From ecbe9ffad63367c36d849dd883f7783c52e335a4 Mon Sep 17 00:00:00 2001 From: Mathieu Schaer Date: Mon, 9 Nov 2026 20:55:47 +0100 Subject: [PATCH 01/10] Never search neighbors or triangulate in latitude/longitude degrees The generic path of interp2poi and interp2grid (KDTree methods and griddata) ran in latitude/longitude degrees whenever lat/lon coordinates were present, distorting distances (a degree of longitude is about 0.68 of a degree of latitude in Switzerland). Coordinates are now always in meters: - native coordinates projected in meters are used first (get_coords_name_crs(prefer_projected=True)); - metric source or destination: its CRS (the larger grid if both are metric); - both in lat/lon: LV95 if the destination is within lv95_max_offset (700 km) of the LV95 central northing, otherwise earth-centered earth-fixed coordinates (ECEF), which supports global grids; - linear, linear_3D and cubic raise an error in the ECEF case; - altitude is always the last coordinate column; height methods no longer force Swiss coordinates. Other changes: - geographic CRS on a sphere (e.g. CF rotated pole with earth_radius) are considered on WGS84, so that the datum shift (~180 m to LV95) is applied; parse_grid_mapping builds such CF mappings directly (pyproj.CRS.from_cf took ~0.3 s) and caches the other ones; - set_grid_mapping and parse_grid_mapping support numpy values in grid mappings; - create_dst_grid_from_src spans exactly the reprojected source domain; - interp2grid raises a clear error for unstructured destination grids. Co-Authored-By: Claude Opus 5.5 --- CHANGELOG.rst | 25 ++ grid_process/config/grid_process.yaml | 4 + grid_process/config/library_settings.py | 1 + grid_process/data_model.py | 142 ++++++++- grid_process/grid_interp.py | 383 ++++++++++++++++-------- grid_process/grid_interp_utils.py | 20 +- grid_process/helpers.py | 72 ++++- test/test_data_model.py | 120 ++++++++ test/test_grid_interp.py | 251 +++++++++++++++- test/test_grid_interp_utils.py | 87 +++++- test/test_helpers.py | 49 +++ 11 files changed, 1001 insertions(+), 153 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 164c712..34629f5 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -12,6 +12,31 @@ Unreleased integers (e.g. an LV95 destination grid built with ``np.arange``), which gave wrong values or NaN * fixed ``create_dst_grid`` with ``topright`` and ``resolution``: the resolution is now exact and ``topright`` a closed upper bound, instead of the grid spacing being stretched to end exactly on ``topright`` +* fixed ``interp2poi`` and ``interp2grid`` (outside of the regular grid interpolation) searching nearest neighbors + and triangulating (``linear``, ``cubic``) in latitude/longitude degrees, where a degree of longitude is shorter + than a degree of latitude (about 0.68 times in Switzerland). Distances are now always in meters: native + coordinates projected in meters are used even if latitude/longitude coordinates are present (e.g. LV95 grids), + and latitude/longitude grids are converted to Swiss LV95, or to earth-centered earth-fixed coordinates (ECEF) far + from Switzerland. Neighbors, IDW weights and ``linear``/``cubic`` results change slightly for grids that were + searched in latitude/longitude, including native LV95 grids with latitude/longitude coordinates +* fixed the reprojection of grids in rotated pole (or latitude/longitude) on a sphere without datum, e.g. from a CF + grid mapping with ``earth_radius``: their latitudes and longitudes are now considered as WGS84 ones, as their + latitude/longitude coordinates usually are. The datum shift to Swiss coordinates (about 180 m) was left out. + ``parse_grid_mapping`` (and ``get_grid_crs``) now return such CF mappings directly on WGS84, without + ``pyproj.CRS.from_cf``, which took about 0.3 s for a rotated pole; other mappings parsed by ``from_cf`` are cached +* added the support of global and other non-Swiss latitude/longitude grids by ``interp2poi`` and ``interp2grid`` + (except ``linear``, ``linear_3D`` and ``cubic``, which raise an error), with nearest neighbor searches in ECEF + coordinates. The maximum distance of the destination to the LV95 central northing for using LV95 is set by the + new setting ``lv95_max_offset`` (environment variable ``GRID_PROCESS__lv95_max_offset``, default 700 km) +* changed the methods with altitude, which no longer reproject to Swiss coordinates when the source grid or the + destination has another CRS projected in meters (e.g. UTM) +* added an explicit error when the destination grid of ``interp2grid`` is unstructured, which is not supported +* fixed ``create_dst_grid_from_src``: the destination grid now spans exactly the bounding box of the source domain + reprojected in the destination CRS (from native coordinates projected in meters when available), instead of the + reprojected corners of its latitude/longitude bounding box, which could be much larger (e.g. +80 km for ICON-CH2 + in LV95) or miss parts of the domain (e.g. 28 km for IFS in LV95) +* fixed ``set_grid_mapping`` with CF grid mappings holding numpy arrays or non-float64 numpy values (e.g. a + ``standard_parallel`` array read from a netCDF file), which raised a ``TypeError`` 1.0.0 ----- diff --git a/grid_process/config/grid_process.yaml b/grid_process/config/grid_process.yaml index 78c1452..d7bbc76 100644 --- a/grid_process/config/grid_process.yaml +++ b/grid_process/config/grid_process.yaml @@ -4,6 +4,10 @@ cache_size: reproject_poi: 6 reproject_grid: 6 +# [m] max |N_LV95 - 1 200 000| of the destination for interpolating in LV95 when both grids are +# in lat/lon (otherwise ECEF). 700 km ≈ 0.6 % scale error, covers ICON-CH1/CH2 (523 km) +lv95_max_offset: 700000 + data_model: # supported names grid: unstructured_dim: ["cell", "points"] diff --git a/grid_process/config/library_settings.py b/grid_process/config/library_settings.py index 939227e..b28b44d 100644 --- a/grid_process/config/library_settings.py +++ b/grid_process/config/library_settings.py @@ -55,6 +55,7 @@ class LibrarySettings(BaseSettings): ) cache_size: CacheSizeSettings data_model: DataModelSettings + lv95_max_offset: float = Field(gt=0) @classmethod def settings_customise_sources( diff --git a/grid_process/data_model.py b/grid_process/data_model.py index ff8917e..ba40b05 100644 --- a/grid_process/data_model.py +++ b/grid_process/data_model.py @@ -10,12 +10,15 @@ import logging import json +from functools import lru_cache from typing import Any, TypeVar, Literal, overload from collections.abc import Callable import numpy as np from numpy.typing import NDArray from pyproj import CRS +from pyproj.crs import DerivedGeographicCRS +from pyproj.crs.coordinate_operation import PoleRotationNetCDFCFConversion import geopandas as gpd import xarray as xr @@ -203,12 +206,6 @@ def set_grid_mapping(ds: xr.Dataset) -> xr.Dataset: data variable encoding the grid mapping is removed. """ - def serializable_mapping() -> dict[str, Any]: - return { - key: float(value) if isinstance(value, np.floating) else value - for key, value in ds_out[mapname].attrs.items() - } - ds_out = ds.copy(deep=False) grid_map = set() for varname, da in ds_out.items(): @@ -224,7 +221,7 @@ def serializable_mapping() -> dict[str, Any]: mapname = grid_map.pop() if {"grid_mapping", "crs"}.isdisjoint(ds_out.attrs) and mapname in ds_out: - ds_out.attrs["grid_mapping"] = json.dumps(serializable_mapping()) + ds_out.attrs["grid_mapping"] = _grid_mapping_json(ds_out[mapname].attrs) return ds_out.drop_vars(mapname, errors="ignore") @@ -452,23 +449,104 @@ def parse_grid_mapping(grid_mapping: