 100 * (r >= n).mean())\n",
+    "        for axis in AXES\n",
+    "        for n in (1, 2, 3)\n",
+    "    }\n",
+    ").loc[sensors.index]\n",
+    "sensitivity.round(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23ff7ff6",
+   "metadata": {},
+   "source": [
+    "Each sensor resolves fewer features with a stricter criterion: more resolution elements across a feature, or the cross-track size of the element. A future infrared sounder is insensitive to either (it resolves every feature it observes, even with three cross-track elements), whereas the resolved share of a future microwave sounder falls from 14% to 8% as the criterion tightens from one to three equivalent elements, and to 6% with three cross-track elements. With a lenient criterion of one element, a future microwave sounder resolves more features than a future infrared sounder, because its wider swath observes more. The cross-track measure lowers the shares by at most about 2 percentage points, much less than the number of elements does, because most observed features are viewed at moderate zenith angles (a median of 38 to 44 degrees), where the cross-track size exceeds the equivalent diameter by only 13 to 18%."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57adeec0",
+   "metadata": {},
+   "source": [
+    "## Trade the Constellation Design\n",
+    "\n",
+    "An OSSE is most useful for comparing designs that do not exist yet. The analysis above finds that the current sounders observe only 12 to 14% of the features, because all three satellites share one orbit plane, and that finer resolution helps only with the features that are observed. This section trades the two: how many satellites, in how many orbit planes, carrying heritage or future sensors.\n",
+    "\n",
+    "Each design places satellites in sun-synchronous orbits at 830 km (the altitude at which the nominal resolutions of the sensors apply), in planes whose ascending equator crossing times are spread evenly over 12 hours from 13:30, so that `P` planes observe at `2P` evenly spaced local times. The satellites of a plane are evenly spaced in it, and each carries both swaths (ATMS and CrIS). Two strategies are compared for 1 to 6 satellites: all in one plane, like the current constellation, and one per plane. A design of 6 satellites in 2 planes (3 per plane) shows how much of the benefit of separate planes a single additional plane gives. Which passes cross the study region depends on the phase of the satellites in their planes, so each design is evaluated for `N_DRAWS` random phases and the results averaged.\n",
+    "\n",
+    "Two days are too few to rank designs, so the trade uses two weeks of the Nature Run (`TRADE_DURATION`), downloaded once (about 120 MB, a few minutes). Evaluating the designs takes about three minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "b02e9ec9",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:16:35.460523Z",
+     "iopub.status.busy": "2026-10-24T21:16:35.460523Z",
+     "iopub.status.idle": "2026-10-24T21:19:45.963475Z",
+     "shell.execute_reply": "2026-10-24T21:19:45.962966Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1590 features in 14 days\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sensor</th>\n",
+       "      <th>observed, ATMS swath</th>\n",
+       "      <th>observed, CrIS swath</th>\n",
+       "      <th>resolved, ATMS</th>\n",
+       "      <th>resolved, CrIS</th>\n",
+       "      <th>resolved, Future MW</th>\n",
+       "      <th>resolved, Future IR</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>satellites</th>\n",
+       "      <th>design</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <th>1 plane(s) x 1</th>\n",
+       "      <td>4.2</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>3.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
+       "      <th>1 plane(s) x 2</th>\n",
+       "      <td>8.4</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>6.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2 plane(s) x 1</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>7.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">3</th>\n",
+       "      <th>1 plane(s) x 3</th>\n",
+       "      <td>12.6</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>10.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3 plane(s) x 1</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>11.3</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>11.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>current (3 in the 13:30 plane)</th>\n",
+       "      <td>11.6</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>9.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
+       "      <th>1 plane(s) x 4</th>\n",
+       "      <td>14.8</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>12.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4 plane(s) x 1</th>\n",
+       "      <td>19.0</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>2.1</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>15.1</td>\n",
+       "      <td>15.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
+       "      <th>1 plane(s) x 5</th>\n",
+       "      <td>15.5</td>\n",
+       "      <td>13.2</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>7.8</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>13.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5 plane(s) x 1</th>\n",
+       "      <td>23.8</td>\n",
+       "      <td>19.4</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>19.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
+       "      <th>1 plane(s) x 6</th>\n",
+       "      <td>15.5</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>13.1</td>\n",
+       "      <td>13.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2 plane(s) x 3</th>\n",
+       "      <td>29.8</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>14.1</td>\n",
+       "      <td>23.4</td>\n",
+       "      <td>23.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6 plane(s) x 1</th>\n",
+       "      <td>29.3</td>\n",
+       "      <td>23.9</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>14.4</td>\n",
+       "      <td>23.2</td>\n",
+       "      <td>23.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "sensor                                     observed, ATMS swath  \\\n",
+       "satellites design                                                 \n",
+       "1          1 plane(s) x 1                                   4.2   \n",
+       "2          1 plane(s) x 2                                   8.4   \n",
+       "           2 plane(s) x 1                                  10.0   \n",
+       "3          1 plane(s) x 3                                  12.6   \n",
+       "           3 plane(s) x 1                                  14.0   \n",
+       "           current (3 in the 13:30 plane)                  11.6   \n",
+       "4          1 plane(s) x 4                                  14.8   \n",
+       "           4 plane(s) x 1                                  19.0   \n",
+       "5          1 plane(s) x 5                                  15.5   \n",
+       "           5 plane(s) x 1                                  23.8   \n",
+       "6          1 plane(s) x 6                                  15.5   \n",
+       "           2 plane(s) x 3                                  29.8   \n",
+       "           6 plane(s) x 1                                  29.3   \n",
+       "\n",
+       "sensor                                     observed, CrIS swath  \\\n",
+       "satellites design                                                 \n",
+       "1          1 plane(s) x 1                                   3.5   \n",
+       "2          1 plane(s) x 2                                   6.9   \n",
+       "           2 plane(s) x 1                                   8.0   \n",
+       "3          1 plane(s) x 3                                  10.5   \n",
+       "           3 plane(s) x 1                                  11.3   \n",
+       "           current (3 in the 13:30 plane)                   9.8   \n",
+       "4          1 plane(s) x 4                                  12.3   \n",
+       "           4 plane(s) x 1                                  15.6   \n",
+       "5          1 plane(s) x 5                                  13.2   \n",
+       "           5 plane(s) x 1                                  19.4   \n",
+       "6          1 plane(s) x 6                                  13.3   \n",
+       "           2 plane(s) x 3                                  24.0   \n",
+       "           6 plane(s) x 1                                  23.9   \n",
+       "\n",
+       "sensor                                     resolved, ATMS  resolved, CrIS  \\\n",
+       "satellites design                                                           \n",
+       "1          1 plane(s) x 1                             0.5             2.2   \n",
+       "2          1 plane(s) x 2                             0.8             4.1   \n",
+       "           2 plane(s) x 1                             1.1             4.4   \n",
+       "3          1 plane(s) x 3                             1.2             6.1   \n",
+       "           3 plane(s) x 1                             1.6             6.8   \n",
+       "           current (3 in the 13:30 plane)             0.9             5.3   \n",
+       "4          1 plane(s) x 4                             1.5             7.3   \n",
+       "           4 plane(s) x 1                             2.1             8.8   \n",
+       "5          1 plane(s) x 5                             1.5             7.8   \n",
+       "           5 plane(s) x 1                             2.6            11.1   \n",
+       "6          1 plane(s) x 6                             1.8             8.2   \n",
+       "           2 plane(s) x 3                             3.4            14.1   \n",
+       "           6 plane(s) x 1                             3.6            14.4   \n",
+       "\n",
+       "sensor                                     resolved, Future MW  \\\n",
+       "satellites design                                                \n",
+       "1          1 plane(s) x 1                                  3.4   \n",
+       "2          1 plane(s) x 2                                  6.9   \n",
+       "           2 plane(s) x 1                                  7.7   \n",
+       "3          1 plane(s) x 3                                 10.2   \n",
+       "           3 plane(s) x 1                                 11.2   \n",
+       "           current (3 in the 13:30 plane)                  9.6   \n",
+       "4          1 plane(s) x 4                                 12.0   \n",
+       "           4 plane(s) x 1                                 15.1   \n",
+       "5          1 plane(s) x 5                                 12.9   \n",
+       "           5 plane(s) x 1                                 19.0   \n",
+       "6          1 plane(s) x 6                                 13.1   \n",
+       "           2 plane(s) x 3                                 23.4   \n",
+       "           6 plane(s) x 1                                 23.2   \n",
+       "\n",
+       "sensor                                     resolved, Future IR  \n",
+       "satellites design                                               \n",
+       "1          1 plane(s) x 1                                  3.4  \n",
+       "2          1 plane(s) x 2                                  6.9  \n",
+       "           2 plane(s) x 1                                  7.9  \n",
+       "3          1 plane(s) x 3                                 10.4  \n",
+       "           3 plane(s) x 1                                 11.2  \n",
+       "           current (3 in the 13:30 plane)                  9.6  \n",
+       "4          1 plane(s) x 4                                 12.1  \n",
+       "           4 plane(s) x 1                                 15.4  \n",
+       "5          1 plane(s) x 5                                 13.1  \n",
+       "           5 plane(s) x 1                                 19.2  \n",
+       "6          1 plane(s) x 6                                 13.2  \n",
+       "           2 plane(s) x 3                                 23.7  \n",
+       "           6 plane(s) x 1                                 23.6  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from datetime import time as time_of_day\n",
+    "\n",
+    "from tatc.schemas import SunSynchronousOrbit\n",
+    "\n",
+    "TRADE_DURATION = timedelta(days=14)\n",
+    "N_DRAWS = 6  # random phases of the satellites in their planes, per design\n",
+    "# designs (planes, satellites per plane): all in one plane, one per plane, and 6 satellites in 2 planes\n",
+    "DESIGNS = [(1, n) for n in range(1, 7)] + [(n, 1) for n in range(2, 7)] + [(2, 3)]\n",
+    "\n",
+    "\n",
+    "def constellation(n_planes, per_plane, rng):\n",
+    "    \"\"\"Satellites in n_planes sun-synchronous planes at 830 km, whose ascending equator crossing times are spread evenly\n",
+    "    over 12 hours from 13:30, with per_plane satellites evenly spaced in each plane at a random phase.\"\"\"\n",
+    "    members = []\n",
+    "    for p in range(n_planes):\n",
+    "        hours = (13.5 + 12 * p / n_planes) % 24\n",
+    "        crossing = time_of_day(int(hours), round(60 * (hours % 1)) % 60)\n",
+    "        phase = rng.uniform(0, 360)\n",
+    "        for s in range(per_plane):\n",
+    "            orbit = SunSynchronousOrbit(\n",
+    "                altitude=830e3,\n",
+    "                equator_crossing_time=crossing,\n",
+    "                epoch=start,\n",
+    "                true_anomaly=(phase + 360 * s / per_plane) % 360,\n",
+    "            )\n",
+    "            members.append(\n",
+    "                Satellite(\n",
+    "                    name=f\"plane {p + 1} satellite {s + 1}\",\n",
+    "                    orbit=orbit,\n",
+    "                    instruments=[swath_instrument(\"ATMS\"), swath_instrument(\"CrIS\")],\n",
+    "                )\n",
+    "            )\n",
+    "    return members\n",
+    "\n",
+    "\n",
+    "def evaluate(members, features):\n",
+    "    \"\"\"The share (%) of the features that each sensor observes and resolves, flown on the members.\"\"\"\n",
+    "    tracks = collect_tracks(members, TRADE_DURATION)\n",
+    "    looks = collect_looks(tracks, features, {member.name: member.orbit for member in members})\n",
+    "    return assess(looks, features).groupby(\"sensor\")[[\"observed\", \"resolved\"]].mean().mul(100)\n",
+    "\n",
+    "\n",
+    "trade_features = find_features(load_nature(TRADE_DURATION))\n",
+    "rng = np.random.default_rng(0)\n",
+    "trade = pd.concat(\n",
+    "    [\n",
+    "        evaluate(constellation(n_planes, per_plane, rng), trade_features).assign(\n",
+    "            planes=n_planes, per_plane=per_plane, draw=draw\n",
+    "        )\n",
+    "        for n_planes, per_plane in DESIGNS\n",
+    "        for draw in range(N_DRAWS)\n",
+    "    ]\n",
+    ").reset_index()\n",
+    "trade[\"satellites\"] = trade.planes * trade.per_plane\n",
+    "trade[\"design\"] = trade.planes.astype(str) + \" plane(s) x \" + trade.per_plane.astype(str)\n",
+    "# the current constellation over the same two weeks, for reference\n",
+    "current = evaluate(satellites, trade_features).assign(satellites=3, design=\"current (3 in the 13:30 plane)\")\n",
+    "\n",
+    "# features observed (by each swath) and resolved (by each sensor), % of all features, averaged over the draws\n",
+    "trade_summary = pd.concat([trade, current.reset_index()]).pivot_table(\n",
+    "    index=[\"satellites\", \"design\"], columns=\"sensor\", values=[\"observed\", \"resolved\"]\n",
+    ")\n",
+    "trade_summary = pd.concat(\n",
+    "    [\n",
+    "        trade_summary[\"observed\"][[\"ATMS\", \"CrIS\"]].add_prefix(\"observed, \").add_suffix(\" swath\"),\n",
+    "        trade_summary[\"resolved\"][sensors.index].add_prefix(\"resolved, \"),\n",
+    "    ],\n",
+    "    axis=1,\n",
+    ")\n",
+    "print(f\"{len(trade_features)} features in {TRADE_DURATION.days} days\")\n",
+    "trade_summary.round(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d9b2cedf",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T21:19:45.965476Z",
+     "iopub.status.busy": "2026-10-24T21:19:45.965476Z",
+     "iopub.status.idle": "2026-10-24T21:19:46.211542Z",
+     "shell.execute_reply": "2026-10-24T21:19:46.211542Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x420 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "STRATEGIES = {\"separate planes\": (\"-\", \"o\"), \"one plane\": (\"--\", \"s\")}\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(11, 4.2), sharey=True)\n",
+    "for ax, (title, pair) in zip(\n",