str | dict[str, Any]) -> CRS: See also -------- :func:`get_grid_crs` + + Note + ---- + CF ``latitude_longitude`` and ``rotated_latitude_longitude`` mappings without ellipsoid parameters other than + ``earth_radius`` (as NWP models on a sphere) are built directly on WGS84, which is much faster than + pyproj.CRS.from_cf(). The result is the same as ``sphere_as_wgs84(pyproj.CRS.from_cf(grid_mapping))``, see + :func:`~grid_process.helpers.sphere_as_wgs84`. """ + # pyproj.CRS.from_cf() takes ~0.3 s for mappings with ellipsoid parameters (custom datum): cached on the JSON if isinstance(grid_mapping, str): - grid_mapping = json.loads(grid_mapping) + return _parse_grid_mapping_str(grid_mapping) + try: + return _parse_grid_mapping_str(_grid_mapping_json(grid_mapping)) + except TypeError: # not serializable: parse without cache + return _parse_grid_mapping_dict(grid_mapping) + + +def _grid_mapping_json(grid_mapping: dict[str, Any]) -> str: + """ + JSON string of a grid mapping dictionary, with sorted keys and numpy scalars and arrays (e.g. attributes read + from a netCDF file such as "standard_parallel") converted to numbers and lists. + """ + + return json.dumps(grid_mapping, sort_keys=True, default=lambda value: value.tolist()) + + +@lru_cache(maxsize=32) +def _parse_grid_mapping_str(grid_mapping: str) -> CRS: + """ + Cached parsing of a JSON "grid_mapping" attribute, see :func:`parse_grid_mapping`. + """ + + return _parse_grid_mapping_dict(json.loads(grid_mapping)) + + +def _parse_grid_mapping_dict(grid_mapping: dict[str, Any]) -> CRS: + """ + Parsing of a "grid_mapping" dictionary, see :func:`parse_grid_mapping`. + """ + if "epsg_code" in grid_mapping: - return CRS.from_string(grid_mapping["epsg_code"]) # type: ignore[index] + return CRS.from_string(grid_mapping["epsg_code"]) + + if _ELLIPSOID_KEYS.isdisjoint(grid_mapping): + mapping_name = grid_mapping.get("grid_mapping_name") + if mapping_name == "latitude_longitude": + return CRS.from_epsg(4326) + if mapping_name == "rotated_latitude_longitude": + return DerivedGeographicCRS( + base_crs=CRS.from_epsg(4326), + conversion=PoleRotationNetCDFCFConversion( + grid_north_pole_latitude=grid_mapping["grid_north_pole_latitude"], + grid_north_pole_longitude=grid_mapping["grid_north_pole_longitude"], + north_pole_grid_longitude=grid_mapping.get("north_pole_grid_longitude", 0.0), + ), + ) + + return CRS.from_cf(grid_mapping) + + +# CF grid mapping attributes defining the ellipsoid or datum (other than earth_radius for a sphere) +_ELLIPSOID_KEYS = frozenset( + ( + "semi_major_axis", + "semi_minor_axis", + "inverse_flattening", + "reference_ellipsoid_name", + "horizontal_datum_name", + "geographic_crs_name", + "prime_meridian_name", + "longitude_of_prime_meridian", + "towgs84", + "crs_wkt", + "spatial_ref", # WKT written by GDAL + ) +) + + +def is_metric_crs(crs: CRS) -> bool: + """ + Whether ``crs`` is a projected CRS with all axes in meters (e.g. LV95, LV03 or UTM), i.e. in which euclidean + distances can be computed directly from the coordinates. + """ - return CRS.from_cf(grid_mapping) # type: ignore[arg-type] + return bool(crs.is_projected) and all(axis.unit_name in ("metre", "meter") for axis in crs.axis_info) def get_coords_name_crs( - grid: xr.Dataset | xr.DataArray, spatial_dims: tuple[str, ...] | None = None + grid: xr.Dataset | xr.DataArray, + spatial_dims: tuple[str, ...] | None = None, + prefer_projected: bool = False, ) -> tuple[str, str, CRS]: """ Get correct coordinate names and CRS according to supported conventions. When non-dimensional longitude and latitude coordinates arrays are present, these - are used instead of the native spatial dimension coordinates. + are used instead of the native spatial dimension coordinates, unless ``prefer_projected`` + is True and the native CRS is projected with axes in meters. Parameters ---------- @@ -480,6 +558,10 @@ def get_coords_name_crs( name of the ``grid`` spatial dimension(s). Has size one in case of unstructured grid otherwise contains dimension names in the order (y, x). If not provided, calls :func:`~grid_process.helpers.get_spatial_dims` + prefer_projected : bool, optional + If True, return the native spatial coordinates when their CRS is projected with axes in meters (e.g. LV95), + even if longitude and latitude coordinates are also present. Native coordinates in degrees (e.g. rotated + pole) still give the longitude and latitude coordinates when present. Default is False. Returns ------- @@ -491,6 +573,13 @@ def get_coords_name_crs( pyproj representation of input grid coordinate reference system """ + if prefer_projected: + if spatial_dims is None: + spatial_dims = get_spatial_dims(grid) + native = _get_native_metric_coords(grid, spatial_dims) + if native is not None: + return native + latlon = get_latlon_coords(grid) if latlon is not None: grid_y, grid_x = latlon @@ -510,6 +599,35 @@ def get_coords_name_crs( raise ValueError("Missing spatial coordinates") +def _get_native_metric_coords( + grid: xr.Dataset | xr.DataArray, spatial_dims: tuple[str, ...] +) -> tuple[str, str, CRS] | None: + """ + Names of the native (y, x) spatial coordinates and their CRS if this CRS is projected with axes in meters, + otherwise None. Used by :func:`get_coords_name_crs` with ``prefer_projected=True``. + """ + + if len(spatial_dims) == 1: + try: + grid_y, grid_x = get_spatial_coords(grid) + except ValueError: + return None + else: + grid_y, grid_x = spatial_dims + if grid_y not in grid.coords or grid_x not in grid.coords: + return None + + if (grid_y, grid_x) == get_latlon_coords(grid, nondim=False): + return None + + try: + crs = get_grid_crs(grid) + except ValueError: # no CRS attribute: only longitude and latitude can be used + return None + + return (grid_y, grid_x, crs) if is_metric_crs(crs) else None + + def is_decreasing1d(coords: xr.DataArray) -> bool: """ Whether 1 dimensional ``coords`` are in decreasing order. diff --git a/grid_process/grid_interp.py b/grid_process/grid_interp.py index 80ed764..f9af7fa 100644 --- a/grid_process/grid_interp.py +++ b/grid_process/grid_interp.py @@ -22,10 +22,10 @@ import geopandas as gpd from scipy.interpolate import griddata from scipy.spatial import KDTree -from pyproj import CRS +from pyproj import CRS, Transformer from grid_process import CONFIG -from grid_process.helpers import make_accessor, reproject +from grid_process.helpers import make_accessor, outer_ring, reproject, sphere_as_wgs84 from grid_process import data_model as dm from grid_process.optimization import memoize from grid_process.ds_utils import ds_to_da, cast_dtype @@ -52,7 +52,11 @@ def __init__(self, ds: xr.Dataset): POI_DIM = POI_MODEL.dim POI_COORD_COL = POI_MODEL.coord_from SRC_ATTRS_KEEP = ("source", "title", "institution", "history", "references", "comment") -SWISS_CRS = ("EPSG:2056", "EPSG:21781") +# CRS of the nearest neighbor searches and triangulations when both grids are geographic (lat/lon, rotated pole): +# Swiss LV95 if the destination is close enough to Switzerland, otherwise earth-centered earth-fixed (ECEF) +LV95 = CRS.from_epsg(2056) +LV95_NORTHING_CENTER = 1_200_000.0 +ECEF = CRS.from_epsg(4978) InterpMethods = ( Literal[ @@ -91,13 +95,13 @@ def interp_temp( # pylint: disable=too-many-locals ---------- coords_flat : numpy.ndarray, shape (n_grids, m) array of flattened grid coordinates. n_grids is the number of model - grid points, m the number of coordinates dimensions (i.e. 3) + grid points, m the number of coordinates dimensions (3 or 4, with the altitude as last column) data_flat : numpy.ndarray, shape (n_grids, ...) array containing the flattened model data with various trailing dimensions pois_coords : numpy.ndarray , shape (n_pois, m) array of POI coordinates. n_pois is the number of POIs, m the number of - coordinates dimensions (i.e. 3) + coordinates dimensions (3 or 4, with the altitude as last column) z_fact : positive int, optional factor to multiply the z coordinates to find nearest neighbors based on a non-euclidean distance that penalizes more altitude differences. Default @@ -453,13 +457,13 @@ def interp_temp_fixgrad( ---------- coords_flat : numpy.ndarray, shape (n_grids, m) array of flattened grid coordinates. n_grids is the number of model - grid points, m the number of coordinates dimensions (i.e. 3) + grid points, m the number of coordinates dimensions (3 or 4, with the altitude as last column) data_flat : numpy.ndarray, shape (n_grids, ...) array containing the flattened model data with various trailing dimensions pois_coords : numpy.ndarray , shape (n_pois, m) array of POI coordinates. n_pois is the number of POIs, m the number of - coordinates dimensions (i.e. 3) + coordinates dimensions (3 or 4, with the altitude as last column) r_search : positive float, optional radius in model gridpoints that define the number of neighbors included in the search. Default is 4 @@ -548,13 +552,13 @@ def interp_nearest_alt( ---------- coords_flat : numpy.ndarray, shape (n_grids, m) array of flattened grid coordinates. n_grids is the number of model - grid points, m the number of coordinates dimensions (i.e. 3) + grid points, m the number of coordinates dimensions (3 or 4, with the altitude as last column) data_flat : numpy.ndarray, shape (n_grids, ...) array containing the flattened model data with various trailing dimensions pois_coords : numpy.ndarray , shape (n_pois, m) array of POI coordinates. n_pois is the number of POIs, m the number of - coordinates dimensions (i.e. 3) + coordinates dimensions (3 or 4, with the altitude as last column) r_search : positive float, optional radius in model gridpoints that define the number of neighbors included in the search. Default is 4 @@ -594,7 +598,7 @@ def _grid_assignation( k = round(np.pi * r_search**2) # find indexes and distances of nearest neighbor grids for all POIs - dist_nn_horz, ind_nn = nn_lookup_cached(coords_flat[:, :2], pois_coords[:, :2], k) + dist_nn_horz, ind_nn = nn_lookup_cached(coords_flat[:, :-1], pois_coords[:, :-1], k) dist_nn_vert = np.abs(coords_flat[ind_nn, -1] - pois_coords[:, [-1]]) dist_weighted = dist_nn_horz + z_weight * (dist_nn_vert - deltah_min).clip(0) @@ -617,13 +621,13 @@ def interp_idw( ---------- coords_flat : numpy.ndarray, shape (n_grids,m) array of flattened grid coordinates. n_grids is the number of model - grid points, m the number of coordinates dimensions (i.e. 2 or 3) + grid points, m the number of coordinates dimensions (2 to 4) data_flat : numpy.ndarray, shape (n_grids, ...) array containing the flattened model data with various trailing dimensions pois_coords : numpy.ndarray , shape (n_pois, m) array of POI coordinates. n_pois is the number of POIs, m the number of - coordinates dimensions (i.e. 2 or 3) + coordinates dimensions (2 to 4) k : positive int, optional number of considered nearest neighbors. Default is 4 weight_power : positive float, optional @@ -675,13 +679,13 @@ def interp_upscaled( ---------- coords_flat : numpy.ndarray, shape (n_grids, m) array of flattened grid coordinates. n_grids is the number of model - grid points, m the number of coordinates dimensions (i.e. 2 or 3) + grid points, m the number of coordinates dimensions (2 to 4) data_flat : numpy.ndarray, shape (n_grids, ...) array containing the flattened model data with various trailing dimensions pois_coords : numpy.ndarray , shape (n_pois, m) array of POI coordinates. n_pois is the number of POIs, m the number of - coordinates dimensions (i.e. 2 or 3). + coordinates dimensions (2 to 4). k : positive int, optional number of considered nearest neighbors. Default is 4 agg_func : callable, optional @@ -717,13 +721,13 @@ def interp_nearest(coords_flat: np.ndarray, data_flat: np.ndarray, pois_coords: ---------- coords_flat : numpy.ndarray, shape (n_grids, m) array of flattened grid coordinates. n_grids is the number of model - grid points, m the number of coordinates dimensions (i.e. 2 or 3). + grid points, m the number of coordinates dimensions (2 to 4). data_flat : numpy.ndarray, shape (n_grids, ...) array containing the flattened model data with various trailing dimensions pois_coords : numpy.ndarray , shape (n_pois, m) array of POI coordinates. n_pois is the number of POIs, m the number of - coordinates dimensions (i.e. 2 or 3). + coordinates dimensions (2 to 4). Returns ------- @@ -762,13 +766,13 @@ def interp_snowlim( ---------- coords_flat : numpy.ndarray, shape (n_grids, m) array of flattened grid coordinates. n_grids is the number of model - grid points, m the number of coordinates dimensions (i.e. 3) + grid points, m the number of coordinates dimensions (3 or 4, with the altitude as last column) data_flat : numpy.ndarray, shape (n_grids, ...) Array containing the flattened model data with various trailing dimension. pois_coords : numpy.ndarray , shape (n_pois, m) array of POI coordinates. n_pois is the number of POIs, m the number of - coordinates dimensions (i.e. 3) + coordinates dimensions (3 or 4, with the altitude as last column) k : positive int Number of considered nearest neighbors to check for altitude of NaN grid points. @@ -798,14 +802,14 @@ def interp_snowlim( data_flat = data_flat.reshape((data_shape[0], -1), order="C") # array with default values when there is only NaNs within distance d_max - poi_data = (pois_coords[:, 2] - 200).clip(0, 200).astype(data_flat.dtype, copy=False) + poi_data = (pois_coords[:, -1] - 200).clip(0, 200).astype(data_flat.dtype, copy=False) poi_data -= poi_data % 50 # floor to multiple of 50 poi_data = np.broadcast_to(poi_data[:, None], (n_pois, data_flat.shape[-1])).copy() for idx, array in enumerate(data_flat.T): ind = ~np.isnan(array) if ind.any(): - dist_nn, ind_nn = nn_lookup(coords_flat[ind, :2], pois_coords[:, :2], 1) + dist_nn, ind_nn = nn_lookup(coords_flat[ind, :-1], pois_coords[:, :-1], 1) bool_dist = dist_nn < d_max poi_data[bool_dist, idx] = array[ind][ind_nn[bool_dist]] @@ -824,13 +828,13 @@ def _fillna_alt(coords_flat: np.ndarray, data_flat: np.ndarray, pois_coords: np. ---------- coords_flat : numpy.ndarray, shape (n_grids, m) array of flattened grid coordinates. n_grids is the number of model - grid points, m the number of coordinates dimensions (i.e. 3) + grid points, m the number of coordinates dimensions (3 or 4, with the altitude as last column) data_flat : numpy.ndarray, shape (n_grids, ...) array containing the flattened model data with various trailing dimensions pois_coords : numpy.ndarray , shape (n_pois, m) array of POI coordinates. n_pois is the number of POIs, m the number of - coordinates dimensions (i.e. 3) + coordinates dimensions (3 or 4, with the altitude as last column) k : positive int number of considered nearest neighbors to check for altitude of NaN @@ -846,7 +850,7 @@ def _fillna_alt(coords_flat: np.ndarray, data_flat: np.ndarray, pois_coords: np. """ ndim_trail = data_flat.ndim - 1 - _, ind_nn = nn_lookup(coords_flat[:, :2], pois_coords[:, :2], k) + _, ind_nn = nn_lookup(coords_flat[:, :-1], pois_coords[:, :-1], k) data_poi = data_flat[ind_nn] ind_na = np.isnan(data_poi) alt_poi = coords_flat[ind_nn, -1].reshape(ind_nn.shape + (1,) * ndim_trail) @@ -942,6 +946,20 @@ def interp2poi( (e.g., excluding