+    "    axes, {\"Microwave (ATMS swath)\": (\"ATMS\", \"Future MW\"), \"Infrared (CrIS swath)\": (\"CrIS\", \"Future IR\")}.items()\n",
+    "):\n",
+    "    ends = []\n",
+    "    for sensor in pair:\n",
+    "        color = SENSOR_COLORS[sensor]\n",
+    "        for strategy, (ls, marker) in STRATEGIES.items():\n",
+    "            selected = trade[(trade.sensor == sensor) & ((trade.planes == 1) if strategy == \"one plane\" else (trade.per_plane == 1))]\n",
+    "            stats = selected.groupby(\"satellites\")[\"resolved\"].agg([\"mean\", \"std\"])\n",
+    "            ax.fill_between(stats.index, stats[\"mean\"] - stats[\"std\"], stats[\"mean\"] + stats[\"std\"], color=color, alpha=0.12, lw=0)\n",
+    "            ax.plot(stats.index, stats[\"mean\"], ls=ls, marker=marker, ms=6, lw=2, color=color, mec=\"white\", mew=1.5)\n",
+    "            ends.append([stats[\"mean\"].iloc[-1], f\"{sensor}, {strategy}\"])\n",
+    "        two_planes = trade[(trade.sensor == sensor) & (trade.planes == 2) & (trade.per_plane == 3)][\"resolved\"].mean()\n",
+    "        ax.plot(6, two_planes, marker=\"D\", ms=8, color=\"white\", mec=color, mew=2, zorder=3)\n",
+    "    # direct labels at the line ends, nudged apart so that they do not overlap\n",
+    "    ends.sort()\n",
+    "    for i in range(1, len(ends)):\n",
+    "        ends[i][0] = max(ends[i][0], ends[i - 1][0] + 1.4)\n",
+    "    for y, label in ends:\n",
+    "        ax.annotate(label, (6, y), xytext=(10, 0), textcoords=\"offset points\", va=\"center\", fontsize=8, color=INK)\n",
+    "    # the features observed at any resolution, with separate planes: the most that any sensor can resolve\n",
+    "    observed = trade[(trade.sensor == pair[0]) & (trade.per_plane == 1)].groupby(\"satellites\")[\"observed\"].mean()\n",
+    "    ax.plot(observed.index, observed, color=GRAY, lw=1, ls=\":\")\n",
+    "    ax.set(title=title, xlabel=\"Satellites\", xticks=range(1, 7), xlim=(0.7, 8.9))\n",
+    "    ax.title.set_fontsize(10)\n",
+    "axes[0].set(ylabel=\"Features resolved (% of all features)\", ylim=(0, None))\n",
+    "legend = [\n",
+    "    plt.Line2D([], [], color=GRAY, ls=\"-\", marker=\"o\", mec=\"white\", label=\"separate planes\"),\n",
+    "    plt.Line2D([], [], color=GRAY, ls=\"--\", marker=\"s\", mec=\"white\", label=\"one plane\"),\n",
+    "    plt.Line2D([], [], color=GRAY, ls=\"none\", marker=\"D\", mfc=\"white\", mew=2, label=\"2 planes x 3 satellites\"),\n",
+    "    plt.Line2D([], [], color=GRAY, ls=\":\", label=\"observed at any resolution (separate planes)\"),\n",
+    "]\n",
+    "fig.legend(handles=legend, loc=\"lower center\", ncol=4, fontsize=8, frameon=False)\n",
+    "fig.suptitle(\n",
+    "    f\"Resolved features by constellation design ({TRADE_DURATION.days} days, mean and standard deviation over {N_DRAWS} phases)\",\n",
+    "    fontsize=11,\n",
+    ")\n",
+    "fig.tight_layout(rect=(0, 0.06, 1, 1))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d240737",
+   "metadata": {},
+   "source": [
+    "Over the two weeks, the study region has 1,590 features. The current constellation observes 12% of them in the ATMS swath and 10% in the CrIS swath, and resolves 1% (ATMS), 5% (CrIS), and 10% (either future sensor), somewhat different from the two days above. Evenly spaced in its plane, a constellation of three satellites does slightly better (13% and 11% observed) than the current one, whose satellites are spaced unevenly.\n",
+    "\n",
+    "* **Satellites in one plane saturate.** Adding satellites to a single plane observes more features up to about four satellites, and then hardly any more (15 to 16% in the ATMS swath for four to six satellites), because they all observe at the same local times (near 13:30 and 01:30).\n",
+    "* **A second plane gives most of the benefit.** Six satellites in separate planes observe 29% of the features, almost twice as many as six in one plane. Six satellites in two planes (three in each) observe as many (30%): what matters is that the satellites do not duplicate each other's local times, not how many planes there are.\n",
+    "* **Planes and resolution trade against each other.** With heritage sensors, six satellites in separate planes resolve 14% of the features with CrIS, more than six satellites with a future infrared sounder in one plane (13%). Finer resolution pays off fully only with separate planes: a future sounder on six satellites in separate (or two) planes resolves 23 to 24% of the features: nearly all of those observed in the CrIS swath, and four fifths of those in the wider ATMS swath.\n",
+    "* **ATMS is limited by resolution, not sampling.** More planes raise the share resolved by ATMS only from 2% to 4%, because its resolution element is larger than most features wherever they are observed.\n",
+    "\n",
+    "The spread between the random phases (the shaded bands) is small compared with these differences. The trade holds the sensors and the altitude fixed; their swaths, altitudes, and resolutions, and orbits other than sun-synchronous ones, are further dimensions of a full trade study."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e0fdc10",
+   "metadata": {},
+   "source": [
+    "## Limitations\n",
+    "\n",
+    "* **Resolution is geometric.** A sounder's resolution depends on its channels, the retrieval, and the scene. The resolution element here is the larger of the nominal footprint and the sample spacing, scaled by the viewing geometry, without modeling the response (point spread function) of each footprint, the overlap of neighbouring footprints, or where the gaps between the views of CrIS fall on a feature.\n",
+    "* **Infrared sounders do not see through cloud.** Cold cloud tops, the features here, are observed by CrIS at their tops but, unlike microwave sounders, it does not observe the atmosphere beneath. This is not modeled.\n",
+    "* **The future sensors are assumed.** They share the swaths and platforms of ATMS and CrIS and a nominal resolution, and sample in proportion to their footprints, which requires many more views per scan (about 20 times as many for a future infrared sounder as for CrIS). Their own swaths and altitudes are open design variables; the constellation trade varies only the number of satellites and their orbit planes, at the altitude at which the nominal resolutions apply.\n",
+    "* **One realization.** Two days of one nature run year are a small sample (the constellation trade uses two weeks, over which the shares of features that the current constellation resolves differ from those of the two days by up to 3 percentage points). Extend `duration` and `start` to the whole season (mid-June to mid-September) and other years of the Nature Run for statistics.\n",
+    "* **The minimum feature size is a choice.** Features smaller than the Nature Run's effective resolution (`EFFECTIVE_RESOLUTION_KM`, four grid spacings by default) are excluded, about half of the patches of two or more grid cells. The G5NR evaluation gives no single value, and a smaller one would admit features whose structure is set by the model's damping of small scales rather than its physics.\n",
+    "* **Features are snapshots.** A convective system that persists is a feature in each 30-minute frame, and the features of a frame are not tracked from frame to frame.\n",
+    "* **The Nature Run is not reality.** Its convection is simulated and its feature statistics differ from those observed, so the results show the effect of the observing systems on a nature run, which is the point of an OSSE, rather than a forecast of real observations."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/index.rst b/docs/examples/index.rst
index 14aa3de3..5069d11d 100644
--- a/docs/examples/index.rst
+++ b/docs/examples/index.rst
@@ -19,6 +19,8 @@ The following provide examples of TAT-C use in a Jupyter notebook environment.
   CollectRO.ipynb
   CollectLimb.ipynb
   CollectSpace.ipynb
+  RunOSSEMonsoon.ipynb
+  RunOSSEHurricane.ipynb
 
 Note that running the above examples in a local Python environment requires additional dependencies which can be installed via::
 
diff --git a/docs/validation/ValidateImagerATMS.ipynb b/docs/validation/ValidateImagerATMS.ipynb
index d038a91b..2449b609 100644
--- a/docs/validation/ValidateImagerATMS.ipynb
+++ b/docs/validation/ValidateImagerATMS.ipynb
@@ -14,15 +14,16 @@
     "* an `Instrument` with a **field of regard**, the cone of view angles from nadir within which a point can be observed. Its instantaneous footprint is a circle, and its swath is the band that circle traces.\n",
     "* a `PointedInstrument` with cross-track and along-track **fields of view**, a rectangular (or elliptical) view that can be divided into a grid of pixels.\n",
     "\n",
-    "TAT-C does not model the scan mechanism. Every pixel in a view is observed at the same instant, and the along-track field of view stands in for the time the instrument takes to sweep the ground: tailored to an analysis time step, consecutive views tile the swath. The comparison asks five questions:\n",
+    "TAT-C does not model the scan mechanism. Every pixel in a view is observed at the same instant, and the along-track field of view stands in for the time the instrument takes to sweep the ground: tailored to an analysis time step, consecutive views tile the swath. The comparison asks six questions:\n",
     "\n",
     "1. **Orbit.** Does TAT-C's propagation of the operational two-line element (TLE) set reproduce NOAA-20's position?\n",
     "2. **Scan geometry.** Which view angles and which along-track reference direction reproduce ATMS's actual scan?\n",
     "3. **Beam geolocation.** How closely do TAT-C's pixel positions (`collect_ground_pixels`) match ATMS's beam centers, and how much of the difference comes from scan timing, which TAT-C does not model?\n",
     "4. **Swath.** Does TAT-C's swath (`compute_ground_track`) match the area ATMS samples, for time steps from one scan to two minutes?\n",
     "5. **Daily observations.** Does `collect_observations` predict which points ATMS observes in a day, when, and at what elevation angle?\n",
+    "6. **Footprint size.** How large are ATMS footprints (its horizontal resolution), and how do they grow toward the swath edge?\n",
     "\n",
-    "A companion notebook, [Imager Validation: NOAA-20 VIIRS](ValidateImagerVIIRS.ipynb), repeats the exercise for a visible and infrared imager on the same spacecraft."
+    "A companion notebook, [Imager Validation: NOAA-20 VIIRS](ValidateImagerVIIRS.ipynb), repeats the exercise for a visible and infrared imager on the same spacecraft, and [Imager Validation: NOAA-20 CrIS](ValidateImagerCrIS.ipynb) for an infrared sounder."
    ]
   },
   {
@@ -1634,6 +1635,397 @@
     "The observation times differ. The nadir `Instrument` reports the time of closest approach, which is about 11 s (95th percentile) from the time ATMS actually scans a point near the swath edges, because the scan line is rotated from the perpendicular to the ground track (Test 2). A `PointedInstrument` in the Earth-fixed frame has the same offset. In the inertial frame, TAT-C's epochs match the actual scan times to within 0.26 s (95th percentile), and the elevation angles at those times to within 0.02 deg."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "a7c1e601",
+   "metadata": {},
+   "source": [
+    "## Test 6: Footprint Size\n",
+    "\n",
+    "The ATMS geolocation file does not report footprint dimensions. A footprint is the projection of a beam onto the surface. ATMS has beamwidths (full width at half maximum) of 5.2 deg for channels 1 and 2 (23.8 and 31.4 GHz), 2.2 deg for channels 3 to 16 (the 50 to 60 GHz temperature sounding channels and 88.2 GHz), and 1.1 deg for channels 17 to 22 (165.5 and 183.3 GHz), corresponding to about 75, 32, and 16 km at nadir ([ATMS documentation at GES DISC](https://www.earthdata.nasa.gov/data/catalog/ges-disc-snppatmsl1b-2); [ECMWF](https://www.ecmwf.int/sites/default/files/elibrary/2012/8301-evaluation-and-assimilation-atms-data-ecmwf-system.pdf)). These are published specifications, not derived from the data. The 2.2 deg beam, used here, is that of the temperature sounding channels.\n",
+    "\n",
+    "The along-track dimension of a footprint is the slant range times the beamwidth, and its cross-track dimension is longer by the secant of the satellite zenith angle at the surface (on a flat surface). The reported zenith angles and slant ranges, which describe the viewing geometry, are therefore compared first with TAT-C's: the zenith angle from the satellite's elevation as seen from each pixel (`collect_ground_pixels` with `sat_altaz=True`), and the slant range from the zenith angle and the sub-satellite height on a spherical Earth. The reported slant range is the distance from the spacecraft position at each beam's sample time to the beam center. The same pixels as in Test 3 (the fitted, inertial configuration) are used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "a7c1e602",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-15T18:50:58.824854Z",
+     "iopub.status.busy": "2026-10-15T18:50:58.824854Z",
+     "iopub.status.idle": "2026-10-15T18:51:15.626878Z",
+     "shell.execute_reply": "2026-10-15T18:51:15.626878Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n</th>\n",
+       "      <th>median</th>\n",
+       "      <th>p95 |x|</th>\n",
+       "      <th>max |x|</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>zenith angle, TAT-C minus reported (deg)</th>\n",
+       "      <td>31104.0</td>\n",
+       "      <td>-0.001</td>\n",
+       "      <td>0.028</td>\n",
+       "      <td>0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>slant range, TAT-C minus reported (km)</th>\n",
+       "      <td>31104.0</td>\n",
+       "      <td>-0.053</td>\n",
+       "      <td>0.951</td>\n",
+       "      <td>2.682</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>equivalent diameter, TAT-C minus reported (km)</th>\n",
+       "      <td>31104.0</td>\n",
+       "      <td>-0.002</td>\n",
+       "      <td>0.079</td>\n",
+       "      <td>0.235</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                      n  median  p95 |x|  \\\n",
+       "zenith angle, TAT-C minus reported (deg)        31104.0  -0.001    0.028   \n",
+       "slant range, TAT-C minus reported (km)          31104.0  -0.053    0.951   \n",
+       "equivalent diameter, TAT-C minus reported (km)  31104.0  -0.002    0.079   \n",
+       "\n",
+       "                                                max |x|  \n",
+       "zenith angle, TAT-C minus reported (deg)          0.050  \n",
+       "slant range, TAT-C minus reported (km)            2.682  \n",
+       "equivalent diameter, TAT-C minus reported (km)    0.235  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# TAT-C's satellite zenith angle (from the satellite's elevation as seen from each pixel) at the fitted configuration, in ATMS beam order\n",
+    "track6 = collect_ground_pixels(\n",
+    "    noaa20.model_copy(update={\"instruments\": [configs[\"fitted, inertial\"]]}), times3, sat_altaz=True\n",
+    ")\n",
+    "zenith_model = (90 - track6[\"sat_alt\"].to_numpy().reshape(len(i3), N_BEAMS))[:, ::-1]\n",
+    "zenith_reported = beam_zenith[i3]\n",
+    "range_reported = np.linalg.norm(beam_xyz[i3] - beam_sc[i3], axis=-1)\n",
+    "altitude6 = wgs84.geographic_position_of(orbit.get_orbit_track(times3)).elevation.m\n",
+    "\n",
+    "\n",
+    "def slant_range(altitude, zenith, radius=EARTH_MEAN_RADIUS):\n",
+    "    \"\"\"Slant range (m) from a satellite to a surface point at a satellite zenith angle (deg), on a spherical Earth.\"\"\"\n",
+    "    c = radius * np.cos(np.radians(zenith))\n",
+    "    return -c + np.sqrt(c**2 + 2 * radius * altitude + altitude**2)\n",
+    "\n",
+    "\n",
+    "def footprint_size(range_m, zenith, beamwidth):\n",
+    "    \"\"\"Footprint (along-track, cross-track, equivalent-area diameter) in km of a circular beam (full width at half maximum in deg).\"\"\"\n",
+    "    along = range_m * np.radians(beamwidth) / 1e3\n",
+    "    cross = along / np.cos(np.radians(zenith))\n",
+    "    return along, cross, np.sqrt(along * cross)\n",
+    "\n",
+    "\n",
+    "BEAMWIDTH = 2.2  # deg, channels 3 to 16 (5.2 deg for channels 1 and 2, 1.1 deg for channels 17 to 22)\n",
+    "range_model = slant_range(altitude6[:, np.newaxis], zenith_model)\n",
+    "pd.DataFrame(\n",
+    "    {\n",
+    "        \"zenith angle, TAT-C minus reported (deg)\": summarize(zenith_model - zenith_reported),\n",
+    "        \"slant range, TAT-C minus reported (km)\": summarize((range_model - range_reported) / 1e3),\n",
+    "        \"equivalent diameter, TAT-C minus reported (km)\": summarize(\n",
+    "            footprint_size(range_model, zenith_model, BEAMWIDTH)[2]\n",
+    "            - footprint_size(range_reported, zenith_reported, BEAMWIDTH)[2]\n",
+    "        ),\n",
+    "    }\n",
+    ").T.round(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a7c1e603",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-15T18:51:15.626878Z",
+     "iopub.status.busy": "2026-10-15T18:51:15.626878Z",
+     "iopub.status.idle": "2026-10-15T18:51:15.784263Z",
+     "shell.execute_reply": "2026-10-15T18:51:15.784263Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>scan angle (deg)</th>\n",
+       "      <th>zenith angle (deg)</th>\n",
+       "      <th>range (km)</th>\n",
+       "      <th>along-track size (km)</th>\n",
+       "      <th>cross-track size (km)</th>\n",
+       "      <th>equivalent diameter (km)</th>\n",
+       "      <th>cross-track spacing (km)</th>\n",
+       "      <th>along-track spacing (km)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beam</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>52.8</td>\n",
+       "      <td>64.4</td>\n",
+       "      <td>1602.0</td>\n",
+       "      <td>61.5</td>\n",
+       "      <td>142.2</td>\n",
+       "      <td>93.5</td>\n",
+       "      <td>68.1</td>\n",
+       "      <td>17.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>39.5</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>1140.8</td>\n",
+       "      <td>43.8</td>\n",
+       "      <td>63.1</td>\n",
+       "      <td>52.6</td>\n",
+       "      <td>31.1</td>\n",
+       "      <td>17.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>26.2</td>\n",
+       "      <td>29.9</td>\n",
+       "      <td>949.9</td>\n",
+       "      <td>36.5</td>\n",
+       "      <td>42.1</td>\n",
+       "      <td>39.2</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>17.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>838.8</td>\n",
+       "      <td>32.2</td>\n",
+       "      <td>32.2</td>\n",
+       "      <td>32.2</td>\n",
+       "      <td>16.2</td>\n",
+       "      <td>17.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>-0.5</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>838.8</td>\n",
+       "      <td>32.2</td>\n",
+       "      <td>32.2</td>\n",
+       "      <td>32.2</td>\n",
+       "      <td>16.3</td>\n",
+       "      <td>17.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>-26.1</td>\n",
+       "      <td>29.8</td>\n",
+       "      <td>949.1</td>\n",
+       "      <td>36.4</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>39.1</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>17.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>-39.4</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>1139.4</td>\n",
+       "      <td>43.8</td>\n",
+       "      <td>62.9</td>\n",
+       "      <td>52.5</td>\n",
+       "      <td>32.5</td>\n",
+       "      <td>17.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>-52.8</td>\n",
+       "      <td>64.3</td>\n",
+       "      <td>1599.5</td>\n",
+       "      <td>61.4</td>\n",
+       "      <td>141.7</td>\n",
+       "      <td>93.3</td>\n",
+       "      <td>67.9</td>\n",
+       "      <td>17.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      scan angle (deg)  zenith angle (deg)  range (km)  along-track size (km)  \\\n",
+       "beam                                                                            \n",
+       "0                 52.8                64.4      1602.0                   61.5   \n",
+       "12                39.5                46.0      1140.8                   43.8   \n",
+       "24                26.2                29.9       949.9                   36.5   \n",
+       "47                 0.6                 0.7       838.8                   32.2   \n",
+       "48                -0.5                 0.6       838.8                   32.2   \n",
+       "71               -26.1                29.8       949.1                   36.4   \n",
+       "83               -39.4                46.0      1139.4                   43.8   \n",
+       "95               -52.8                64.3      1599.5                   61.4   \n",
+       "\n",
+       "      cross-track size (km)  equivalent diameter (km)  \\\n",
+       "beam                                                    \n",
+       "0                     142.2                      93.5   \n",
+       "12                     63.1                      52.6   \n",
+       "24                     42.1                      39.2   \n",
+       "47                     32.2                      32.2   \n",
+       "48                     32.2                      32.2   \n",
+       "71                     42.0                      39.1   \n",
+       "83                     62.9                      52.5   \n",
+       "95                    141.7                      93.3   \n",
+       "\n",
+       "      cross-track spacing (km)  along-track spacing (km)  \n",
+       "beam                                                      \n",
+       "0                         68.1                      17.4  \n",
+       "12                        31.1                      17.7  \n",
+       "24                        21.0                      17.7  \n",
+       "47                        16.2                      17.7  \n",
+       "48                        16.3                      17.7  \n",
+       "71                        21.5                      17.7  \n",
+       "83                        32.5                      17.7  \n",
+       "95                        67.9                      17.4  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# measured spacing (km) between adjacent beam centers within a scan and between the same beam on consecutive scans\n",
+    "spacing_cross = np.median(np.linalg.norm(np.diff(beam_xyz[i4], axis=1), axis=-1), axis=0) / 1e3\n",
+    "spacing_cross = np.append(spacing_cross, spacing_cross[-1])\n",
+    "spacing_along = np.median(np.linalg.norm(np.diff(beam_xyz, axis=0), axis=-1)[contiguous], axis=0) / 1e3\n",
+    "along_r, cross_r, equivalent_r = footprint_size(range_reported, zenith_reported, BEAMWIDTH)\n",
+    "rows = [0, 12, 24, 47, 48, 71, 83, 95]\n",
+    "table6 = pd.DataFrame(\n",
+    "    {\n",
+    "        \"scan angle (deg)\": measured[rows],\n",
+    "        \"zenith angle (deg)\": zenith_reported.mean(axis=0)[rows],\n",
+    "        \"range (km)\": range_reported.mean(axis=0)[rows] / 1e3,\n",
+    "        \"along-track size (km)\": along_r.mean(axis=0)[rows],\n",
+    "        \"cross-track size (km)\": cross_r.mean(axis=0)[rows],\n",
+    "        \"equivalent diameter (km)\": equivalent_r.mean(axis=0)[rows],\n",
+    "        \"cross-track spacing (km)\": spacing_cross[rows],\n",
+    "        \"along-track spacing (km)\": spacing_along[rows],\n",
+    "    },\n",
+    "    index=pd.Index(rows, name=\"beam\"),\n",
+    ")\n",
+    "table6.round(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "a7c1e604",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-15T18:51:15.784263Z",
+     "iopub.status.busy": "2026-10-15T18:51:15.784263Z",
+     "iopub.status.idle": "2026-10-15T18:51:15.879292Z",
+     "shell.execute_reply": "2026-10-15T18:51:15.879292Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(7, 3.6))\n",
+    "ax.plot(np.arange(N_BEAMS), along_r.mean(axis=0), color=BLUE, label=\"along-track size\")\n",
+    "ax.plot(np.arange(N_BEAMS), cross_r.mean(axis=0), color=ORANGE, label=\"cross-track size\")\n",
+    "ax.plot(np.arange(N_BEAMS), equivalent_r.mean(axis=0), color=INK, label=\"equivalent diameter\")\n",
+    "ax.plot(np.arange(N_BEAMS), spacing_cross, color=GRAY, ls=\"--\", label=\"cross-track spacing of beams\")\n",
+    "ax.plot(np.arange(N_BEAMS), spacing_along, color=GRAY, ls=\":\", label=\"along-track spacing of scans\")\n",
+    "ax.set(xlabel=\"ATMS beam index (0 = left edge)\", ylabel=\"Size or spacing (km)\", title=\"ATMS footprint size (2.2 deg beam)\")\n",
+    "ax.legend(frameon=False, fontsize=8)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7c1e605",
+   "metadata": {},
+   "source": [
+    "TAT-C's viewing geometry reproduces the reported zenith angles to 0.03 deg and slant ranges to 1 km (95th percentile), so footprint sizes follow to better than 0.1 km. The nadir footprint of the 2.2 deg beam is 32.2 km, confirming the nominal 32 km. Footprints grow toward the swath edge as the range lengthens (to 1,600 km, compared with 839 km at nadir) and the view slants (to a zenith angle of 64 deg): at the outermost beams they are 61 km along track and 142 km cross track, an equivalent diameter (that of a circle of the same area) of 93 km, about 2.9 times that at nadir. This is a stronger growth than for CrIS, whose wider swath edge is reached at a smaller zenith angle (58 deg; see [Imager Validation: NOAA-20 CrIS](ValidateImagerCrIS.ipynb)), and it is the reason that the resolution of a cross-track scanner is best described by its footprint at the location of each observation rather than at nadir.\n",
+    "\n",
+    "Beams are sampled 16 km apart across track at nadir (growing to 68 km at the swath edge) and 17.7 km apart along track, so the 32 km beams of the temperature sounding channels overlap (oversample) their neighbors by about a factor of two at nadir. The across-track spacing grows more slowly than the footprint, so the oversampling persists across the swath."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "cb2698a1",
@@ -1648,6 +2040,7 @@
     "| 3. Beam geolocation (`collect_ground_pixels`) | up to 90 km along track at the edges with the default Earth-fixed frame; about 5 km with the inertial frame (scan timing); 0.7 km median with fitted scan angles and per-beam sample times |\n",
     "| 4. Swath (`compute_ground_track`) | swath width 11 km narrow with the nominal field of regard, within 2 km (median) with the measured one; one-orbit IoU 0.99 for time steps from 8/3 to 128 s |\n",
     "| 5. Daily observations (`collect_observations`) | more than 99% of passes matched for all instruments; epochs within 0.26 s (95th percentile) for an inertial-frame `PointedInstrument`, vs. 11 s for a nadir `Instrument` |\n",
+    "| 6. Footprint size | zenith angles within 0.03 deg and slant ranges within 1 km (95th percentile) of reported; 32.2 km footprint at nadir for the 2.2 deg beam, growing to an equivalent diameter of 93 km at the swath edge |\n",
     "\n",
     "**Along-track reference direction.** The largest difference between TAT-C's default imager geometry and ATMS is the orientation of the scan. Like most Earth observation spacecraft without yaw steering, NOAA-20 keeps its attitude aligned with the orbit plane, so the scan is perpendicular to the inertial velocity, while TAT-C's default view is perpendicular to the ground track (the Earth-fixed velocity, as for a yaw-steered spacecraft). Setting `velocity_frame=VelocityFrame.INERTIAL` on a `PointedInstrument` removes along-track errors of up to 90 km at the swath edges and observation time errors of about 10 s.\n",
     "\n",
@@ -1655,7 +2048,10 @@
     "\n",
     "**Instrument parameters.** Nominal scan angles produce a swath about 11 km narrower than observed; the effective spacing and roll fitted from the geolocation close the gap. For a nadir `Instrument`, the swath width also depends on the satellite's altitude, which varies by about 30 km over NOAA-20's orbit (mostly from Earth's oblateness). `collect_observations` refines its access periods to the times when a point's angle from nadir equals half the field of regard, so they follow the actual altitude (an earlier approximation, a minimum elevation angle at the apogee radius minus the mean Earth radius, narrowed the swath at middle and high latitudes and missed 37 rather than 22 observed passes).\n",
     "\n",
-    "**Changes to TAT-C.** This validation led to the `velocity_frame` option and exposed two problems with the rectangular `PointedInstrument`, which have been fixed: rectangular footprint polygons for wide, elongated views were inaccurate (misplaced corners and too few points along the long sides), and `collect_observations` checked a pointed instrument's view only at the midpoint of each access period, missing up to 77% of observations for a view tailored to a scan period. It now checks the view at the time the view sweeps over the point and reports that time as the epoch."
+    "**Changes to TAT-C.** This validation led to the `velocity_frame` option and exposed two problems with the rectangular `PointedInstrument`, which have been fixed: rectangular footprint polygons for wide, elongated views were inaccurate (misplaced corners and too few points along the long sides), and `collect_observations` checked a pointed instrument's view only at the midpoint of each access period, missing up to 77% of observations for a view tailored to a scan period. It now checks the view at the time the view sweeps over the point and reports that time as the epoch.\n",
+    "\n",
+    "**Footprint size.** TAT-C's viewing geometry gives the zenith angle and slant range of every pixel, from which the footprint of a beam of known width follows. ATMS's 2.2 deg beam has a 32 km footprint at nadir (confirming the nominal value), which grows to about 93 km (equivalent diameter) at the swath edge. Resolution therefore depends on where in the swath a feature is observed, which the [monsoon OSSE example](../examples/RunOSSEMonsoon.ipynb) accounts for.\n",
+    ""
    ]
   }
  ],
diff --git a/docs/validation/ValidateImagerCrIS.ipynb b/docs/validation/ValidateImagerCrIS.ipynb
new file mode 100644
index 00000000..3b1c4a49
--- /dev/null
+++ b/docs/validation/ValidateImagerCrIS.ipynb
@@ -0,0 +1,2277 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "de55b147",
+   "metadata": {},
+   "source": [
+    "# Imager Validation: NOAA-20 CrIS\n",
+    "\n",
+    "This notebook validates TAT-C's representation of a cross-track sounder against the [Cross-track Infrared Sounder (CrIS)](https://www.star.nesdis.noaa.gov/jpss/CrIS.php) aboard NOAA-20 (JPSS-1). CrIS is a Fourier-transform spectrometer (3.9 to 15.4 um) that scans perpendicular to the flight direction in 30 fields of regard (FORs) spaced about 3.33 deg apart, from -48.33 to +48.33 deg (a swath about 2,200 km wide), every 8 s. Each FOR holds a 3 x 3 array of circular fields of view (FOVs), 0.963 deg in diameter (about 14 km at nadir) on a pitch of about 1.1 deg (about 16 km) ([WMO OSCAR](https://space.oscar.wmo.int/instruments/view/cris)).\n",
+    "\n",
+    "TAT-C does not model the scan mechanism. As described in the companion notebook, [Imager Validation: NOAA-20 ATMS](ValidateImagerATMS.ipynb), every pixel of a `PointedInstrument` view is observed at the same instant, and the along-track field of view stands in for the time the instrument takes to scan. CrIS is modeled as a rectangular view of 30 x 3 = 90 cross-track and 3 along-track pixels, three pixels for each FOR in each direction. The comparison asks five questions:\n",
+    "\n",
+    "1. **Orbit.** Does TAT-C's propagation of the operational two-line element (TLE) set reproduce NOAA-20's position?\n",
+    "2. **Scan geometry.** Which scan angles, array layout, and scan plane orientation reproduce the actual scan?\n",
+    "3. **Geolocation.** How closely do TAT-C's pixel positions (`collect_ground_pixels`) match the reported FOV positions, and how much of the difference comes from scan timing and the rotation of the FOV array, which TAT-C does not model?\n",
+    "4. **Swath.** Does TAT-C's swath (`compute_ground_track`) match the area CrIS samples, for time steps from one scan to two minutes?\n",
+    "5. **Footprint size.** Does the satellite viewing geometry reproduce the reported zenith angles and ranges on which the footprint size depends, and how large are CrIS footprints and how do they grow toward the swath edge? Footprint size (horizontal resolution) is the figure of merit of the [monsoon OSSE example](../examples/RunOSSEMonsoon.ipynb), which uses the parameters fit here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59cc3e3d",
+   "metadata": {},
+   "source": [
+    "## Reference Data\n",
+    "\n",
+    "NOAA distributes Joint Polar Satellite System (JPSS) data through the [NOAA Open Data Dissemination](https://www.noaa.gov/nodd) program. The NOAA-20 archive is public on AWS ([`noaa-nesdis-n20-pds`](https://registry.opendata.aws/noaa-jpss/)) and requires no account. Two products are used, as in the ATMS validation:\n",
+    "\n",
+    "* **CrIS SDR geolocation** (`CrIS-SDR-GEO`, files `GCRSO_*`): one HDF5 file per 32 s granule of four 8 s scans. For each scan, it gives the start and middle times and the spacecraft's Earth-fixed position and velocity at the middle time. For each of the 30 FORs, it gives the time, and for each of its nine FOVs, the geodetic latitude, longitude, and height of the FOV center, the satellite zenith angle, and the slant range.\n",
+    "* **Two-line elements** (`TLE-AUX`): the element sets used by the JPSS ground system, one per day with an epoch at 00:00 UTC.\n",
+    "\n",