water pixels with `ds.where(ds.fr_land > 0.5).interp2poi(..., try_reg=False)`). - For regular grid interpolation, all NaN grid points are kept. - Environment variables allow flexible support for custom grid and POI data models. + - Outside of the regular grid interpolation, nearest neighbors are searched (and triangulations for 'linear', + 'linear_3D' and 'cubic' are done) with distances in meters, never in latitude/longitude degrees. The source + grid uses its native coordinates if they are projected in meters (e.g. LV95, LV03, UTM), even if latitude and + longitude coordinates are present, otherwise its latitude/longitude. The coordinates are converted to: + + - the CRS projected in meters of the source grid or of the destination, if any (the one of the larger grid + if both are); + - otherwise Swiss LV95 (EPSG:2056) if the destination is within ``lv95_max_offset`` meters (default 700 km, + set with the environment variable GRID_PROCESS__lv95_max_offset) of the LV95 central northing + (1 200 000 m), where the LV95 scale error is small (0.6 % at 700 km); + - otherwise earth-centered earth-fixed coordinates (ECEF, EPSG:4978), e.g. for global grids. The methods + 'linear', 'linear_3D' and 'cubic' are not supported in this case. + + Methods with altitude use it as an additional coordinate in meters. """ def _fullnan_ds(data: xr.DataArray) -> xr.Dataset: @@ -1143,10 +1161,10 @@ def nn_lookup(grid_coords: np.ndarray, points: np.ndarray, k: int) -> tuple[np.n ---------- grid_coords : numpy.ndarray, shape (n, m) array of flattened grid coordinates. N is the number of model grid - points, m the number of coordinates dimensions (i.e. 2 or 3) + points, m the number of coordinates dimensions (2 to 4) points : numpy.ndarray, shape (p, m) array of POIs coordinates. P is the number of POIs, m the number of - coordinates dimensions (i.e. 2 or 3) + coordinates dimensions (2 to 4) k : positive int number of considered nearest neighbors @@ -1242,42 +1260,32 @@ def _flatten_concat( method: str, ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: """ - Flattens the grid and concatenate columnwise `x` and `y` coordinates into a - single 2D array. Also, reprojects the POIs coordinates if they are not in - the same CRS than the model coordinates + Flattens the grid and concatenate columnwise the horizontal coordinates (and the altitude for + ``HEIGHT_METHODS``) into a single 2D array, in the CRS chosen by :func:`_choose_kdtree_crs`. The POIs + coordinates are converted in the same CRS. """ data, grid_y, grid_x, grid_crs = _flatten_coords_crs(data, spatial_dims) - if method in HEIGHT_METHODS: - # include elevation - surf_alt = dm.get_surf_alt(data) - coords_flat = _stack_coords( - data[grid_x].values, - data[grid_y].values, - data[surf_alt].values, - ) - - if grid_crs not in SWISS_CRS: - # project to CH1903+/LV95 to have units in [m] - new_crs = CRS.from_string(SWISS_CRS[0]) - coords_flat[:, 0], coords_flat[:, 1] = _reproject_grid_cached( - coords_flat[:, 0], coords_flat[:, 1], grid_crs, new_crs - ) - grid_crs = new_crs - else: - coords_flat = _stack_coords(data[grid_x].values, data[grid_y].values) - - # Reproject POIs coordinates in the grid CRS - pois_coords = _convert_poi(poi_info, grid_crs, coords_flat.dtype) - if method in HEIGHT_METHODS: - pois_coords_cat = np.c_[ - pois_coords[0], - pois_coords[1], - dm.get_poi_alt(poi_info), - ] - else: - pois_coords_cat = np.c_[pois_coords[0], pois_coords[1]] + target_crs = _choose_kdtree_crs( + grid_crs, + poi_info.crs.to_2d(), + src_larger=True, + dst_lv95=lambda: _convert_poi(poi_info, LV95, np.dtype(np.float64)), + ) + _check_griddata_crs(method, target_crs) + + height = method in HEIGHT_METHODS + coords_flat = _kdtree_coords( + data[grid_x].values, + data[grid_y].values, + grid_crs, + target_crs, + data[dm.get_surf_alt(data)].values if height else None, + ) + pois_coords_cat = _poi_kdtree_coords( + poi_info, target_crs, coords_flat.dtype, dm.get_poi_alt(poi_info) if height else None + ) if method == "snowlim": return coords_flat, data.values, pois_coords_cat @@ -1348,6 +1356,135 @@ def _reproject_grid_cached( return x_coords.astype(np.float64, copy=False), y_coords.astype(np.float64, copy=False) +@memoize(CONFIG.cache_size.reproject_grid) +def _to_ecef(x_coords: np.ndarray, y_coords: np.ndarray, src_crs: CRS) -> np.ndarray: + """ + Earth-centered earth-fixed (ECEF, EPSG:4978) coordinates [m] at zero ellipsoidal height, shape (n, 3), of + horizontal coordinates in ``src_crs``. That can be cached. + """ + + transformer = Transformer.from_crs(sphere_as_wgs84(src_crs), ECEF, always_xy=True) + x_coords = np.asarray(x_coords, dtype=np.float64) + y_coords = np.asarray(y_coords, dtype=np.float64) + return np.column_stack(transformer.transform(x_coords, y_coords, np.zeros_like(x_coords))) + + +def _to_ecef_cached(x_coords: np.ndarray, y_coords: np.ndarray, src_crs: CRS) -> np.ndarray: + """ + Cached conversion of horizontal coordinates to ECEF coordinates, see :func:`_to_ecef`. + """ + + hkey = (x_coords.tobytes(), y_coords.tobytes(), x_coords.shape, src_crs, "ECEF") + return _to_ecef(x_coords, y_coords, src_crs, hkey=hkey) # type: ignore[call-arg] + + +def _choose_kdtree_crs( + src_crs: CRS, + dst_crs: CRS, + src_larger: bool, + dst_lv95: Callable[[], tuple[np.ndarray, np.ndarray]], +) -> CRS: + """ + Choose the CRS in which the nearest neighbor searches (KDTree) and triangulations (griddata) of the + generic interpolation path are done, so that distances are euclidean distances in meters (never in degrees): + + - if exactly one of the source and destination CRS is projected with axes in meters (e.g. LV95, LV03, UTM), + this one, so that only the other side is reprojected; + - if both are, the CRS of the larger grid (``src_larger``), whose reprojection would be the most costly; + - if both are geographic (e.g. latitude/longitude or rotated pole), Swiss LV95 (EPSG:2056) if all the + destination points are within ``CONFIG.lv95_max_offset`` meters of the LV95 central northing (where the LV95 + scale error is small), otherwise earth-centered earth-fixed coordinates (ECEF, EPSG:4978). + + Parameters + ---------- + src_crs, dst_crs : pyproj.CRS + CRS of the source grid and of the destination (grid or POIs) coordinates + src_larger : bool + whether the source grid has more points than the destination + dst_lv95 : callable + returns the LV95 coordinates (x, y) of the destination points to check against ``CONFIG.lv95_max_offset``: + the POIs, or the outer ring of a destination grid (see :func:`~grid_process.helpers.outer_ring`). Only called + when both CRS are geographic. + + Returns + ------- + pyproj.CRS + the chosen CRS. ``crs.is_geocentric`` is True for ECEF. + """ + + src_metric, dst_metric = dm.is_metric_crs(src_crs), dm.is_metric_crs(dst_crs) + if src_metric and dst_metric: + return src_crs if src_larger else dst_crs + if src_metric: + return src_crs + if dst_metric: + return dst_crs + + x_lv95, y_lv95 = dst_lv95() + offset = np.abs(np.asarray(y_lv95, dtype=np.float64) - LV95_NORTHING_CENTER) + if np.isfinite(x_lv95).all() and np.isfinite(offset).all() and offset.max() <= CONFIG.lv95_max_offset: + return LV95 + + logger.debug("Destination beyond %s m of the LV95 central northing: interpolation in ECEF", CONFIG.lv95_max_offset) + return ECEF + + +def _check_griddata_crs(method: str, target_crs: CRS) -> None: + """ + Raise an error for griddata methods (triangulation) in ECEF coordinates, see :func:`_choose_kdtree_crs`. + """ + + if method in GRIDDATA_METHODS and target_crs.is_geocentric: + raise