+    "Two hours of one day (12:00 to 14:00 UTC on 2 October 2026; 225 granules, about 21 MB), more than the 101-minute orbit used in the swath test, are downloaded with `requests` and read with `h5py` (`pip install h5py`). Reduced arrays are cached in a local `data/jpss` directory, so later runs are fast."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "aa358a3a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-15T18:43:36.836904Z",
+     "iopub.status.busy": "2026-10-15T18:43:36.836904Z",
+     "iopub.status.idle": "2026-10-15T18:43:37.505734Z",
+     "shell.execute_reply": "2026-10-15T18:43:37.505734Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 43013U 17073A   26275.00000000  .00000083  00000-0  39562-4 0  2455\n",
+      "2 43013 098.7849 213.5412 0000651 045.7112 319.4843 14.19528658459636\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "scans            900\n",
+       "FORs per scan     30\n",
+       "FOVs per FOR       9\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import concurrent.futures\n",
+    "import io\n",
+    "import re\n",
+    "from datetime import datetime, timezone\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import h5py\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import requests\n",
+    "\n",
+    "DATA_DIR = Path(\"data\") / \"jpss\"\n",
+    "DATA_DIR.mkdir(parents=True, exist_ok=True)\n",
+    "BUCKET = \"https://noaa-nesdis-n20-pds.s3.amazonaws.com/\"\n",
+    "DAY = datetime(2026, 10, 2, tzinfo=timezone.utc)\n",
+    "HOURS = (12, 14)  # UTC hour range of the geolocation granules used\n",
+    "\n",
+    "\n",
+    "def list_keys(prefix):\n",
+    "    \"\"\"Keys of all objects in the bucket with a prefix.\"\"\"\n",
+    "    keys, token = [], None\n",
+    "    while True:\n",
+    "        params = {\"list-type\": \"2\", \"prefix\": prefix}\n",
+    "        if token:\n",
+    "            params[\"continuation-token\"] = token\n",
+    "        listing = requests.get(BUCKET, params=params, timeout=60).content.decode()\n",
+    "        keys += re.findall(r\"<Key>(.*?)</Key>\", listing)\n",
+    "        match = re.search(r\"<NextContinuationToken>(.*?)</NextContinuationToken>\", listing)\n",
+    "        if match is None:\n",
+    "            return keys\n",
+    "        token = match.group(1)\n",
+    "\n",
+    "\n",
+    "def fetch(key):\n",
+    "    \"\"\"Download an object, retrying transient failures.\"\"\"\n",
+    "    for attempt in range(5):\n",
+    "        try:\n",
+    "            response = requests.get(BUCKET + key, timeout=120)\n",
+    "            response.raise_for_status()\n",
+    "            return response.content\n",
+    "        except requests.RequestException:\n",
+    "            if attempt == 4:\n",
+    "                raise\n",
+    "\n",
+    "\n",
+    "TLE_FILE = DATA_DIR / f\"n20_tle_{DAY:%Y%m%d}.txt\"\n",
+    "if not TLE_FILE.exists():\n",
+    "    # the element set with an epoch at the start of the day\n",
+    "    key = next(k for k in list_keys(f\"TLE-AUX/{DAY:%Y/%m/%d}/\") if f\"Z_{DAY:%Y%m%d}000000Z_\" in k)\n",
+    "    with h5py.File(io.BytesIO(fetch(key))) as f:\n",
+    "        TLE_FILE.write_text(bytes(f[\"All_Data/TLE-AUX_All/Dataset_Array_0\"][:]).decode().strip() + \"\\n\")\n",
+    "tle = TLE_FILE.read_text().splitlines()\n",
+    "\n",
+    "CRIS_FILE = DATA_DIR / f\"cris_gcrso_{DAY:%Y%m%d}_{HOURS[0]:02d}-{HOURS[1]:02d}h.npz\"\n",
+    "CRIS_FIELDS = [\n",
+    "    \"StartTime\", \"MidTime\", \"FORTime\", \"SCPosition\", \"SCVelocity\", \"Latitude\", \"Longitude\", \"Height\",\n",
+    "    \"SatelliteZenithAngle\", \"SatelliteRange\", \"QF1_CRISSDRGEO\",\n",
+    "]\n",
+    "if not CRIS_FILE.exists():\n",
+    "\n",
+    "    def read_granule(key):\n",
+    "        with h5py.File(io.BytesIO(fetch(key))) as f:\n",
+    "            return {field: f[\"All_Data/CrIS-SDR-GEO_All/\" + field][:] for field in CRIS_FIELDS}\n",
+    "\n",
+    "    keys = [\n",
+    "        k\n",
+    "        for k in list_keys(f\"CrIS-SDR-GEO/{DAY:%Y/%m/%d}/\")\n",
+    "        if HOURS[0] <= int(re.search(r\"_t(\\d{2})\\d{5}_\", k).group(1)) < HOURS[1]\n",
+    "    ]\n",
+    "    with concurrent.futures.ThreadPoolExecutor(16) as pool:\n",
+    "        granules = list(pool.map(read_granule, keys))\n",
+    "    np.savez_compressed(CRIS_FILE, **{field: np.concatenate([g[field] for g in granules]) for field in CRIS_FIELDS})\n",
+    "\n",
+    "cris = dict(np.load(CRIS_FILE))\n",
+    "print(tle[0], tle[1], sep=\"\\n\")\n",
+    "pd.Series({\"scans\": len(cris[\"MidTime\"]), \"FORs per scan\": cris[\"Latitude\"].shape[1], \"FOVs per FOR\": cris[\"Latitude\"].shape[2]})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee30be6c",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Times in JPSS products count microseconds since 1 January 1958 including leap seconds (IDPS Epoch Time, IET); subtracting the 37 leap seconds in effect gives UTC. Scans flagged by the quality flag or with missing (fill value) positions are excluded.\n",
+    "\n",
+    "NOAA-20 is modeled with the operational TLE. The nominal CrIS scan has 30 FORs spaced `2 x 48.33 / 29` = 3.333 deg apart, and TAT-C's CrIS model has 90 pixels spaced a third of that (1.111 deg), so the cross-track field of view is 30 x 3.333 = 100 deg and the field of regard spans the outermost FOR centers, 96.66 deg. TAT-C numbers pixels starting from the right of the direction of motion and from the fore, and the layout test below determines how CrIS FOVs map to them. Helper functions convert between geodetic and Earth-fixed coordinates and decompose horizontal offsets into components along and to the right of a reference direction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ef1bc172",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-15T18:43:37.507277Z",
+     "iopub.status.busy": "2026-10-15T18:43:37.507277Z",
+     "iopub.status.idle": "2026-10-15T18:43:39.306511Z",
+     "shell.execute_reply": "2026-10-15T18:43:39.305583Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "scans used                                           900\n",
+       "scans excluded                                         0\n",
+       "first scan (UTC)        2026-10-02 12:00:05.883963+00:00\n",
+       "last scan (UTC)         2026-10-02 13:59:57.883964+00:00\n",
+       "FOV height range (m)                           -66 to 67\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from datetime import timedelta\n",
+    "\n",
+    "import cartopy.crs as ccrs\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pyproj import Transformer\n",
+    "from scipy.optimize import least_squares\n",
+    "from scipy.spatial import cKDTree\n",
+    "from skyfield.api import wgs84\n",
+    "from skyfield.framelib import itrs\n",
+    "\n",
+    "from tatc.analysis import collect_ground_pixels, compute_ground_track\n",
+    "from tatc.constants import EARTH_MEAN_RADIUS, EARTH_ROTATION_RATE\n",
+    "from tatc.schemas import GeneralPerturbationsOrbit, Instrument, PointedInstrument, Satellite\n",
+    "from tatc.utils import VelocityFrame, field_of_regard_to_swath_width\n",
+    "\n",
+    "IET_EPOCH = pd.Timestamp(\"1958-01-01\", tz=\"UTC\")\n",
+    "LEAP_SECONDS = 37\n",
+    "N_FOR, N_FOV = 30, 9  # fields of regard per scan, fields of view per field of regard (3 x 3)\n",
+    "EDGE = 48.33  # deg, nominal scan angle of the outermost field of regard\n",
+    "FOR_SPACING = 2 * EDGE / (N_FOR - 1)  # deg\n",
+    "FOV_SPACING = FOR_SPACING / 3  # deg, spacing of the 3 x 3 array within a field of regard\n",
+    "FOV_DIAMETER = 0.963  # deg, nominal instantaneous field of view (diameter)\n",
+    "SCAN_PERIOD = 8  # s\n",
+    "\n",
+    "\n",
+    "def iet_to_utc(iet):\n",
+    "    return IET_EPOCH + pd.to_timedelta(np.asarray(iet) - LEAP_SECONDS * 1_000_000, unit=\"us\")\n",
+    "\n",
+    "\n",
+    "good = (cris[\"QF1_CRISSDRGEO\"] == 0) & np.all(np.abs(cris[\"Latitude\"]) <= 90, axis=(1, 2))\n",
+    "t_mid = iet_to_utc(cris[\"MidTime\"][good])\n",
+    "t_for = iet_to_utc(cris[\"FORTime\"][good].ravel()).to_numpy().reshape(-1, N_FOR)\n",
+    "dt_for = (cris[\"FORTime\"][good] - cris[\"MidTime\"][good][:, np.newaxis]) / 1e6  # s from the middle time\n",
+    "sc_pos, sc_vel = cris[\"SCPosition\"][good].astype(float), cris[\"SCVelocity\"][good].astype(float)\n",
+    "fov_lat, fov_lon = cris[\"Latitude\"][good].astype(float), cris[\"Longitude\"][good].astype(float)\n",
+    "fov_height = cris[\"Height\"][good].astype(float) if \"Height\" in cris else np.zeros_like(fov_lat)\n",
+    "fov_zenith = cris[\"SatelliteZenithAngle\"][good].astype(float)\n",
+    "fov_range = cris[\"SatelliteRange\"][good].astype(float)\n",
+    "\n",
+    "noaa20 = Satellite(name=\"NOAA-20\", orbit=GeneralPerturbationsOrbit.from_tle(tle))\n",
+    "orbit = noaa20.orbit.to_gp_orbit()\n",
+    "\n",
+    "TO_ECEF = Transformer.from_crs(\"EPSG:4979\", \"EPSG:4978\", always_xy=True)\n",
+    "TO_GEODETIC = Transformer.from_crs(\"EPSG:4978\", \"EPSG:4979\", always_xy=True)\n",
+    "\n",
+    "\n",
+    "def ecef(lon, lat, height=0.0):\n",
+    "    \"\"\"Earth-fixed position (m) of geodetic coordinates, stacked on the last axis.\"\"\"\n",
+    "    return np.stack(TO_ECEF.transform(lon, lat, np.broadcast_to(height, np.shape(lon))), axis=-1)\n",
+    "\n",
+    "\n",
+    "def up(lon, lat):\n",
+    "    \"\"\"Geodetic up unit vector, stacked on the last axis.\"\"\"\n",
+    "    lon, lat = np.radians(lon), np.radians(lat)\n",
+    "    return np.stack([np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1)\n",
+    "\n",
+    "\n",
+    "def normalize(x):\n",
+    "    return x / np.linalg.norm(x, axis=-1, keepdims=True)\n",
+    "\n",
+    "\n",
+    "def along_right(lon, lat, offset, direction):\n",
+    "    \"\"\"Components (km) of Earth-fixed offsets along and to the right of the horizontal projection of a direction at a location.\"\"\"\n",
+    "    u = up(lon, lat)\n",
+    "    along = normalize(direction - np.sum(direction * u, axis=-1, keepdims=True) * u)\n",
+    "    right = np.cross(along, u)\n",
+    "    return np.sum(offset * along, axis=-1) / 1e3, np.sum(offset * right, axis=-1) / 1e3\n",
+    "\n",
+    "\n",
+    "def inertial_velocity(position, velocity):\n",
+    "    \"\"\"Inertial velocity expressed in Earth-fixed coordinates.\"\"\"\n",
+    "    return velocity + EARTH_ROTATION_RATE * np.stack(\n",
+    "        [-position[..., 1], position[..., 0], np.zeros(position.shape[:-1])], axis=-1\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "def summarize(x):\n",
+    "    \"\"\"Summary statistics of a residual sample.\"\"\"\n",
+    "    x = np.asarray(x, dtype=float)\n",
+    "    x = x[np.isfinite(x)]\n",
+    "    return pd.Series(\n",
+    "        {\n",
+    "            \"n\": x.size,\n",
+    "            \"median\": np.median(x),\n",
+    "            \"p95 |x|\": np.percentile(np.abs(x), 95),\n",
+    "            \"max |x|\": np.abs(x).max(),\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "def binned(x, y, edges, q=(5, 50, 95)):\n",
+    "    \"\"\"Percentiles of y in bins of x.\"\"\"\n",
+    "    i = np.digitize(x, edges) - 1\n",
+    "    return np.array([np.percentile(y[i == k], q) if np.any(i == k) else [np.nan] * len(q) for k in range(len(edges) - 1)])\n",
+    "\n",
+    "\n",
+    "BLUE, ORANGE, AQUA, GRAY, INK = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\"\n",
+    "plt.rcParams.update(\n",
+    "    {\n",
+    "        \"axes.spines.top\": False,\n",
+    "        \"axes.spines.right\": False,\n",
+    "        \"axes.grid\": True,\n",
+    "        \"grid.color\": \"#e6e5e0\",\n",
+    "        \"grid.linewidth\": 0.6,\n",
+    "    }\n",
+    ")\n",
+    "pd.Series(\n",
+    "    {\n",
+    "        \"scans used\": int(good.sum()),\n",
+    "        \"scans excluded\": int((~good).sum()),\n",
+    "        \"first scan (UTC)\": t_mid[0],\n",
+    "        \"last scan (UTC)\": t_mid[-1],\n",
+    "        \"FOV height range (m)\": f\"{fov_height.min():.0f} to {fov_height.max():.0f}\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ede7c83",
+   "metadata": {},
+   "source": [
+    "## Test 1: Orbit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "19b2daba",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-15T18:43:39.307489Z",
+     "iopub.status.busy": "2026-10-15T18:43:39.307489Z",
+     "iopub.status.idle": "2026-10-15T18:43:39.353939Z",
+     "shell.execute_reply": "2026-10-15T18:43:39.352945Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n</th>\n",
+       "      <th>median</th>\n",
+       "      <th>p95 |x|</th>\n",
+       "      <th>max |x|</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>along-track (km)</th>\n",
+       "      <td>180.0</td>\n",
+       "      <td>0.499</td>\n",
+       "      <td>0.688</td>\n",
+       "      <td>0.693</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cross-track, right (km)</th>\n",
+       "      <td>180.0</td>\n",
+       "      <td>0.023</td>\n",
+       "      <td>0.282</td>\n",
+       "      <td>0.287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>radial (km)</th>\n",
+       "      <td>180.0</td>\n",
+       "      <td>0.042</td>\n",
+       "      <td>0.132</td>\n",
+       "      <td>0.133</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>velocity (m/s)</th>\n",
+       "      <td>180.0</td>\n",
+       "      <td>0.590</td>\n",
+       "      <td>0.690</td>\n",
+       "      <td>0.740</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             n  median  p95 |x|  max |x|\n",
+       "along-track (km)         180.0   0.499    0.688    0.693\n",
+       "cross-track, right (km)  180.0   0.023    0.282    0.287\n",
+       "radial (km)              180.0   0.042    0.132    0.133\n",
+       "velocity (m/s)           180.0   0.590    0.690    0.740"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "i1 = np.arange(0, len(t_mid), 5)\n",
+    "p1, v1 = orbit.get_orbit_track(list(t_mid[i1])).frame_xyz_and_velocity(itrs)\n",
+    "offset1 = np.array(p1.m).T - sc_pos[i1]\n",
+    "radial1 = normalize(sc_pos[i1])\n",
+    "along1 = normalize(sc_vel[i1] - np.sum(sc_vel[i1] * radial1, axis=-1, keepdims=True) * radial1)\n",
+    "test1 = pd.DataFrame(\n",
+    "    {\n",
+    "        \"along-track (km)\": summarize(np.sum(offset1 * along1, axis=-1) / 1e3),\n",
+    "        \"cross-track, right (km)\": summarize(np.sum(offset1 * np.cross(along1, radial1), axis=-1) / 1e3),\n",
+    "        \"radial (km)\": summarize(np.sum(offset1 * radial1, axis=-1) / 1e3),\n",
+    "        \"velocity (m/s)\": summarize(np.linalg.norm(np.array(v1.m_per_s).T - sc_vel[i1], axis=-1)),\n",
+    "    }\n",
+    ").T\n",
+    "test1.round(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3468442a",
+   "metadata": {},
+   "source": [
+    "The TLE reproduces NOAA-20's position to about 0.7 km along track (95th percentile) over the two hours, and much better radially and cross-track, which is small compared with a CrIS footprint (about 14 km at nadir).\n",
+    "\n",
+    "## Test 2: Scan Geometry\n",
+    "\n",
+    "This test uses only the reference data: the reported spacecraft position and velocity and the FOV positions.\n",
+    "\n",
+    "CrIS samples each FOR for 0.2 s, so the FORs of a scan span about 5.8 s, starting at the scan's start time. The line of sight of each FOV is the vector from the spacecraft position at the FOR's time (extrapolated linearly from the reported position and velocity) to the FOV center. The lines of sight to the centers of the FORs lie in a plane (the scan plane) to within about 0.03 deg. The cross-track (scan) angle of a line of sight is its angle from nadir within this plane, positive to the left of the direction of motion (TAT-C's positive roll direction), and its along-track angle is the angle out of the plane, positive forward.\n",
+    "\n",
+    "### Scan Angles\n",
+    "\n",
+    "The scan angle of each FOR is the mean over its nine FOVs. TAT-C rotates a rolled view rigidly, so a pixel's angle from nadir is the sum of the roll angle and the pixel's angular offset from the view center; the measured angles are fit with this model to find the effective spacing and roll angle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a06ec367",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-15T18:43:39.355060Z",
+     "iopub.status.busy": "2026-10-15T18:43:39.355060Z",
+     "iopub.status.idle": "2026-10-15T18:43:39.437276Z",
+     "shell.execute_reply": "2026-10-15T18:43:39.437276Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "field of regard 0 side                        left\n",
+       "sample interval, field of regard (ms)        200.0\n",
+       "nominal spacing (deg)                     3.333103\n",
+       "fitted spacing (deg)                       3.33399\n",
+       "fitted roll (deg)                        -0.092556\n",
+       "fit residual, max |x| (deg)               0.009403\n",
+       "scan-to-scan std. dev., median (deg)      0.000875\n",
+       "left edge, measured (deg)                48.254462\n",
+       "right edge, measured (deg)              -48.431246\n",
+       "field of regard, measured (deg)          96.685708\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# spacecraft position at each field of regard's time (extrapolated linearly from the middle time)\n",
+    "sc_for = sc_pos[:, np.newaxis] + sc_vel[:, np.newaxis] * dt_for[..., np.newaxis]  # (scan, FOR, xyz)\n",
+    "fov_xyz = ecef(fov_lon, fov_lat, fov_height)  # (scan, FOR, FOV, xyz)\n",
+    "sc_lon, sc_lat, _ = TO_GEODETIC.transform(sc_for[..., 0], sc_for[..., 1], sc_for[..., 2])\n",
+    "nadir = -up(sc_lon, sc_lat)  # (scan, FOR, xyz)\n",
+    "los = normalize(fov_xyz - sc_for[:, :, np.newaxis])  # (scan, FOR, FOV, xyz)\n",
+    "\n",
+    "# the scan plane of each scan: normal to the lines of sight to the centers of its fields of regard\n",
+    "los_for = normalize(los.mean(axis=2))\n",
+    "normal = np.array([np.linalg.svd(los_for[k])[2][-1] for k in range(len(los_for))])\n",
+    "normal *= np.sign(np.sum(normal * sc_vel, axis=-1))[:, np.newaxis]  # facing forward\n",
+    "n_ = normal[:, np.newaxis]\n",
+    "# cross-track (scan) angle in the scan plane, positive to the left, and along-track angle out of it, positive forward\n",
+    "nadir_plane = normalize(nadir - np.sum(nadir * n_, axis=-1, keepdims=True) * n_)\n",
+    "left_plane = np.cross(n_, nadir_plane)\n",
+    "los_plane = los - np.sum(los * n_[:, :, np.newaxis], axis=-1, keepdims=True) * n_[:, :, np.newaxis]\n",
+    "cross_angle = np.degrees(\n",
+    "    np.arctan2(np.sum(los_plane * left_plane[:, :, np.newaxis], axis=-1), np.sum(los_plane * nadir_plane[:, :, np.newaxis], axis=-1))\n",
+    ")\n",
+    "along_angle = np.degrees(np.arcsin(np.einsum(\"kjic,kc->kji\", los, normal)))\n",
+    "for_angle = cross_angle.mean(axis=2)  # scan angle of the center of each field of regard\n",
+    "measured = np.median(for_angle, axis=0)\n",
+    "orientation = np.sign(measured[0] - measured[-1])  # +1 if field of regard 0 is on the left\n",
+    "nominal = orientation * ((N_FOR - 1) / 2 - np.arange(N_FOR)) * FOR_SPACING\n",
+    "\n",
+    "\n",
+    "def model(params, index=np.arange(N_FOR)):\n",
+    "    spacing, roll = params\n",
+    "    return roll + orientation * spacing * ((N_FOR - 1) / 2 - index)\n",
+    "\n",
+    "\n",
+    "fit = least_squares(lambda params: model(params) - measured, [FOR_SPACING, 0]).x\n",
+    "fitted_spacing, fitted_roll = fit\n",
+    "pd.Series(\n",
+    "    {\n",
+    "        \"field of regard 0 side\": \"left\" if orientation > 0 else \"right\",\n",
+    "        \"sample interval, field of regard (ms)\": np.median(np.diff(dt_for, axis=1)) * 1e3,\n",
+    "        \"nominal spacing (deg)\": FOR_SPACING,\n",
+    "        \"fitted spacing (deg)\": fitted_spacing,\n",
+    "        \"fitted roll (deg)\": fitted_roll,\n",
+    "        \"fit residual, max |x| (deg)\": np.abs(model(fit) - measured).max(),\n",
+    "        \"scan-to-scan std. dev., median (deg)\": np.median(np.std(for_angle, axis=0)),\n",
+    "        \"left edge, measured (deg)\": measured.max(),\n",
+    "        \"right edge, measured (deg)\": measured.min(),\n",
+    "        \"field of regard, measured (deg)\": measured.max() - measured.min(),\n",
+    "    }\n",
+    ").round(4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d1b31837",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-15T18:43:39.439269Z",
+     "iopub.status.busy": "2026-10-15T18:43:39.439269Z",
+     "iopub.status.idle": "2026-10-15T18:43:39.553727Z",
+     "shell.execute_reply": "2026-10-15T18:43:39.553727Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x340 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(7, 3.4))\n",
+    "ax.plot(np.arange(N_FOR), measured - nominal, color=BLUE, marker=\"o\", ms=3, lw=0, label=\"measured (median)\")\n",
+    "ax.plot(np.arange(N_FOR), model(fit) - nominal, color=ORANGE, label=f\"fit: spacing {fitted_spacing:.4f} deg, roll {fitted_roll:+.3f} deg\")\n",
+    "ax.axhline(0, color=GRAY, lw=0.8)\n",
+    "ax.set(xlabel=\"CrIS field of regard index (0 = first scanned)\", ylabel=\"Scan angle minus nominal (deg)\", title=\"CrIS scan angles\")\n",
+    "ax.legend(frameon=False)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b23451de",
+   "metadata": {},
+   "source": [
+    "The measured scan angles are stable from scan to scan (a standard deviation of about 0.001 deg) and fit the rigid-rotation model to within 0.01 deg with a spacing of 3.334 deg, matching the nominal 3.333 deg, and a roll of 0.09 deg to the right. FOR 0 is scanned first, on the left. The measured field of regard (96.69 deg) is within 0.03 deg of the nominal 96.66 deg.\n",
+    "\n",
+    "### Field of View Layout\n",
+    "\n",
+    "The offsets of each FOV from the center of its FOR, in the scan plane (cross-track, positive left) and out of it (along-track, positive forward), are taken from the two FORs nearest nadir (whose small rotations cancel). They show a square 3 x 3 array on a pitch of 1.092 deg, slightly smaller than the nominal 1.1 deg, with FOV 5 at the center. The table also gives each FOV's TAT-C pixel indices within its FOR (cross-track 0 on the right to 2 on the left, along-track 0 at the fore to 2 at the aft), used in the geolocation test."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3cd3ee82",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-15T18:43:39.555836Z",
+     "iopub.status.busy": "2026-10-15T18:43:39.555836Z",
+     "iopub.status.idle": "2026-10-15T18:43:39.569728Z",
+     "shell.execute_reply": "2026-10-15T18:43:39.569728Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FOV pitch 1.0918 deg (nominal 1.1110); central view: FOV 5\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cross-track offset (deg)</th>\n",
+       "      <th>along-track offset (deg)</th>\n",
+       "      <th>TAT-C cross-track pixel</th>\n",
+       "      <th>TAT-C along-track pixel</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FOV</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.090</td>\n",
+       "      <td>-1.096</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.003</td>\n",
+       "      <td>-1.095</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-1.098</td>\n",
+       "      <td>-1.091</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.092</td>\n",
+       "      <td>-0.003</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>-1.093</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.100</td>\n",
+       "      <td>1.094</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.003</td>\n",
+       "      <td>1.088</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>-1.092</td>\n",
+       "      <td>1.099</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     cross-track offset (deg)  along-track offset (deg)  \\\n",
+       "FOV                                                       \n",
+       "1                       1.090                    -1.096   \n",
+       "2                      -0.003                    -1.095   \n",
+       "3                      -1.098                    -1.091   \n",
+       "4                       1.092                    -0.003   \n",
+       "5                       0.000                     0.000   \n",
+       "6                      -1.093                     0.004   \n",
+       "7                       1.100                     1.094   \n",
+       "8                       0.003                     1.088   \n",
+       "9                      -1.092                     1.099   \n",
+       "\n",
+       "     TAT-C cross-track pixel  TAT-C along-track pixel  \n",
+       "FOV                                                    \n",
+       "1                          2                        2  \n",
+       "2                          1                        2  \n",
+       "3                          0                        2  \n",
+       "4                          2                        1  \n",
+       "5                          1                        1  \n",
+       "6                          0                        1  \n",
+       "7                          2                        0  \n",
+       "8                          1                        0  \n",
+       "9                          0                        0  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# offsets of each view from the center of its field of regard (deg): cross-track (positive left) and along-track (positive forward)\n",
+    "offset_cross = cross_angle - for_angle[..., np.newaxis]\n",
+    "offset_along = along_angle - along_angle.mean(axis=2, keepdims=True)\n",
+    "fov_offsets = np.stack([np.median(offset_cross, axis=0), np.median(offset_along, axis=0)], axis=-1)  # (FOR, FOV, 2)\n",
+    "central = [N_FOR // 2 - 1, N_FOR // 2]  # fields of regard nearest nadir (symmetric, so their rotations cancel)\n",
+    "reference = fov_offsets[central].mean(axis=0)  # (FOV, 2)\n",
+    "layout = pd.DataFrame(\n",
+    "    {\"cross-track offset (deg)\": reference[:, 0], \"along-track offset (deg)\": reference[:, 1]},\n",
+    "    index=pd.RangeIndex(1, N_FOV + 1, name=\"FOV\"),\n",
+    ")\n",
+    "fov_pitch = np.sort(np.hypot(*reference.T))[1:5].mean()  # distance to the four nearest neighbors of the central view\n",
+    "# TAT-C pixel indices within a field of regard: cross-track 0 (right) to 2 (left), along-track 0 (fore) to 2 (aft)\n",
+    "pixel_cross = np.round(reference[:, 0] / fov_pitch).astype(int) + 1\n",
+    "pixel_along = 1 - np.round(reference[:, 1] / fov_pitch).astype(int)\n",
+    "assert len(set(zip(pixel_cross, pixel_along))) == N_FOV\n",
+    "center = int(np.flatnonzero((pixel_cross == 1) & (pixel_along == 1))[0])  # the central view of the array\n",
+    "layout[\"TAT-C cross-track pixel\"] = pixel_cross\n",
+    "layout[\"TAT-C along-track pixel\"] = pixel_along\n",
+    "print(f\"FOV pitch {fov_pitch:.4f} deg (nominal {FOV_SPACING:.4f}); central view: FOV {center + 1}\")\n",
+    "layout.round(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6607b5bf",
+   "metadata": {},
+   "source": [
+    "The array of views turns as the scan mirror sweeps. The rotation of each field of regard's array from its layout at nadir (a rigid rotation fit to the nine views) is compared with its scan angle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b20313d0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-15T18:43:39.571887Z",
+     "iopub.status.busy": "2026-10-15T18:43:39.570728Z",
+     "iopub.status.idle": "2026-10-15T18:43:39.656251Z",
+     "shell.execute_reply": "2026-10-15T18:43:39.656251Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x340 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "rotation per degree of scan angle     1.003\n",
+       "rotation at nadir (deg)               0.065\n",
+       "fit residual, max |x| (deg)           0.052\n",
+       "rotation at the left edge (deg)      48.405\n",
+       "rotation at the right edge (deg)    -48.547\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# angle (deg) of the rotation of the array (counterclockwise in cross-track, along-track axes) that best maps the nadir layout onto each field of regard\n",
+    "p = fov_offsets  # (FOR, FOV, 2)\n",
+    "rotation = np.degrees(\n",
+    "    np.arctan2(\n",
+    "        np.sum(reference[:, 0] * p[..., 1] - reference[:, 1] * p[..., 0], axis=1),\n",
+    "        np.sum(reference[:, 0] * p[..., 0] + reference[:, 1] * p[..., 1], axis=1),\n",
+    "    )\n",
+    ")\n",
+    "rotation_fit = np.polyfit(measured, rotation, 1)\n",
+    "fig, ax = plt.subplots(figsize=(7, 3.4))\n",
+    "ax.plot(measured, rotation, color=BLUE, marker=\"o\", ms=3, lw=0, label=\"measured\")\n",
+    "ax.plot(measured, np.polyval(rotation_fit, measured), color=ORANGE, label=f\"fit: {rotation_fit[0]:+.3f} x scan angle {rotation_fit[1]:+.2f} deg\")\n",
+    "ax.axhline(0, color=GRAY, lw=0.8)\n",
+    "ax.set(xlabel=\"Scan angle (deg)\", ylabel=\"Rotation of the FOV array (deg)\", title=\"Rotation of the CrIS view array with scan angle\")\n",
+    "ax.legend(frameon=False)\n",
+    "fig.tight_layout()\n",
+    "plt.show()\n",
+    "pd.Series(\n",
+    "    {\n",
+    "        \"rotation per degree of scan angle\": rotation_fit[0],\n",
+    "        \"rotation at nadir (deg)\": rotation_fit[1],\n",
+    "        \"fit residual, max |x| (deg)\": np.abs(np.polyval(rotation_fit, measured) - rotation).max(),\n",
+    "        \"rotation at the left edge (deg)\": rotation[measured.argmax()],\n",
+    "        \"rotation at the right edge (deg)\": rotation[measured.argmin()],\n",
+    "    }\n",
+    ").round(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8bbf15cc",
+   "metadata": {},
+   "source": [
+    "The array rotates with the scan angle, one degree per degree (as for a fixed focal plane behind a scan mirror), reaching about 48 deg at the swath edges. TAT-C's pixel array is a rigid, unrotated rectangle in every FOR, so it cannot represent this rotation; the effect is to displace the FOVs in a FOR (by up to about 60 km at the swath edge, as Test 3 shows), but not the center of the array.\n",
+    "\n",
+    "### Scan Plane Orientation\n",
+    "\n",
+    "As in the ATMS validation, the scan plane's normal is compared with the horizontal direction of the Earth-fixed and inertial velocity (with yaw angles positive counterclockwise viewed from above). The tilt of the plane forward of nadir is also computed, from the angle between the plane normal and the nadir at each FOR."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "dedffedc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-15T18:43:39.658540Z",
+     "iopub.status.busy": "2026-10-15T18:43:39.657490Z",
+     "iopub.status.idle": "2026-10-15T18:43:39.787258Z",
+     "shell.execute_reply": "2026-10-15T18:43:39.786551Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n</th>\n",
+       "      <th>median</th>\n",
+       "      <th>p95 |x|</th>\n",
+       "      <th>max |x|</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>yaw vs. Earth-fixed velocity (deg)</th>\n",
+       "      <td>180.0</td>\n",
+       "      <td>-0.585</td>\n",
+       "      <td>4.046</td>\n",
+       "      <td>4.117</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yaw vs. inertial velocity (deg)</th>\n",
+       "      <td>180.0</td>\n",
+       "      <td>-0.165</td>\n",
+       "      <td>0.166</td>\n",
+       "      <td>0.166</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>line of sight out of plane (deg)</th>\n",
+       "      <td>5400.0</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>0.017</td>\n",
+       "      <td>0.034</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>scan plane tilt forward (deg)</th>\n",
+       "      <td>180.0</td>\n",
+       "      <td>0.223</td>\n",
+       "      <td>0.228</td>\n",
+       "      <td>0.229</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                         n  median  p95 |x|  max |x|\n",
+       "yaw vs. Earth-fixed velocity (deg)   180.0  -0.585    4.046    4.117\n",
+       "yaw vs. inertial velocity (deg)      180.0  -0.165    0.166    0.166\n",
+       "line of sight out of plane (deg)    5400.0   0.007    0.017    0.034\n",
+       "scan plane tilt forward (deg)        180.0   0.223    0.228    0.229"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "i2 = np.arange(0, len(t_mid), 5)\n",
+    "sub_lon, sub_lat, _ = TO_GEODETIC.transform(sc_pos[i2, 0], sc_pos[i2, 1], sc_pos[i2, 2])\n",
+    "u2 = up(sub_lon, sub_lat)\n",
+    "\n",
+    "\n",
+    "def yaw(direction, reference):\n",
+    "    \"\"\"Signed angle (deg) from the horizontal projection of a reference direction to that of a direction.\"\"\"\n",
+    "    a = normalize(reference - np.sum(reference * u2, axis=-1, keepdims=True) * u2)\n",
+    "    b = normalize(direction - np.sum(direction * u2, axis=-1, keepdims=True) * u2)\n",
+    "    return np.degrees(np.arctan2(np.sum(np.cross(a, b) * u2, axis=-1), np.sum(a * b, axis=-1)))\n",
+    "\n",
+    "\n",
+    "normal2 = normal[i2]\n",
+    "yaw_earth_fixed = yaw(normal2, sc_vel[i2])\n",
+    "yaw_inertial = yaw(normal2, inertial_velocity(sc_pos[i2], sc_vel[i2]))\n",
+    "ascending = sc_vel[i2, 2] > 0\n",
+    "out_of_plane = np.degrees(np.abs(np.arcsin(np.einsum(\"kjc,kc->kj\", los_for[i2], normal2))))\n",
+    "# tilt of the scan plane forward of nadir (positive forward), from the angle between the plane normal and the nadir at each field of regard\n",
+    "tilt = -np.degrees(np.arcsin(np.sum(nadir * normal[:, np.newaxis], axis=-1)))  # (scan, FOR)\n",
+    "scan_plane_tilt = float(np.median(tilt))\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(7, 3.6))\n",
+    "for mask, color, label in [(ascending, BLUE, \"ascending\"), (~ascending, AQUA, \"descending\")]:\n",
+    "    ax.scatter(sub_lat[mask], yaw_earth_fixed[mask], s=6, color=color, label=f\"vs. Earth-fixed velocity, {label}\")\n",
+    "ax.scatter(sub_lat, yaw_inertial, s=6, color=ORANGE, label=\"vs. inertial velocity, both\")\n",
+    "ax.axhline(0, color=GRAY, lw=0.8)\n",
+    "ax.set(xlabel=\"Sub-satellite latitude (deg)\", ylabel=\"Scan plane yaw (deg)\", title=\"Orientation of the CrIS scan plane\", ylim=(-5, 5))\n",
+    "ax.legend(frameon=False, fontsize=8, markerscale=3, loc=\"center\")\n",
+    "fig.tight_layout()\n",
+    "plt.show()\n",
+    "pd.DataFrame(\n",
+    "    {\n",
+    "        \"yaw vs. Earth-fixed velocity (deg)\": summarize(yaw_earth_fixed),\n",
+    "        \"yaw vs. inertial velocity (deg)\": summarize(yaw_inertial),\n",
+    "        \"line of sight out of plane (deg)\": summarize(out_of_plane),\n",
+    "        \"scan plane tilt forward (deg)\": summarize(np.median(tilt[i2], axis=1)),\n",
+    "    }\n",
+    ").T.round(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6b6df40",
+   "metadata": {},
+   "source": [
+    "Like ATMS, the scan plane faces the inertial velocity at all latitudes, to within a constant 0.17 deg, whereas the Earth-fixed velocity is rotated by up to about 4 deg, with opposite signs on ascending and descending passes. A `PointedInstrument` with `velocity_frame=VelocityFrame.INERTIAL` reproduces this orientation. The scan plane is also tilted 0.22 deg forward of nadir on both ascending and descending passes (about 3 km on the ground), which `PointedInstrument.pitch_angle` can reproduce.\n",
+    "\n",
+    "## Test 3: Geolocation\n",
+    "\n",
+    "TAT-C pixel positions from `collect_ground_pixels`, at the middle time of every twentieth scan, are compared with the reported FOV positions. Five configurations isolate the contributions of the along-track reference direction, the scan angles, the scan plane tilt, and scan timing:\n",
+    "\n",
+    "* **Nominal, Earth-fixed:** 3.333 deg FOR spacing with TAT-C's default velocity frame.\n",
+    "* **Nominal, inertial:** the same with `velocity_frame=VelocityFrame.INERTIAL`.\n",
+    "* **Fitted, inertial:** the FOR spacing, roll angle, and FOV pitch fitted in Test 2.\n",
+    "* **Fitted, inertial, tilted:** the same with the scan plane tilt as `pitch_angle`.\n",
+    "* **Fitted, inertial, tilted, sample times:** the same, but with each FOR projected at its own sample time rather than all at the scan's middle time. TAT-C does not do this; it is computed here (with `PointedInstrument.compute_projected_pixel_position`) to quantify the effect of scan timing.\n",
+    "\n",
+    "Residuals (TAT-C minus CrIS) are decomposed along and to the right of the ground track (the horizontal direction of the reported Earth-fixed velocity). The first table gives the residuals of the central FOV of each FOR, the center of its array; the second, those of all nine FOVs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "853055c9",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-15T18:43:39.788820Z",
+     "iopub.status.busy": "2026-10-15T18:43:39.788820Z",
+     "iopub.status.idle": "2026-10-15T18:43:43.540486Z",
+     "shell.execute_reply": "2026-10-15T18:43:43.540486Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>median</th>\n",
+       "      <th>p95 |x|</th>\n",
+       "      <th>max |x|</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">nominal, Earth-fixed</th>\n",
+       "      <th>along</th>\n",
+       "      <td>-2.83</td>\n",
+       "      <td>59.21</td>\n",
+       "      <td>93.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>right</th>\n",
+       "      <td>-1.73</td>\n",
+       "      <td>4.25</td>\n",
+       "      <td>6.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>horizontal</th>\n",
+       "      <td>12.42</td>\n",
+       "      <td>59.33</td>\n",
+       "      <td>93.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">nominal, inertial</th>\n",
+       "      <th>along</th>\n",
+       "      <td>-2.88</td>\n",
+       "      <td>21.22</td>\n",
+       "      <td>22.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>right</th>\n",
+       "      <td>-1.77</td>\n",
+       "      <td>3.61</td>\n",
+       "      <td>4.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>horizontal</th>\n",
+       "      <td>10.13</td>\n",
+       "      <td>21.51</td>\n",
+       "      <td>23.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">fitted, inertial</th>\n",
+       "      <th>along</th>\n",
+       "      <td>-2.92</td>\n",
+       "      <td>21.23</td>\n",
+       "      <td>22.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>right</th>\n",
+       "      <td>-0.04</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>0.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>horizontal</th>\n",
+       "      <td>9.96</td>\n",
+       "      <td>21.23</td>\n",
+       "      <td>22.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">fitted, inertial, tilted</th>\n",
+       "      <th>along</th>\n",
+       "      <td>0.40</td>\n",
+       "      <td>19.04</td>\n",
+       "      <td>19.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>right</th>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>0.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>horizontal</th>\n",
+       "      <td>10.00</td>\n",
+       "      <td>19.04</td>\n",
+       "      <td>19.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">fitted, inertial, tilted, sample times</th>\n",
+       "      <th>along</th>\n",
+       "      <td>0.45</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>0.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>right</th>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.33</td>\n",
+       "      <td>0.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>horizontal</th>\n",
+       "      <td>0.48</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>0.74</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   median  p95 |x|  max |x|\n",
+       "nominal, Earth-fixed                   along        -2.83    59.21    93.66\n",
+       "                                       right        -1.73     4.25     6.02\n",
+       "                                       horizontal   12.42    59.33    93.69\n",
+       "nominal, inertial                      along        -2.88    21.22    22.95\n",
+       "                                       right        -1.77     3.61     4.90\n",
+       "                                       horizontal   10.13    21.51    23.45\n",
+       "fitted, inertial                       along        -2.92    21.23    22.86\n",
+       "                                       right        -0.04     0.26     0.49\n",
+       "                                       horizontal    9.96    21.23    22.86\n",
+       "fitted, inertial, tilted               along         0.40    19.04    19.59\n",
+       "                                       right         0.01     0.35     0.70\n",
+       "                                       horizontal   10.00    19.04    19.59\n",
+       "fitted, inertial, tilted, sample times along         0.45     0.67     0.72\n",
+       "                                       right         0.01     0.33     0.65\n",
+       "                                       horizontal    0.48     0.70     0.74"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>median</th>\n",
+       "      <th>p95 |x|</th>\n",
+       "      <th>max |x|</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">nominal, Earth-fixed</th>\n",
+       "      <th>along</th>\n",
+       "      <td>-2.55</td>\n",
+       "      <td>62.23</td>\n",
+       "      <td>125.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>right</th>\n",
+       "      <td>-1.62</td>\n",
+       "      <td>32.60</td>\n",
+       "      <td>64.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>horizontal</th>\n",
+       "      <td>16.13</td>\n",
+       "      <td>67.36</td>\n",
+       "      <td>127.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">nominal, inertial</th>\n",
+       "      <th>along</th>\n",
+       "      <td>-2.96</td>\n",
+       "      <td>32.09</td>\n",
+       "      <td>51.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>right</th>\n",
+       "      <td>-1.65</td>\n",
+       "      <td>32.76</td>\n",
+       "      <td>61.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>horizontal</th>\n",
+       "      <td>11.55</td>\n",
+       "      <td>43.99</td>\n",
+       "      <td>64.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">fitted, inertial</th>\n",
+       "      <th>along</th>\n",
+       "      <td>-2.96</td>\n",
+       "      <td>32.13</td>\n",
+       "      <td>50.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>right</th>\n",
+       "      <td>-0.04</td>\n",
+       "      <td>32.62</td>\n",
+       "      <td>58.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>horizontal</th>\n",
+       "      <td>11.44</td>\n",
+       "      <td>42.14</td>\n",
+       "      <td>69.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">fitted, inertial, tilted</th>\n",
+       "      <th>along</th>\n",
+       "      <td>0.36</td>\n",
+       "      <td>31.93</td>\n",
+       "      <td>46.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>right</th>\n",
+       "      <td>-0.01</td>\n",
+       "      <td>32.91</td>\n",
+       "      <td>58.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>horizontal</th>\n",
+       "      <td>11.31</td>\n",
+       "      <td>42.89</td>\n",
+       "      <td>67.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">fitted, inertial, tilted, sample times</th>\n",
+       "      <th>along</th>\n",
+       "      <td>0.49</td>\n",
+       "      <td>18.86</td>\n",
+       "      <td>27.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>right</th>\n",
+       "      <td>-0.00</td>\n",
+       "      <td>32.92</td>\n",
+       "      <td>58.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>horizontal</th>\n",
+       "      <td>8.37</td>\n",
+       "      <td>34.71</td>\n",
+       "      <td>60.38</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   median  p95 |x|  max |x|\n",
+       "nominal, Earth-fixed                   along        -2.55    62.23   125.26\n",
+       "                                       right        -1.62    32.60    64.26\n",
+       "                                       horizontal   16.13    67.36   127.46\n",
+       "nominal, inertial                      along        -2.96    32.09    51.10\n",
+       "                                       right        -1.65    32.76    61.72\n",
+       "                                       horizontal   11.55    43.99    64.66\n",
+       "fitted, inertial                       along        -2.96    32.13    50.47\n",
+       "                                       right        -0.04    32.62    58.69\n",
+       "                                       horizontal   11.44    42.14    69.31\n",
+       "fitted, inertial, tilted               along         0.36    31.93    46.80\n",
+       "                                       right        -0.01    32.91    58.85\n",
+       "                                       horizontal   11.31    42.89    67.59\n",
+       "fitted, inertial, tilted, sample times along         0.49    18.86    27.68\n",
+       "                                       right        -0.00    32.92    58.87\n",
+       "                                       horizontal    8.37    34.71    60.38"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "N_PIX = N_FOR * 3\n",
+    "i3 = np.arange(0, len(t_mid), 20)\n",
+    "times3 = list(t_mid[i3])\n",
+    "# TAT-C cross-track pixel index (0 on the right) of each field of regard and view, and along-track pixel index of each view\n",
+    "for_position = (N_FOR - 1 - np.arange(N_FOR)) if orientation > 0 else np.arange(N_FOR)\n",
+    "cross_index = 3 * for_position[:, np.newaxis] + pixel_cross[np.newaxis, :]  # (FOR, FOV)\n",
+    "along_index = np.broadcast_to(pixel_along, cross_index.shape)\n",
+    "\n",
+    "cris_pixels = PointedInstrument(\n",
+    "    name=\"CrIS\",\n",
+    "    field_of_regard=N_FOR * FOR_SPACING,\n",
+    "    cross_track_field_of_view=N_FOR * FOR_SPACING,\n",
+    "    along_track_field_of_view=3 * FOV_SPACING,\n",
+    "    is_rectangular=True,\n",
+    "    cross_track_pixels=N_PIX,\n",
+    "    along_track_pixels=3,\n",
+    ")\n",
+    "fitted = {\n",
+    "    \"velocity_frame\": VelocityFrame.INERTIAL,\n",
+    "    \"cross_track_field_of_view\": N_FOR * fitted_spacing,\n",
+    "    \"field_of_regard\": N_FOR * fitted_spacing,\n",
+    "    \"along_track_field_of_view\": 3 * fov_pitch,\n",
+    "    \"roll_angle\": fitted_roll,\n",
+    "}\n",
+    "configs = {\n",
+    "    \"nominal, Earth-fixed\": cris_pixels,\n",
+    "    \"nominal, inertial\": cris_pixels.model_copy(update={\"velocity_frame\": VelocityFrame.INERTIAL}),\n",
+    "    \"fitted, inertial\": cris_pixels.model_copy(update=fitted),\n",
+    "    \"fitted, inertial, tilted\": cris_pixels.model_copy(update={**fitted, \"pitch_angle\": scan_plane_tilt}),\n",
+    "}\n",
+    "measured_lon = np.zeros((len(i3), N_PIX, 3))\n",
+    "measured_lat = np.zeros((len(i3), N_PIX, 3))\n",
+    "measured_lon[:, cross_index, along_index] = fov_lon[i3]\n",
+    "measured_lat[:, cross_index, along_index] = fov_lat[i3]\n",
+    "\n",
+    "pixels = {}\n",
+    "for name, instrument in configs.items():\n",
+    "    track = collect_ground_pixels(noaa20.model_copy(update={\"instruments\": [instrument]}), times3)\n",
+    "    pixels[name] = (\n",
+    "        track.geometry.x.to_numpy().reshape(len(i3), N_PIX, 3),\n",
+    "        track.geometry.y.to_numpy().reshape(len(i3), N_PIX, 3),\n",
+    "    )\n",
+    "# each field of regard at its own sample time, which TAT-C does not model\n",
+    "sample_time_instrument = configs[\"fitted, inertial, tilted\"]\n",
+    "lon3, lat3 = np.zeros((len(i3), N_PIX, 3)), np.zeros((len(i3), N_PIX, 3))\n",
+    "for j in range(N_FOR):\n",
+    "    track3 = orbit.get_orbit_track([t + timedelta(seconds=dt_for[k, j]) for k, t in zip(i3, times3)])\n",
+    "    for m in range(N_FOV):\n",
+    "        position = sample_time_instrument.compute_projected_pixel_position(track3, cross_index[j, m], along_index[j, m])\n",
+    "        lon3[:, cross_index[j, m], along_index[j, m]] = position.longitude.degrees\n",
+    "        lat3[:, cross_index[j, m], along_index[j, m]] = position.latitude.degrees\n",
+    "pixels[\"fitted, inertial, tilted, sample times\"] = (lon3, lat3)\n",
+    "\n",
+    "residuals3 = {\n",
+    "    name: along_right(\n",
+    "        measured_lon, measured_lat,\n",
+    "        ecef(lon, lat) - ecef(measured_lon, measured_lat),\n",
+    "        sc_vel[i3, np.newaxis, np.newaxis],\n",
+    "    )\n",
+    "    for name, (lon, lat) in pixels.items()\n",
+    "}\n",
+    "\n",
+    "\n",
+    "def per_for(values):\n",
+    "    \"\"\"Values regrouped by field of regard and view: (scan, FOR, FOV).\"\"\"\n",
+    "    return values[:, cross_index, along_index]\n",
+    "\n",
+    "\n",
+    "def residual_table(select):\n",
+    "    return pd.DataFrame(\n",
+    "        {\n",
+    "            (name, component): summarize(select(values)).loc[[\"median\", \"p95 |x|\", \"max |x|\"]]\n",
+    "            for name, (along, right) in residuals3.items()\n",
+    "            for component, values in [(\"along\", along), (\"right\", right), (\"horizontal\", np.hypot(along, right))]\n",
+    "        }\n",
+    "    ).T.round(2)\n",
+    "\n",
+    "\n",
+    "display(residual_table(lambda values: per_for(values)[:, :, center]))\n",
+    "display(residual_table(lambda values: values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "05bf193a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-15T18:43:43.543249Z",
+     "iopub.status.busy": "2026-10-15T18:43:43.543249Z",
+     "iopub.status.idle": "2026-10-15T18:43:43.846242Z",
+     "shell.execute_reply": "2026-10-15T18:43:43.846242Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x380 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ascending3 = sc_vel[i3, 2] > 0\n",
+    "colors3 = dict(zip(residuals3, [BLUE, ORANGE, AQUA, GRAY, INK]))\n",
+    "\n",
+    "\n",
+    "def band(ax, values, color, label, ls=\"-\"):\n",
+    "    \"\"\"Percentiles (5, 50, 95) of residuals by field of regard, for values of shape (scan, FOR) or (scan, FOR, FOV).\"\"\"\n",
+    "    q = np.percentile(values.reshape(len(values), N_FOR, -1).transpose(1, 0, 2).reshape(N_FOR, -1), [5, 50, 95], axis=1)\n",
+    "    ax.fill_between(np.arange(N_FOR), q[0], q[2], color=color, alpha=0.15, lw=0)\n",
+    "    ax.plot(np.arange(N_FOR), q[1], color=color, ls=ls, label=label)\n",
+    "\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 3.8), sharex=True)\n",
+    "along, right = residuals3[\"nominal, Earth-fixed\"]\n",
+    "band(axes[0], per_for(along)[ascending3][:, :, center], BLUE, \"nominal, Earth-fixed, ascending\")\n",
+    "band(axes[0], per_for(along)[~ascending3][:, :, center], BLUE, \"nominal, Earth-fixed, descending\", ls=\"--\")\n",
+    "for name, (along, right) in residuals3.items():\n",
+    "    if name != \"nominal, Earth-fixed\":\n",
+    "        band(axes[1], per_for(along)[:, :, center], colors3[name], name)\n",
+    "    band(axes[2], per_for(right)[:, :, center], colors3[name], name)\n",
+    "axes[0].set(ylabel=\"TAT-C minus CrIS (km)\", title=\"Along track, Earth-fixed frame\")\n",
+    "axes[1].set(title=\"Along track, inertial frame\", ylim=(-25, 25))\n",
+    "axes[2].set(title=\"Cross track (right), all\", ylim=(-10, 10))\n",
+    "for ax in axes:\n",
+    "    ax.set_xlabel(\"CrIS field of regard index (0 = first scanned)\")\n",
+    "    ax.axhline(0, color=GRAY, lw=0.8)\n",
+    "    ax.legend(frameon=False, fontsize=8)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f35d7f1",
+   "metadata": {},
+   "source": [
+    "Lines show medians and bands the 5th to 95th percentiles of the residuals of the central FOVs.\n",
+    "\n",
+    "* **Along-track reference direction.** With TAT-C's default Earth-fixed velocity frame, the along-track residual of the central FOVs grows across the scan to about 60 km (95th percentile) with opposite signs on ascending and descending passes. The inertial velocity frame removes this error.\n",
+    "* **Scan timing.** With all pixels at the scan's middle time, the remaining along-track residual is a tilt across the scan of about 19 km at each edge: CrIS samples its first FOR 2.9 s before the middle time and its last 2.9 s after, while NOAA-20's ground track advances about 6.6 km/s. Projecting each FOR at its sample time removes the tilt. This is the integration time that TAT-C's single-instant view does not resolve, and it is much larger than for ATMS because a CrIS scan takes 5.8 s instead of 1.7 s.\n",
+    "* **Scan angles and tilt.** With the nominal spacing, the cross-track residual grows to about 4 km (95th percentile) at the edges; the fitted spacing and roll angle reduce it to 0.3 km. The fitted scan plane tilt removes a constant along-track bias of about 2.9 km.\n",
+    "\n",
+    "With all three accounted for, TAT-C reproduces the centers of the CrIS FOR arrays to 0.5 km (median) and 0.7 km (95th percentile) everywhere in the swath, comparable to the orbit error of Test 1. The residuals of all FOVs are larger (95th percentile of about 35 km) only because TAT-C does not rotate the FOV array: its FOVs are displaced from the true ones, by up to about 60 km at the swath edges, which does not affect the extent of the swath or the footprint size.\n",
+    "\n",
+    "## Test 4: Swath\n",
+    "\n",
+    "### Swath Width\n",
+    "\n",
+    "For a nadir `Instrument`, TAT-C converts the field of regard to a swath width with `field_of_regard_to_swath_width`, assuming a spherical Earth with the mean radius and the satellite's geodetic altitude. The observed swath width is the distance along the surface from the first to the last FOR center, through the intermediate FOR centers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "23adba58",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-15T18:43:43.848113Z",
+     "iopub.status.busy": "2026-10-15T18:43:43.848113Z",
+     "iopub.status.idle": "2026-10-15T18:43:43.988900Z",
+     "shell.execute_reply": "2026-10-15T18:43:43.988900Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n</th>\n",
+       "      <th>median</th>\n",
+       "      <th>p95 |x|</th>\n",
+       "      <th>max |x|</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>nominal field of regard</th>\n",
+       "      <td>450.0</td>\n",
+       "      <td>-2.7</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>6.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>measured field of regard</th>\n",
+       "      <td>450.0</td>\n",
+       "      <td>-1.5</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>5.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              n  median  p95 |x|  max |x|\n",
+       "nominal field of regard   450.0    -2.7      6.7      6.8\n",
+       "measured field of regard  450.0    -1.5      5.5      5.6"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for_xyz = fov_xyz.mean(axis=2)\n",
+    "for_unit = normalize(for_xyz)\n",
+    "i4 = np.arange(0, len(t_mid), 2)\n",
+    "observed_width = np.sum(np.linalg.norm(np.diff(for_xyz[i4], axis=1), axis=-1), axis=1) / 1e3\n",
+    "sub4 = wgs84.geographic_position_of(orbit.get_orbit_track(list(t_mid[i4])))\n",
+    "swath_width = np.vectorize(field_of_regard_to_swath_width)\n",
+    "widths = {\n",
+    "    \"nominal field of regard\": swath_width(sub4.elevation.m, 2 * EDGE) / 1e3,\n",
+    "    \"measured field of regard\": swath_width(sub4.elevation.m, measured.max() - measured.min()) / 1e3,\n",
+    "}\n",
+    "edges4 = np.arange(-90, 91, 5)\n",
+    "centers4 = (edges4[:-1] + edges4[1:]) / 2\n",
+    "fig, ax = plt.subplots(figsize=(7, 3.6))\n",
+    "ax.plot(centers4, binned(sub4.latitude.degrees, observed_width, edges4)[:, 1], color=INK, marker=\"o\", ms=3, label=\"observed (CrIS)\")\n",
+    "for (name, width), color in zip(widths.items(), [BLUE, ORANGE]):\n",
+    "    ax.plot(centers4, binned(sub4.latitude.degrees, width, edges4)[:, 1], color=color, label=f\"TAT-C: {name}\")\n",
+    "ax.set(xlabel=\"Sub-satellite latitude (deg)\", ylabel=\"Swath width (km)\", title=\"CrIS swath width\")\n",
+    "ax.legend(frameon=False, fontsize=8)\n",
+    "fig.tight_layout()\n",
+    "plt.show()\n",
+    "pd.DataFrame({name: summarize(width - observed_width) for name, width in widths.items()}).T.round(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8624671",
+   "metadata": {},
+   "source": [
+    "NOAA-20's geodetic altitude varies by about 30 km over an orbit, so the swath width varies by about 100 km. TAT-C's nominal field of regard reproduces this variation but is about 3 km too narrow (median); the measured field of regard (Test 2) agrees to within about 1.5 km (median; 5.5 km at the 95th percentile).\n",
+    "\n",
+    "### Swath Area\n",
+    "\n",
+    "Over one orbit (starting at 12:00 UTC), the area sampled by CrIS (the union of the quadrilaterals formed by the centers of adjacent FORs on consecutive scans) is compared with the union of TAT-C footprints from `compute_ground_track`, on a 0.1 deg grid:\n",
+    "\n",
+    "* a nadir `Instrument` with the nominal field of regard, at a 60 s time step (circular footprints of about 2,600 km diameter); and\n",
+    "* a rectangular `PointedInstrument` with the nominal field of regard as its cross-track field of view and the inertial velocity frame, at time steps of 8 s (one scan), 32 s, and 128 s, with the along-track field of view tailored to each time step: the angle subtended at nadir by the ground distance traveled in one time step.\n",
+    "\n",
+    "Footprints at the first and last time steps extend beyond the orbit. To compare swaths rather than these end effects, the comparison is limited to grid cells within 300 km of an FOR center sampled at least 128 s after the start and before the end of the orbit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3c88ae31",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-15T18:43:43.990810Z",
+     "iopub.status.busy": "2026-10-15T18:43:43.990810Z",
+     "iopub.status.idle": "2026-10-15T18:43:56.702245Z",
+     "shell.execute_reply": "2026-10-15T18:43:56.702245Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observed (10^6 km^2)</th>\n",
+       "      <th>TAT-C (10^6 km^2)</th>\n",
+       "      <th>IoU</th>\n",
+       "      <th>observed only (%)</th>\n",
+       "      <th>TAT-C only (%)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>field of regard, 60 s</th>\n",
+       "      <td>80.827</td>\n",
+       "      <td>79.852</td>\n",
+       "      <td>0.986</td>\n",
+       "      <td>1.281</td>\n",
+       "      <td>0.076</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rectangular, 8 s (3.60 deg along track)</th>\n",
+       "      <td>80.827</td>\n",
+       "      <td>80.248</td>\n",
+       "      <td>0.991</td>\n",
+       "      <td>0.816</td>\n",
+       "      <td>0.101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rectangular, 32 s (14.32 deg along track)</th>\n",
+       "      <td>80.827</td>\n",
+       "      <td>80.323</td>\n",
+       "      <td>0.991</td>\n",
+       "      <td>0.763</td>\n",
+       "      <td>0.139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rectangular, 128 s (53.36 deg along track)</th>\n",
+       "      <td>80.827</td>\n",
+       "      <td>81.167</td>\n",
+       "      <td>0.987</td>\n",
+       "      <td>0.440</td>\n",
+       "      <td>0.857</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            observed (10^6 km^2)  \\\n",
+       "field of regard, 60 s                                     80.827   \n",
+       "rectangular, 8 s (3.60 deg along track)                   80.827   \n",
+       "rectangular, 32 s (14.32 deg along track)                 80.827   \n",
+       "rectangular, 128 s (53.36 deg along track)                80.827   \n",
+       "\n",
+       "                                            TAT-C (10^6 km^2)    IoU  \\\n",
+       "field of regard, 60 s                                  79.852  0.986   \n",
+       "rectangular, 8 s (3.60 deg along track)                80.248  0.991   \n",
+       "rectangular, 32 s (14.32 deg along track)              80.323  0.991   \n",
+       "rectangular, 128 s (53.36 deg along track)             81.167  0.987   \n",
+       "\n",
+       "                                            observed only (%)  TAT-C only (%)  \n",
+       "field of regard, 60 s                                   1.281           0.076  \n",
+       "rectangular, 8 s (3.60 deg along track)                 0.816           0.101  \n",
+       "rectangular, 32 s (14.32 deg along track)               0.763           0.139  \n",
+       "rectangular, 128 s (53.36 deg along track)              0.440           0.857  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from shapely import contains_xy, prepare\n",
+    "\n",
+    "contiguous = np.diff(cris[\"MidTime\"][good]) < 1.5 * SCAN_PERIOD * 1e6\n",
+    "\n",
+    "\n",
+    "def observed_cells(points, scans):\n",
+    "    \"\"\"Whether unit vectors lie within a quadrilateral cell of adjacent fields of regard on consecutive scans (a contiguous range of scan indices).\"\"\"\n",
+    "    k0, k1 = scans[0], scans[-1]\n",
+    "    cells = for_unit[k0 : k1 + 1]\n",
+    "    tree = cKDTree(cells.reshape(-1, 3))\n",
+    "    _, nearest = tree.query(points, k=2, workers=-1)\n",
+    "    inside = np.zeros(len(points), dtype=bool)\n",
+    "    for column in range(2):\n",
+    "        k, j = np.divmod(nearest[:, column], N_FOR)\n",
+    "        for dk in (-1, 0):\n",
+    "            for dj in (-1, 0):\n",
+    "                kk, jj = k + dk, j + dj\n",
+    "                valid = (kk >= 0) & (kk < len(cells) - 1) & (jj >= 0) & (jj < N_FOR - 1)\n",
+    "                kk, jj = np.clip(kk, 0, len(cells) - 2), np.clip(jj, 0, N_FOR - 2)\n",
+    "                valid &= contiguous[k0 + kk]\n",
+    "                corners = [cells[kk, jj], cells[kk, jj + 1], cells[kk + 1, jj + 1], cells[kk + 1, jj]]\n",
+    "                signs = np.stack([np.sum(np.cross(a, b) * points, axis=-1) for a, b in zip(corners, corners[1:] + corners[:1])])\n",
+    "                inside |= valid & (np.all(signs >= 0, axis=0) | np.all(signs <= 0, axis=0))\n",
+    "    return inside\n",
+    "\n",
+    "\n",
+    "STEP4, MARGIN4 = 0.1, 128  # deg, s\n",
+    "lon_grid, lat_grid = np.meshgrid(np.arange(-180 + STEP4 / 2, 180, STEP4), np.arange(-90 + STEP4 / 2, 90, STEP4))\n",
+    "cell_area = np.cos(np.radians(lat_grid)) * (STEP4 * 111.32) ** 2  # km^2\n",
+    "grid_unit = up(lon_grid.ravel(), lat_grid.ravel())\n",
+    "orbit_start = datetime(2026, 10, 2, 12, tzinfo=timezone.utc)\n",
+    "orbit_period = timedelta(days=1 / float(tle[1][52:63]))\n",
+    "scans4 = np.nonzero((t_mid >= orbit_start) & (t_mid < orbit_start + orbit_period))[0]\n",
+    "first, last = t_mid[scans4[0]].to_pydatetime(), t_mid[scans4[-1]].to_pydatetime()\n",
+    "# grid cells near fields of regard sampled away from the ends of the orbit\n",
+    "distance, nearest = cKDTree(for_unit[scans4].reshape(-1, 3)).query(grid_unit, workers=-1)\n",
+    "nearest_time = (t_mid[scans4] - t_mid[scans4[0]]).total_seconds().to_numpy()[nearest // N_FOR]\n",
+    "compared = (distance < 2 * np.sin(300 / 6371 / 2)) & (nearest_time > MARGIN4) & (nearest_time < (last - first).total_seconds() - MARGIN4)\n",
+    "observed4 = np.zeros(len(grid_unit), dtype=bool)\n",
+    "observed4[compared] = observed_cells(grid_unit[compared], scans4)\n",
+    "ground_speed = 2 * np.pi * 6371.0088e3 / orbit_period.total_seconds()  # m/s, approximate\n",
+    "altitude = float(np.median(sub4.elevation.m))\n",
+    "\n",
+    "\n",
+    "def swath_area_row(instrument, time_step):\n",
+    "    n = int((last - first).total_seconds() // time_step)\n",
+    "    times = [first + timedelta(seconds=time_step * (i + 0.5)) for i in range(n)]\n",
+    "    geometry = compute_ground_track(noaa20.model_copy(update={\"instruments\": [instrument]}), times).geometry.iloc[0]\n",
+    "    prepare(geometry)\n",
+    "    predicted = np.zeros(len(grid_unit), dtype=bool)\n",
+    "    predicted[compared] = contains_xy(geometry, lon_grid.ravel()[compared], lat_grid.ravel()[compared])\n",
+    "    area = lambda mask: np.sum(mask * cell_area.ravel()) / 1e6\n",
+    "    return predicted.reshape(lat_grid.shape), {\n",
+    "        \"observed (10^6 km^2)\": area(observed4),\n",
+    "        \"TAT-C (10^6 km^2)\": area(predicted),\n",
+    "        \"IoU\": area(observed4 & predicted) / area(observed4 | predicted),\n",
+    "        \"observed only (%)\": 100 * area(observed4 & ~predicted) / area(observed4),\n",
+    "        \"TAT-C only (%)\": 100 * area(predicted & ~observed4) / area(predicted),\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "swath_rows, swath_maps = {}, {}\n",
+    "swath_maps[\"field of regard, 60 s\"], swath_rows[\"field of regard, 60 s\"] = swath_area_row(\n",
+    "    Instrument(name=\"CrIS\", field_of_regard=2 * EDGE), 60\n",
+    ")\n",
+    "for time_step in [SCAN_PERIOD, 32, 128]:\n",
+    "    along_fov = np.degrees(2 * np.arctan(ground_speed * time_step / 2 / altitude))\n",
+    "    name = f\"rectangular, {time_step:.3g} s ({along_fov:.2f} deg along track)\"\n",
+    "    swath_maps[name], swath_rows[name] = swath_area_row(\n",
+    "        PointedInstrument(\n",
+    "            name=\"CrIS\",\n",
+    "            field_of_regard=2 * EDGE,\n",
+    "            cross_track_field_of_view=2 * EDGE,\n",
+    "            along_track_field_of_view=along_fov,\n",
+    "            is_rectangular=True,\n",
+    "            velocity_frame=VelocityFrame.INERTIAL,\n",
+    "        ),\n",
+    "        time_step,\n",
+    "    )\n",
+    "observed4 = observed4.reshape(lat_grid.shape)\n",
+    "pd.DataFrame(swath_rows).T.round(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "8856aebf",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-15T18:43:56.704305Z",
+     "iopub.status.busy": "2026-10-15T18:43:56.704305Z",
+     "iopub.status.idle": "2026-10-15T18:44:02.789270Z",
+     "shell.execute_reply": "2026-10-15T18:44:02.789270Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x420 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 4.2), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
+    "for ax, name in zip(axes, [list(swath_maps)[0], list(swath_maps)[-1]]):\n",
+    "    predicted = swath_maps[name]\n",
+    "    category = np.where(observed4 & predicted, 1, np.where(observed4, 2, np.where(predicted, 3, 0))).astype(float)\n",
+    "    category[~compared.reshape(lat_grid.shape)] = 0\n",
+    "    ax.pcolormesh(\n",
+    "        lon_grid, lat_grid, np.ma.masked_equal(category, 0),\n",
+    "        cmap=plt.matplotlib.colors.ListedColormap([\"#d9d8d3\", ORANGE, BLUE]), vmin=0.5, vmax=3.5,\n",
+    "        transform=ccrs.PlateCarree(), shading=\"auto\",\n",
+    "    )\n",
+    "    ax.coastlines(color=GRAY, linewidth=0.5)\n",
+    "    ax.set_global()\n",
+    "    ax.set_title(f\"TAT-C {name}\", fontsize=9)\n",
+    "axes[0].plot([], [], color=\"#d9d8d3\", lw=6, label=\"both\")\n",
+    "axes[0].plot([], [], color=ORANGE, lw=6, label=\"observed only\")\n",
+    "axes[0].plot([], [], color=BLUE, lw=6, label=\"TAT-C only\")\n",
+    "axes[0].legend(frameon=False, loc=\"lower left\", fontsize=8)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38f2e5e7",
+   "metadata": {},
+   "source": [
+    "All configurations reproduce the area sampled over one orbit with an intersection over union (IoU) of 0.99. The differences are thin slivers along the swath edges, mostly area observed but not predicted (0.4 to 1.3%), consistent with TAT-C's slightly narrow nominal swath. Tailoring the along-track field of view to the time step makes consecutive rectangular footprints tile the swath at every time step tested, so the time step can be chosen for computational cost. At 128 s, footprints spanning 53 deg along track begin to bulge beyond the swath edges (TAT-C only, 0.9%).\n",
+    "\n",
+    "Note that the swath of FOR centers compared here is narrower than the swath of complete footprints: the outermost FORs sample beyond their centers by about half of their cross-track footprint size (Test 5), about 20 km.\n",
+    "\n",
+    "## Test 5: Footprint Size\n",
+    "\n",
+    "The CrIS geolocation file does not report footprint dimensions. A footprint is the projection of a FOV, a circle 0.963 deg in diameter (a published specification, not derived from the data) onto the surface. Its along-track dimension is the slant range times the angular diameter, and its cross-track dimension is longer by the secant of the satellite zenith angle at the surface (on a flat surface). The reported zenith angles and slant ranges, which describe the viewing geometry, are therefore compared first with TAT-C's: the zenith angle from the satellite's elevation as seen from each pixel (`collect_ground_pixels` with `sat_altaz=True`), and the slant range from the zenith angle and the sub-satellite height on a spherical Earth. The comparison uses the central FOV of each FOR, which the array rotation does not displace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b3cfb72c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-15T18:44:02.791185Z",
+     "iopub.status.busy": "2026-10-15T18:44:02.791185Z",
+     "iopub.status.idle": "2026-10-15T18:44:10.430323Z",
+     "shell.execute_reply": "2026-10-15T18:44:10.430323Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n</th>\n",
+       "      <th>median</th>\n",
+       "      <th>p95 |x|</th>\n",
+       "      <th>max |x|</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>zenith angle, TAT-C minus reported (deg)</th>\n",
+       "      <td>1350.0</td>\n",
+       "      <td>-0.001</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>0.014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>slant range, TAT-C minus reported (km)</th>\n",
+       "      <td>1350.0</td>\n",
+       "      <td>-0.020</td>\n",
+       "      <td>0.276</td>\n",
+       "      <td>0.765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>equivalent diameter, TAT-C minus reported (km)</th>\n",
+       "      <td>1350.0</td>\n",
+       "      <td>-0.000</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>0.021</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                     n  median  p95 |x|  \\\n",
+       "zenith angle, TAT-C minus reported (deg)        1350.0  -0.001    0.006   \n",
+       "slant range, TAT-C minus reported (km)          1350.0  -0.020    0.276   \n",
+       "equivalent diameter, TAT-C minus reported (km)  1350.0  -0.000    0.007   \n",
+       "\n",
+       "                                                max |x|  \n",
+       "zenith angle, TAT-C minus reported (deg)          0.014  \n",
+       "slant range, TAT-C minus reported (km)            0.765  \n",
+       "equivalent diameter, TAT-C minus reported (km)    0.021  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# TAT-C's satellite zenith angle (from the satellite's elevation as seen from each pixel) at the fitted configuration\n",
+    "track5 = collect_ground_pixels(\n",
+    "    noaa20.model_copy(update={\"instruments\": [configs[\"fitted, inertial, tilted\"]]}), times3, sat_altaz=True\n",
+    ")\n",
+    "zenith_model = per_for(90 - track5[\"sat_alt\"].to_numpy().reshape(len(i3), N_PIX, 3))[:, :, center]  # (scan, FOR)\n",
+    "zenith_reported = fov_zenith[i3][:, :, center]\n",
+    "range_reported = fov_range[i3][:, :, center]\n",
+    "altitude5 = wgs84.geographic_position_of(orbit.get_orbit_track(times3)).elevation.m  # of the sub-satellite point\n",
+    "\n",
+    "\n",
+    "def slant_range(altitude, zenith, radius=EARTH_MEAN_RADIUS):\n",
+    "    \"\"\"Slant range (m) from a satellite to a surface point at a satellite zenith angle (deg), on a spherical Earth.\"\"\"\n",
+    "    c = radius * np.cos(np.radians(zenith))\n",
+    "    return -c + np.sqrt(c**2 + 2 * radius * altitude + altitude**2)\n",
+    "\n",
+    "\n",
+    "def footprint_size(range_m, zenith, ifov):\n",
+    "    \"\"\"Footprint (along-track, cross-track, equivalent-area diameter) in km of a circular field of view (deg).\"\"\"\n",
+    "    along = range_m * np.radians(ifov) / 1e3\n",
+    "    cross = along / np.cos(np.radians(zenith))\n",
+    "    return along, cross, np.sqrt(along * cross)\n",
+    "\n",
+    "\n",
+    "range_model = slant_range(altitude5[:, np.newaxis], zenith_model)\n",
+    "test5 = pd.DataFrame(\n",
+    "    {\n",
+    "        \"zenith angle, TAT-C minus reported (deg)\": summarize(zenith_model - zenith_reported),\n",
+    "        \"slant range, TAT-C minus reported (km)\": summarize((range_model - range_reported) / 1e3),\n",
+    "        \"equivalent diameter, TAT-C minus reported (km)\": summarize(\n",
+    "            footprint_size(range_model, zenith_model, FOV_DIAMETER)[2] - footprint_size(range_reported, zenith_reported, FOV_DIAMETER)[2]\n",
+    "        ),\n",
+    "    }\n",
+    ").T\n",
+    "test5.round(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "4ca6f3ea",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-15T18:44:10.430323Z",
+     "iopub.status.busy": "2026-10-15T18:44:10.430323Z",
+     "iopub.status.idle": "2026-10-15T18:44:10.445787Z",
+     "shell.execute_reply": "2026-10-15T18:44:10.445787Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>scan angle (deg)</th>\n",
+       "      <th>zenith angle (deg)</th>\n",
+       "      <th>range (km)</th>\n",
+       "      <th>along-track size (km)</th>\n",
+       "      <th>cross-track size (km)</th>\n",
+       "      <th>equivalent diameter (km)</th>\n",
+       "      <th>spacing of views (km)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>field of regard</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48.3</td>\n",
+       "      <td>57.6</td>\n",
+       "      <td>1385.9</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>43.4</td>\n",
+       "      <td>31.8</td>\n",
+       "      <td>39.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>34.9</td>\n",
+       "      <td>40.4</td>\n",
+       "      <td>1057.7</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>20.4</td>\n",
+       "      <td>23.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>18.2</td>\n",
+       "      <td>20.7</td>\n",
+       "      <td>889.3</td>\n",
+       "      <td>14.9</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>15.5</td>\n",
+       "      <td>17.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1.6</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>838.9</td>\n",
+       "      <td>14.1</td>\n",
+       "      <td>14.1</td>\n",
+       "      <td>14.1</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>-1.8</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>839.0</td>\n",
+       "      <td>14.1</td>\n",
+       "      <td>14.1</td>\n",
+       "      <td>14.1</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>-15.1</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>872.7</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>17.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>-31.8</td>\n",
+       "      <td>36.6</td>\n",
+       "      <td>1012.4</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>21.2</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>21.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>-48.4</td>\n",
+       "      <td>57.8</td>\n",
+       "      <td>1392.7</td>\n",
+       "      <td>23.4</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>32.1</td>\n",
+       "      <td>40.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 scan angle (deg)  zenith angle (deg)  range (km)  \\\n",
+       "field of regard                                                     \n",
+       "0                            48.3                57.6      1385.9   \n",
+       "4                            34.9                40.4      1057.7   \n",
+       "9                            18.2                20.7       889.3   \n",
+       "14                            1.6                 1.8       838.9   \n",
+       "15                           -1.8                 2.0       839.0   \n",
+       "19                          -15.1                17.1       872.7   \n",
+       "24                          -31.8                36.6      1012.4   \n",
+       "29                          -48.4                57.8      1392.7   \n",
+       "\n",
+       "                 along-track size (km)  cross-track size (km)  \\\n",
+       "field of regard                                                 \n",
+       "0                                 23.3                   43.4   \n",
+       "4                                 17.8                   23.3   \n",
+       "9                                 14.9                   16.0   \n",
+       "14                                14.1                   14.1   \n",
+       "15                                14.1                   14.1   \n",
+       "19                                14.7                   15.3   \n",
+       "24                                17.0                   21.2   \n",
+       "29                                23.4                   44.0   \n",
+       "\n",
+       "                 equivalent diameter (km)  spacing of views (km)  \n",
+       "field of regard                                                   \n",
+       "0                                    31.8                   39.6  \n",
+       "4                                    20.4                   23.5  \n",
+       "9                                    15.5                   17.5  \n",
+       "14                                   14.1                   16.0  \n",
+       "15                                   14.1                   16.0  \n",
+       "19                                   15.0                   17.0  \n",
+       "24                                   19.0                   21.8  \n",
+       "29                                   32.1                   40.0  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# measured spacing (km) of the views of an array: the mean distance from the central view to its four nearest neighbors\n",
+    "neighbors = [m for m in range(N_FOV) if abs(pixel_cross[m] - 1) + abs(pixel_along[m] - 1) == 1]\n",
+    "view_spacing = np.mean([np.linalg.norm(fov_xyz[:, :, m] - fov_xyz[:, :, center], axis=-1) for m in neighbors], axis=0) / 1e3  # (scan, FOR)\n",
+    "along_r, cross_r, equivalent_r = footprint_size(range_reported, zenith_reported, FOV_DIAMETER)\n",
+    "by_for = lambda a: a.mean(axis=0)  # mean over scans of each field of regard\n",
+    "rows = [0, 4, 9, 14, 15, 19, 24, 29]\n",
+    "table5 = pd.DataFrame(\n",
+    "    {\n",
+    "        \"scan angle (deg)\": measured[rows],\n",
+    "        \"zenith angle (deg)\": by_for(zenith_reported)[rows],\n",
+    "        \"range (km)\": by_for(range_reported)[rows] / 1e3,\n",
+    "        \"along-track size (km)\": by_for(along_r)[rows],\n",
+    "        \"cross-track size (km)\": by_for(cross_r)[rows],\n",
+    "        \"equivalent diameter (km)\": by_for(equivalent_r)[rows],\n",
+    "        \"spacing of views (km)\": by_for(view_spacing)[rows],\n",
+    "    },\n",
+    "    index=pd.Index(rows, name=\"field of regard\"),\n",
+    ")\n",
+    "table5.round(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "2391013a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-15T18:44:10.447832Z",
+     "iopub.status.busy": "2026-10-15T18:44:10.446796Z",
+     "iopub.status.idle": "2026-10-15T18:44:10.644708Z",
+     "shell.execute_reply": "2026-10-15T18:44:10.643381Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(7, 3.6))\n",
+    "ax.plot(np.arange(N_FOR), by_for(along_r), color=BLUE, label=\"along-track size\")\n",
+    "ax.plot(np.arange(N_FOR), by_for(cross_r), color=ORANGE, label=\"cross-track size\")\n",
+    "ax.plot(np.arange(N_FOR), by_for(equivalent_r), color=INK, label=\"equivalent diameter\")\n",
+    "ax.plot(np.arange(N_FOR), by_for(view_spacing), color=GRAY, ls=\"--\", label=\"spacing of views\")\n",
+    "ax.set(xlabel=\"CrIS field of regard index (0 = first scanned)\", ylabel=\"Size or spacing (km)\", title=\"CrIS footprint size\")\n",
+    "ax.legend(frameon=False, fontsize=8)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2eb8721",
+   "metadata": {},
+   "source": [
+    "TAT-C's viewing geometry reproduces the reported zenith angles to 0.006 deg and slant ranges to 0.3 km (95th percentile), so footprint sizes follow to better than 0.01 km. The nadir footprint is 14.1 km, confirming the nominal 14 km. Footprints grow toward the swath edge as the range lengthens (to 1,390 km, compared with 839 km at nadir) and the view slants (to a zenith angle of 58 deg): at the outermost FORs they are 23 km along track and 43 km cross track, an equivalent diameter (that of a circle of the same area) of 32 km, about 2.3 times that at nadir. The views of an array are 16 km apart at nadir and 40 km at the swath edge, so footprints leave gaps (88% fill at nadir, 80% at the edge)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "864a7786",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "The parameters fitted to the NOAA-20 CrIS data, used by the [monsoon OSSE example](../examples/RunOSSEMonsoon.ipynb):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "724c596c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-15T18:44:10.646692Z",
+     "iopub.status.busy": "2026-10-15T18:44:10.646692Z",
+     "iopub.status.idle": "2026-10-15T18:44:10.658820Z",
+     "shell.execute_reply": "2026-10-15T18:44:10.658820Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "velocity frame                                           inertial\n",
+       "FOR spacing (deg)                                         3.33399\n",
+       "roll angle (deg)                                        -0.092556\n",
+       "scan plane tilt, pitch angle (deg)                       0.224515\n",
+       "FOV pitch (deg)                                          1.091809\n",
+       "FOV diameter, published (deg)                               0.963\n",
+       "field of regard, between outermost FOR centers (deg)    96.685708\n",
+       "footprint equivalent diameter at nadir (km)              14.10401\n",
+       "footprint equivalent diameter at the swath edge (km)    31.942358\n",
+       "center geolocation error, 95th percentile (km)           0.696837\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(\n",
+    "    {\n",
+    "        \"velocity frame\": \"inertial\",\n",
+    "        \"FOR spacing (deg)\": fitted_spacing,\n",
+    "        \"roll angle (deg)\": fitted_roll,\n",
+    "        \"scan plane tilt, pitch angle (deg)\": scan_plane_tilt,\n",
+    "        \"FOV pitch (deg)\": fov_pitch,\n",
+    "        \"FOV diameter, published (deg)\": FOV_DIAMETER,\n",
+    "        \"field of regard, between outermost FOR centers (deg)\": measured.max() - measured.min(),\n",
+    "        \"footprint equivalent diameter at nadir (km)\": by_for(equivalent_r)[N_FOR // 2 - 1 : N_FOR // 2 + 1].mean(),\n",
+    "        \"footprint equivalent diameter at the swath edge (km)\": by_for(equivalent_r)[[0, -1]].mean(),\n",
+    "        \"center geolocation error, 95th percentile (km)\": summarize(\n",
+    "            np.hypot(*[per_for(v)[:, :, center] for v in residuals3[\"fitted, inertial, tilted, sample times\"]])\n",
+    "        ).loc[\"p95 |x|\"],\n",
+    "    }\n",
+    ").round(3)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/validation/index.rst b/docs/validation/index.rst
index a8a225dd..04bb73ab 100644
--- a/docs/validation/index.rst
+++ b/docs/validation/index.rst
@@ -13,6 +13,7 @@ The following compare TAT-C analysis results with reference data from operationa
   ValidateROPlanetiQ.ipynb
   ValidateRadarNEXRAD.ipynb
   ValidateImagerATMS.ipynb
+  ValidateImagerCrIS.ipynb
   ValidateImagerVIIRS.ipynb
   ValidateSARSentinel1.ipynb
   ValidateSARNISAR.ipynb
diff --git a/pyproject.toml b/pyproject.toml
index cb31def8..bbcf51cf 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -90,12 +90,14 @@ examples = [
     "cartopy"
 ]
 osse = [
+    "tatc[examples]",
     "netcdf4",
     "pydap",
     "rioxarray",
     "scipy",
     "scikit-learn",
-    "seaborn"
+    "seaborn",
+    "xarray"
 ]
 preprocess = [
     "rasterio"