ValueError( + f"Method {method!r} is not supported for geographic grids beyond {CONFIG.lv95_max_offset / 1000:.0f} km" + " of the Swiss LV95 central northing, which are interpolated in earth-centered earth-fixed (ECEF)" + " coordinates: a Delaunay triangulation of points close to the Earth's surface is degenerate in ECEF." + " Use e.g. method='idw' instead, or try_reg=True for a regular grid with 1D coordinates (or adjust" + " GRID_PROCESS__lv95_max_offset)." + ) + + +def _kdtree_coords( + x_coords: np.ndarray, + y_coords: np.ndarray, + src_crs: CRS, + target_crs: CRS, + alt: np.ndarray | None = None, +) -> np.ndarray: + """ + Stack columnwise the flattened horizontal coordinates converted to ``target_crs``, and the altitude ``alt`` + if given (always as last column): ``[x, y(, alt)]`` for a projected ``target_crs``, ``[X, Y, Z(, alt)]`` + in float64 for ECEF. + """ + + if target_crs.is_geocentric: + ecef = _to_ecef_cached(x_coords, y_coords, src_crs) + return ecef.copy() if alt is None else np.column_stack((ecef, alt.astype(np.float64))) + + coords = _stack_coords(x_coords, y_coords) if alt is None else _stack_coords(x_coords, y_coords, alt) + if src_crs != target_crs: + coords[:, 0], coords[:, 1] = _reproject_grid_cached(coords[:, 0], coords[:, 1], src_crs, target_crs) + return coords + + +def _poi_kdtree_coords( + poi_info: gpd.GeoDataFrame, target_crs: CRS, dtype: np.dtype, alt: np.ndarray | None = None +) -> np.ndarray: + """ + POIs coordinates in ``target_crs``, stacked like :func:`_kdtree_coords`. + """ + + if target_crs.is_geocentric: + xy = poi_info.get_coordinates().to_numpy(dtype=np.float64) + ecef = _to_ecef_cached(xy[:, 0], xy[:, 1], poi_info.crs.to_2d()) + return ecef.copy() if alt is None else np.column_stack((ecef, alt.astype(np.float64))) + + pois_coords = _convert_poi(poi_info, target_crs, dtype) + if alt is None: + return np.c_[pois_coords[0], pois_coords[1]] + return np.c_[pois_coords[0], pois_coords[1], alt] + + @dataset_accessor def interp2grid( data: xr.Dataset, @@ -1390,8 +1527,9 @@ def interp2grid( For methods requiring altitude, ensure a surface altitude coordinate is present; extend supported names via GRID_PROCESS__data_model__grid__surf_alt. dst_grid : xarray.Dataset - The destination grid to interpolate to. Same supported data models as the source grid, including conventions for - dimensions and coordinates as well as spatial CRS. + The destination grid to interpolate to. It must be a structured grid with two spatial dimensions (y, x): + unstructured (1D) grids are only supported as source grid. Same supported data models as the source grid, + including conventions for dimensions and coordinates as well as spatial CRS. For methods requiring altitude, ensure a surface altitude coordinate is also present. The destination dataset may contain only spatial coordinates or also data variables which will be ignored. method : str @@ -1414,6 +1552,12 @@ def interp2grid( xr.Dataset Dataset with values from `data` interpolated onto the coordinates of `dst_grid`. + Raises + ------ + ValueError + If ``dst_grid`` is not a structured grid, or for the methods 'linear', 'linear_3D' and 'cubic' when the + interpolation is done in ECEF coordinates (see notes). + See also -------- :func:`interp_temp`, :func:`interp_temp_fixgrad`, :func:`interp_nearest_alt`, @@ -1426,6 +1570,20 @@ def interp2grid( - Recommended methods: 'nearest', 'linear' (if the source grid is regular with 1D coordinates), 'idw' (with k=4), 'temp_fixgrad', 'nearest_3D', 'nearest_alt'. - Environment variables allow flexible support for custom grid data models. + - Outside of the regular grid interpolation, nearest neighbors are searched (and triangulations for 'linear', + 'linear_3D' and 'cubic' are done) with distances in meters, never in latitude/longitude degrees. The source + grid uses its native coordinates if they are projected in meters (e.g. LV95, LV03, UTM), even if latitude and + longitude coordinates are present, otherwise its latitude/longitude. The coordinates are converted to: + + - the CRS projected in meters of the source grid or of the destination, if any (the one of the larger grid + if both are); + - otherwise Swiss LV95 (EPSG:2056) if the destination is within ``lv95_max_offset`` meters (default 700 km, + set with the environment variable GRID_PROCESS__lv95_max_offset) of the LV95 central northing + (1 200 000 m), where the LV95 scale error is small (0.6 % at 700 km); + - otherwise earth-centered earth-fixed coordinates (ECEF, EPSG:4978), e.g. for global grids. The methods + 'linear', 'linear_3D' and 'cubic' are not supported in this case. + + Methods with altitude use it as an additional coordinate in meters. """ def _array2ds(data: xr.DataArray) -> xr.Dataset: @@ -1453,6 +1611,11 @@ def _array2ds(data: xr.DataArray) -> xr.Dataset: src_spatial_dims = dm.get_spatial_dims(data) dst_spatial_dims = dm.get_spatial_dims(dst_grid) + if len(dst_spatial_dims) != 2: + raise ValueError( + f"The destination grid must be a structured grid with two spatial dimensions (y, x), got" + f" {dst_spatial_dims}. Unstructured grids are only supported as source grid." + ) data = dm.set_grid_mapping(data) dst_grid = dm.set_grid_mapping(dst_grid) dst_grid = dst_grid.drop_dims(set(dst_grid.dims) - set(dst_spatial_dims)) @@ -1612,66 +1775,45 @@ def _flatten_concat_grids( # pylint: disable=too-many-locals method: str, ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: """ - Flattens the grids and concatenate columnwise `x` and `y` coordinates into a - single 2D array. Also, reprojects the destination grid coordinates if they are not - in the same CRS than the input model coordinates. + Flattens the grids and concatenate columnwise the horizontal coordinates (and the altitude for + ``HEIGHT_METHODS``) into a single 2D array per grid, in the CRS chosen by :func:`_choose_kdtree_crs`. """ - def _choose_crs_meters() -> CRS: + def _dst_lv95() -> tuple[np.ndarray, np.ndarray]: """ - Choose the target CRS with units in [m] for reprojection. + LV95 coordinates of the outer ring of the destination grid, to check its extent. """ - if reproj_dst: - if grid_crs in SWISS_CRS: - return grid_crs - return CRS.from_string(SWISS_CRS[1]) if dst_grid_crs == SWISS_CRS[1] else CRS.from_string(SWISS_CRS[0]) - - if dst_grid_crs in SWISS_CRS: - return dst_grid_crs - return CRS.from_string(SWISS_CRS[1]) if grid_crs == SWISS_CRS[1] else CRS.from_string(SWISS_CRS[0]) + x_ring, y_ring = outer_ring(dst_grid[dst_grid_x].values, dst_grid[dst_grid_y].values) + return reproject(x_ring.astype(np.float64), y_ring.astype(np.float64), dst_grid_crs, LV95) + dst_grid_y, dst_grid_x, dst_grid_crs = dm.get_coords_name_crs(dst_grid, dst_spatial_dims, prefer_projected=True) data, grid_y, grid_x, grid_crs = _flatten_coords_crs(data, spatial_dims) flat_dim = spatial_dims[0] if len(spatial_dims) == 1 else "cell" - dst_grid, dst_grid_y, dst_grid_x, dst_grid_crs = _flatten_coords_crs(dst_grid, dst_spatial_dims, flat_dim) - - reproj_dst = dst_grid.sizes[flat_dim] <= data.sizes[flat_dim] - - if method in HEIGHT_METHODS: - surf_alt = dm.get_surf_alt(data) - dst_surf_alt = dm.get_surf_alt(dst_grid) - # include elevation - coords_flat = _stack_coords( - data[grid_x].values, - data[grid_y].values, - data[surf_alt].values, - ) - dst_coords_flat = _stack_coords( - dst_grid[dst_grid_x].values, - dst_grid[dst_grid_y].values, - dst_grid[dst_surf_alt].values, - ) - target_crs = _choose_crs_meters() - coords_flat[:, 0], coords_flat[:, 1] = _reproject_grid_cached( - coords_flat[:, 0], coords_flat[:, 1], grid_crs, target_crs - ) - dst_coords_flat[:, 0], dst_coords_flat[:, 1] = _reproject_grid_cached( - dst_coords_flat[:, 0], dst_coords_flat[:, 1], dst_grid_crs, target_crs - ) - return coords_flat, data.values, dst_coords_flat - - # Non-height methods - coords_flat = _stack_coords(data[grid_x].values, data[grid_y].values) - dst_coords_flat = _stack_coords(dst_grid[dst_grid_x].values, dst_grid[dst_grid_y].values) - if reproj_dst: # reproject destination coordinates in the grid CRS - dst_coords_flat[:, 0], dst_coords_flat[:, 1] = _reproject_grid_cached( - dst_coords_flat[:, 0], dst_coords_flat[:, 1], dst_grid_crs, grid_crs - ) - return coords_flat, data.values, dst_coords_flat + dst_flat, _, _, _ = _flatten_coords_crs(dst_grid, dst_spatial_dims, flat_dim) - # reproject grid coordinates in the destination CRS - coords_flat[:, 0], coords_flat[:, 1] = _reproject_grid_cached( - coords_flat[:, 0], coords_flat[:, 1], grid_crs, dst_grid_crs + target_crs = _choose_kdtree_crs( + grid_crs, + dst_grid_crs, + src_larger=dst_flat.sizes[flat_dim] <= data.sizes[flat_dim], + dst_lv95=_dst_lv95, + ) + _check_griddata_crs(method, target_crs) + + height = method in HEIGHT_METHODS + coords_flat = _kdtree_coords( + data[grid_x].values, + data[grid_y].values, + grid_crs, + target_crs, + data[dm.get_surf_alt(data)].values if height else None, + ) + dst_coords_flat = _kdtree_coords( + dst_flat[dst_grid_x].values, + dst_flat[dst_grid_y].values, + dst_grid_crs, + target_crs, + dst_flat[dm.get_surf_alt(dst_flat)].values if height else None, ) return coords_flat, data.values, dst_coords_flat @@ -1692,16 +1834,16 @@ def _flatten_coords_crs( flat_dim: str = "cell", ) -> tuple[xr.DataArray | xr.Dataset, str, str, CRS]: """ - Flatten input dataset if not already and extract spatial coordinates and - corresponding crs to be used. + Flatten input dataset if not already and extract spatial coordinates and corresponding crs to be used: + native coordinates if their CRS is projected with axes in meters, otherwise latitude and longitude + if present (see :func:`~grid_process.data_model.get_coords_name_crs`). """ + grid_y, grid_x, grid_crs = dm.get_coords_name_crs(grid, spatial_dims, prefer_projected=True) + if len(spatial_dims) > 1: - # flatten grid + # flatten grid: 1D dimension coordinates become levels of the flat dimension grid = grid.stack({flat_dim: spatial_dims}).transpose(flat_dim, ...) - spatial_dims = (flat_dim,) - - grid_y, grid_x, grid_crs = dm.get_coords_name_crs(grid, spatial_dims) return grid, grid_y, grid_x, grid_crs @@ -1783,12 +1925,8 @@ def extra_buffer(ratio: int) -> int: ) if grid_crs != dst_grid_crs: - if x_coords.ndim == 1: - x_coords, y_coords = np.meshgrid(x_coords, y_coords) - # only keep the outer rectangle - x_coords = np.append(x_coords[[0, -1], :].ravel(), x_coords[1:-1, [0, -1]].ravel()) - y_coords = np.append(y_coords[[0, -1], :].ravel(), y_coords[1:-1, [0, -1]].ravel()) + x_coords, y_coords = outer_ring(x_coords, y_coords) # Reproject destination coordinates in the grid CRS x_coords, y_coords = reproject(x_coords, y_coords, dst_grid_crs, grid_crs) @@ -2006,6 +2144,9 @@ def _combine_attrs(src_attrs: dict, dst_attrs: dict) -> dict: return combined_attrs +# methods relying on scipy.interpolate.griddata (Delaunay triangulation), not supported in ECEF coordinates +GRIDDATA_METHODS = ("linear", "linear_3D", "cubic") + HEIGHT_METHODS = ( "temp", "temp_fixgrad", diff --git a/grid_process/grid_interp_utils.py b/grid_process/grid_interp_utils.py index d51bc89..5c3f5d9 100755 --- a/grid_process/grid_interp_utils.py +++ b/grid_process/grid_interp_utils.py @@ -14,7 +14,7 @@ from pyproj import CRS import xarray as xr -from grid_process.helpers import reproject +from grid_process.helpers import outer_ring, reproject from grid_process import data_model as dm @@ -164,14 +164,18 @@ def create_dst_grid_from_src( # pylint: disable=too-many-locals spatial_dims = dm.get_spatial_dims(src_grid) src_grid = dm.set_grid_mapping(src_grid) - src_y, src_x, src_crs = dm.get_coords_name_crs(src_grid, spatial_dims) + src_y, src_x, src_crs = dm.get_coords_name_crs(src_grid, spatial_dims, prefer_projected=True) dst_crs = CRS.from_user_input(dst_grid_crs) - x_min, x_max = src_grid[src_x].values.min(), src_grid[src_x].values.max() - y_min, y_max = src_grid[src_y].values.min(), src_grid[src_y].values.max() - x_bnds = (x_min, x_min, x_max, x_max) - y_bnds = (y_min, y_max, y_min, y_max) - x_bnds, y_bnds = reproject(x_bnds, y_bnds, src_crs, dst_crs) - x_bnds, y_bnds = (min(x_bnds), max(x_bnds)), (min(y_bnds), max(y_bnds)) + + # bounding box of the source domain in the destination CRS: of the outer ring of a structured grid (which + # encloses the reprojected grid), of all points of an unstructured one + src_x_coords = src_grid[src_x].values.astype(np.float64) + src_y_coords = src_grid[src_y].values.astype(np.float64) + if len(spatial_dims) == 2: + src_x_coords, src_y_coords = outer_ring(src_x_coords, src_y_coords) + x_src_dst, y_src_dst = reproject(src_x_coords, src_y_coords, src_crs, dst_crs) + x_bnds = (float(np.nanmin(x_src_dst)), float(np.nanmax(x_src_dst))) + y_bnds = (float(np.nanmin(y_src_dst)), float(np.nanmax(y_src_dst))) if len(spatial_dims) == 1: # need to infer expected sizes for x and y dims xy_ratio = (x_bnds[1] - x_bnds[0]) / (y_bnds[1] - y_bnds[0]) diff --git a/grid_process/helpers.py b/grid_process/helpers.py index 54881f3..bcd027b 100644 --- a/grid_process/helpers.py +++ b/grid_process/helpers.py @@ -167,8 +167,78 @@ def reproject( same as input ``x_coords`` in the new CRS and cast as float64 y_coords_new : numpy.ndarray, list or tuple of float64 same as input ``y_coords`` in the new CRS and cast as float64 + + Note + ---- + Geographic CRS defined on a sphere without datum (e.g. the rotated pole of a CF grid mapping with + ``earth_radius``) are considered to be based on WGS84, see :func:`sphere_as_wgs84`. CRS parsed from such a CF + "grid_mapping" attribute are already based on WGS84 (see :func:`~grid_process.data_model.parse_grid_mapping`), + this covers the other ones (e.g. "crs" attribute, POIs CRS or direct calls). """ - transformer = Transformer.from_crs(src_crs, dst_crs, always_xy=True) + transformer = Transformer.from_crs(sphere_as_wgs84(src_crs), sphere_as_wgs84(dst_crs), always_xy=True) return transformer.transform(x_coords, y_coords) + + +def sphere_as_wgs84(crs: CRS | str) -> CRS: + """ + Returns a geographic CRS defined on a sphere (e.g. the rotated pole or latitude/longitude of a CF grid mapping + with ``earth_radius``, as used by NWP models) with WGS84 as base CRS instead, keeping the pole rotation if any. + Other CRS are returned unchanged. + + The latitudes and longitudes on the sphere of NWP models are usually considered as WGS84 latitudes and + longitudes (e.g. the 2D ``latitude`` and ``longitude`` coordinates of their output). Without a defined datum, + pyproj transforms them to other datums with a "ballpark" transformation that leaves out the datum shift, e.g. + ~180 m in Switzerland between a rotated pole grid and Swiss LV95 coordinates. With WGS84 as base CRS, the datum + shift is applied as for WGS84 latitudes and longitudes. The rotation of the pole is the same on any ellipsoid. + + Parameters + ---------- + crs : pyproj.CRS or equivalent string + anything accepted by pyproj.CRS.from_user_input + + Returns + ------- + pyproj.CRS + """ + + crs = CRS.from_user_input(crs) + ellipsoid = crs.ellipsoid + if not crs.is_geographic or ellipsoid is None or ellipsoid.semi_major_metre != ellipsoid.semi_minor_metre: + return crs + + wgs84 = CRS.from_epsg(4326) + crs_json = crs.to_json_dict() + if "base_crs" not in crs_json: # latitude and longitude on the sphere + return wgs84 + + # derived geographic CRS, e.g. rotated pole: same conversion (rotation), WGS84 as base CRS + crs_json["base_crs"] = wgs84.to_json_dict() + return CRS.from_json_dict(crs_json) + + +def outer_ring(x_coords: np.ndarray, y_coords: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """ + Coordinates of the outer ring of points of a structured grid, from 1D (dimension) or 2D coordinates in the + order (y, x). On a grid, it encloses the image of the whole grid in any continuous projection, e.g. the bounding + box of a reprojected grid is the bounding box of its reprojected outer ring. + + Parameters + ---------- + x_coords, y_coords : numpy.ndarray + 1D coordinates of the x and y dimensions, or 2D coordinates of shape (y, x) + + Returns + ------- + x_ring, y_ring : numpy.ndarray + 1D coordinates of the outer ring points (first and last rows, then first and last columns) + """ + + if x_coords.ndim == 1: + x_coords, y_coords = np.meshgrid(x_coords, y_coords) + + def ring(coords: np.ndarray) -> np.ndarray: + return np.append(coords[[0, -1], :].ravel(), coords[1:-1, [0, -1]].ravel()) + + return ring(x_coords), ring(y_coords) diff --git a/test/test_data_model.py b/test/test_data_model.py index 48e56b2..6197b13 100644 --- a/test/test_data_model.py +++ b/test/test_data_model.py @@ -6,6 +6,8 @@ """Tests for the helpers module""" +import json + import pytest import numpy as np import xarray as xr @@ -222,6 +224,30 @@ def test_set_grid_mapping(data_cf_2dflat): dm.set_grid_mapping(ds_multi_gridmap) +def test_set_grid_mapping_numpy_attrs(): + """ + CF grid mapping attributes read from netCDF files can be numpy arrays or scalars (not JSON serializable as such), + e.g. a Lambert conformal conic with two standard parallels. They are serialized and parsed with cache. + """ + + lambert = { + "grid_mapping_name": "lambert_conformal_conic", + "standard_parallel": np.array([45.0, 50.0]), + "longitude_of_central_meridian": np.float32(10.0), + "latitude_of_projection_origin": np.float64(47.0), + "earth_radius": np.int32(6371229), + } + ds = xr.Dataset( + {"var": (("y", "x"), np.zeros((2, 3)), {"grid_mapping": "lambert"}), "lambert": ((), 0, lambert)}, + coords={"y": [0.0, 1000.0], "x": [0.0, 1000.0, 2000.0]}, + ) + + results = dm.set_grid_mapping(ds) + assert json.loads(results.grid_mapping)["standard_parallel"] == [45.0, 50.0] + assert dm.get_grid_crs(results) == CRS.from_cf(lambert) + assert dm.parse_grid_mapping(lambert) is dm.parse_grid_mapping(lambert) + + @pytest.mark.parametrize( "data_cf_2dflat", ["2d_grid", "flat_grid"], @@ -418,6 +444,100 @@ def test_get_coords_name_crs(data_native_lv95_2dflat, data_native_rotlatlon_2dfl ) +@pytest.mark.parametrize( + "data_native_lv95_2dflat,data_native_rotlatlon_2dflat", + [("2d_grid", "2d_grid"), ("flat_grid", "flat_grid")], + indirect=True, +) +def test_get_coords_name_crs_prefer_projected(data_native_lv95_2dflat, data_native_rotlatlon_2dflat): + """ + get_coords_name_crs with prefer_projected=True returns native coordinates in meters before latitude/longitude + """ + + # native LV95 with latitude/longitude: LV95 + lv95 = dm.get_coords_name_crs(data_native_lv95_2dflat, prefer_projected=True) + assert lv95 == ("y", "x", CRS.from_string("EPSG:2056")) + da = data_native_lv95_2dflat[list(data_native_lv95_2dflat)[0]] + # a data variable has no CRS attribute: latitude/longitude, unless the attributes are copied (as ds_to_da does) + assert dm.get_coords_name_crs(da, prefer_projected=True)[:2] == ("latitude", "longitude") + da = da.assign_attrs(data_native_lv95_2dflat.attrs) + assert dm.get_coords_name_crs(da, prefer_projected=True) == lv95 + # default unchanged: latitude/longitude + assert dm.get_coords_name_crs(data_native_lv95_2dflat)[2] == CRS.from_string("EPSG:4326") + + # native rotated pole (in degrees) with latitude/longitude: latitude/longitude + rotated = dm.get_coords_name_crs(data_native_rotlatlon_2dflat, prefer_projected=True) + assert rotated == ("latitude", "longitude", CRS.from_string("EPSG:4326")) + + # no CRS attribute: only latitude/longitude can be used + no_crs = data_native_lv95_2dflat.copy() + no_crs.attrs.pop("grid_mapping") + assert dm.get_coords_name_crs(no_crs, prefer_projected=True) == ("latitude", "longitude", CRS.from_epsg(4326)) + + +def test_parse_grid_mapping(monkeypatch): + """ + CF latitude/longitude and rotated pole mappings on a sphere are built directly on WGS84 (fast, without + pyproj.CRS.from_cf), equivalent to CRS.from_cf with WGS84 as base. Parsed grid mappings are cached. + """ + + from grid_process.helpers import sphere_as_wgs84 + + rotated = { + "earth_radius": 6371229.0, + "grid_mapping_name": "rotated_latitude_longitude", + "grid_north_pole_latitude": 43.0, + "grid_north_pole_longitude": 190.0, + "north_pole_grid_longitude": 0.0, + } + expected = sphere_as_wgs84(CRS.from_cf(rotated)) + lambert = { + "grid_mapping_name": "lambert_conformal_conic", + "standard_parallel": [45.0, 50.0], + "longitude_of_central_meridian": 10.0, + "latitude_of_projection_origin": 47.0, + "earth_radius": 6371229.0, + } + expected_lambert = CRS.from_cf(lambert) + + def fail(*args, **kwargs): + raise AssertionError("CRS.from_cf must not be called") + + with monkeypatch.context() as patch: + patch.setattr(CRS, "from_cf", fail) + assert dm.parse_grid_mapping(rotated) == expected + assert dm.parse_grid_mapping(json.dumps(rotated)) == expected + assert dm.parse_grid_mapping({"grid_mapping_name": "latitude_longitude"}) == CRS.from_epsg(4326) + assert dm.parse_grid_mapping({"epsg_code": "EPSG:2056"}) == CRS.from_epsg(2056) + + # other mappings: pyproj.CRS.from_cf, cached on the JSON string + # (e.g. latitude/longitude on another datum given only by the GDAL "spatial_ref" WKT) + ch1903 = {"grid_mapping_name": "latitude_longitude", "spatial_ref": CRS.from_epsg(4150).to_wkt()} + assert dm.parse_grid_mapping(ch1903) == CRS.from_epsg(4150) + assert dm.parse_grid_mapping(lambert) == expected_lambert + assert dm.parse_grid_mapping(lambert) is dm.parse_grid_mapping(lambert) + assert dm.parse_grid_mapping(json.dumps(lambert)) is dm.parse_grid_mapping(json.dumps(lambert)) + + +def test_is_metric_crs(): + """ + Simple testcase for is_metric_crs + """ + + rotated = CRS.from_cf( + { + "grid_mapping_name": "rotated_latitude_longitude", + "grid_north_pole_latitude": 43.0, + "grid_north_pole_longitude": -170.0, + "earth_radius": 6371229.0, + } + ) + for epsg in (2056, 21781, 32632, 3035): # LV95, LV03, UTM 32N, LAEA Europe + assert dm.is_metric_crs(CRS.from_epsg(epsg)) + for crs in (CRS.from_epsg(4326), CRS.from_epsg(4978), CRS.from_epsg(2263), rotated): # lat/lon, ECEF, US feet + assert not dm.is_metric_crs(crs) + + def test_config_extended(): """ Simple testcase to make sure that extending the data model by exporting diff --git a/test/test_grid_interp.py b/test/test_grid_interp.py index 264385c..27ff77d 100644 --- a/test/test_grid_interp.py +++ b/test/test_grid_interp.py @@ -605,6 +605,7 @@ def test_flatten_concat(model_data, poi_def): import grid_process.grid_interp as gri from grid_process.ds_utils import ds_to_da from grid_process.data_model import get_spatial_dims + from grid_process import data_model as dm methods_1 = ("nearest", "linear", "cubic", "idw", "upscaled") methods_2 = ( @@ -653,14 +654,13 @@ def test_flatten_concat(model_data, poi_def): assert pois_coords.shape == (n_pois, n_coords) + # grid and POIs both in lat/lon over Switzerland: coords transformed to 'EPSG:2056' (CH1903+/LV95) for all + # methods, so that the nearest neighbor searches and triangulations use distances in meters + assert (pois_coords[:, :2] > 1e6).all() and (pois_coords[:, :2] < 3e6).all() + assert (model_coords_flat[:, :2] > 1e6).all() and (model_coords_flat[:, :2] < 3e6).all() if method in methods_2: - # coords transformed to 'EPSG:2056' (CH1903+/LV95) - assert (pois_coords[:, :2] > 1e5).all() and (pois_coords[:, :2] < 1e7).all() - assert (model_coords_flat[:, :2] > 1e5).all() and (model_coords_flat[:, :2] < 1e7).all() - else: - # coords still in 'EPSG:4326' (lat/lon) - assert (pois_coords[:, :2] > -180).all() and (pois_coords[:, :2] < 180).all() - assert (model_coords_flat[:, :2] > -180).all() and (model_coords_flat[:, :2] < 180).all() + # altitude as last column + np.testing.assert_array_equal(pois_coords[:, -1], dm.get_poi_alt(poi_def)) @pytest.mark.parametrize( @@ -972,6 +972,19 @@ def test_interp2grid(model_data_2dflat, data_native_lv95, data_native_rotlatlon) model_data_2dflat = model_data_2dflat.assign_coords(coords).assign_attrs(grid_mapping={"epsg_code": "EPSG:4326"}) + def assert_same_as_regular(regular: xr.Dataset, generic: xr.Dataset, method: str) -> None: + """ + The generic path triangulates in LV95 (meters), the regular path interpolates bilinearly in lat/lon: since + the test data are linear in lat/lon, 'linear' differs by less than 0.2 % of the grid step along x (528 for + test_var_1, 1320 for test_var_2). + """ + + if method == "linear": + xr.testing.assert_allclose(regular, generic, rtol=0, atol=2.5) + assert regular.attrs == generic.attrs + else: + xr.testing.assert_identical(regular, generic) + for method in ["nearest", "linear", "nearest_3D"]: # unregular grid interpolation with lat/lon results = gri.interp2grid(model_data_2dflat, data_native_lv95, method, try_reg=False) @@ -996,7 +1009,7 @@ def test_interp2grid(model_data_2dflat, data_native_lv95, data_native_rotlatlon) # regular grid interpolation with y/x and lat/lon for dst_grid results_regular = gri.interp2grid(model_data_2dflat, data_native_lv95, method, try_reg=True) - xr.testing.assert_identical(results_regular, results) + assert_same_as_regular(results_regular, results, method) # regular grid interpolation with y/x and y/x for dst_grid results_regular_2 = gri.interp2grid( @@ -1009,7 +1022,7 @@ def test_interp2grid(model_data_2dflat, data_native_lv95, data_native_rotlatlon) if method == "linear" and "cell" in model_data_2dflat.dims: results_regular_2.update(results.drop_vars(["longitude", "latitude"])) - xr.testing.assert_identical(results_regular_2, results.drop_vars(["longitude", "latitude"])) + assert_same_as_regular(results_regular_2, results.drop_vars(["longitude", "latitude"]), method) # same with dst_grid in rotated lat lon native grid. for method in ["nearest", "linear", "nearest_3D"]: @@ -1033,7 +1046,9 @@ def test_interp2grid(model_data_2dflat, data_native_lv95, data_native_rotlatlon) # regular grid interpolation with y/x and lat/lon for dst_grid results_regular = gri.interp2grid(model_data_2dflat, data_native_rotlatlon, method, try_reg=True) - xr.testing.assert_allclose(results.where(results_regular.notnull()), results_regular) + # 'linear': triangulation in LV95 vs bilinear in lat/lon, see assert_same_as_regular + atol = 2.5 if method == "linear" else 1e-8 + xr.testing.assert_allclose(results.where(results_regular.notnull()), results_regular, rtol=1e-5, atol=atol) data_flat = data_native_lv95.stack(poi=("y", "x")) poi_def = data_flat.poi.to_dataframe()[["latitude", "longitude"]].reset_index() @@ -1309,3 +1324,219 @@ def test_interp2poi_integer_coords(method, data_native_lv95, poi_def_lv95): expected = interp2poi(data_lv95, poi_def_lv95, method) result = interp2poi(data_lv95_int, poi_def_lv95, method) np.testing.assert_array_equal(result.test_var_1.values, expected.test_var_1.values) + + +# ---------------------------------------------------------------------------------------------------------------------- +# CRS of the generic interpolation path (KDTree / griddata): never latitude/longitude degrees +# ---------------------------------------------------------------------------------------------------------------------- + + +def _latlon_grid(lat: np.ndarray, lon: np.ndarray, alt: float = 500.0) -> xr.Dataset: + """Regular latitude/longitude grid whose values are the flat index of the grid points.""" + + shape = (lat.size, lon.size) + return xr.Dataset( + {"var": (("latitude", "longitude"), np.arange(lat.size * lon.size, dtype=np.float64).reshape(shape))}, + coords={ + "latitude": lat, + "longitude": lon, + "surface_altitude": (("latitude", "longitude"), np.full(shape, alt)), + }, + attrs={"crs": "EPSG:4326"}, + ) + + +def _latlon_pois(lon: np.ndarray, lat: np.ndarray, alt: float = 500.0) -> gpd.GeoDataFrame: + return gpd.GeoDataFrame( + {"name": [f"p{i}" for i in range(len(lon))], "height_masl": np.full(len(lon), alt)}, + geometry=gpd.points_from_xy(lon, lat), + crs="EPSG:4326", + ) + + +@pytest.mark.parametrize( + "src,dst,src_larger,dst_lonlat,expected", + [ + # one metric CRS: that one + ("EPSG:2056", "EPSG:4326", True, None, "EPSG:2056"), + ("EPSG:4326", "EPSG:2056", True, None, "EPSG:2056"), + ("EPSG:32632", "EPSG:4326", False, None, "EPSG:32632"), + # two metric CRS: the one of the larger grid + ("EPSG:21781", "EPSG:2056", True, None, "EPSG:21781"), + ("EPSG:21781", "EPSG:2056", False, None, "EPSG:2056"), + ("EPSG:32632", "EPSG:2056", True, None, "EPSG:32632"), + # two geographic CRS: LV95 if the destination is close enough to Switzerland, otherwise ECEF + ("EPSG:4326", "EPSG:4326", True, ([5.9, 10.5], [45.8, 47.8]), "EPSG:2056"), # Switzerland + ("EPSG:4326", "EPSG:4326", False, ([-0.8, 17.7], [42.0, 50.5]), "EPSG:2056"), # ICON-CH1 domain + ("EPSG:4326", "EPSG:4326", True, ([5.0, 15.0], [55.0, 60.0]), "EPSG:4978"), # Denmark / Scandinavia + ("EPSG:4326", "EPSG:4326", True, ([-180.0, 179.0], [-90.0, 90.0]), "EPSG:4978"), # global + ], +) +def test_choose_kdtree_crs(src, dst, src_larger, dst_lonlat, expected): + """ + Choice of the CRS of the nearest neighbor searches and triangulations + """ + + from pyproj import CRS + + import grid_process.grid_interp as gri + from grid_process.helpers import reproject + + def dst_lv95(): + assert dst_lonlat is not None, "the destination extent is only needed for two geographic CRS" + lon, lat = np.meshgrid(*dst_lonlat) + return reproject(lon.ravel(), lat.ravel(), "EPSG:4326", "EPSG:2056") + + target = gri._choose_kdtree_crs(CRS.from_user_input(src), CRS.from_user_input(dst), src_larger, dst_lv95) + assert target == CRS.from_user_input(expected) + + +def test_choose_kdtree_crs_threshold(monkeypatch): + """ + The LV95 range is configurable, and non-finite LV95 coordinates give ECEF + """ + + from pyproj import CRS + + import grid_process.grid_interp as gri + from grid_process import CONFIG + from grid_process.helpers import reproject + + latlon = CRS.from_epsg(4326) + swiss = reproject(np.array([5.9, 10.5]), np.array([45.8, 47.8]), latlon, "EPSG:2056") # max offset ~ 125 km + assert gri._choose_kdtree_crs(latlon, latlon, True, lambda: swiss) == gri.LV95 + assert gri._choose_kdtree_crs(latlon, latlon, True, lambda: (np.array([np.inf]), np.array([1.2e6]))) == gri.ECEF + monkeypatch.setattr(CONFIG, "lv95_max_offset", 50_000.0) + assert gri._choose_kdtree_crs(latlon, latlon, True, lambda: swiss) == gri.ECEF + + +def test_interp2poi_ecef_nearest(): + """ + Beyond the LV95 range, nearest neighbors are searched in ECEF coordinates: they are the nearest grid points along + the Earth's surface (not in latitude/longitude degrees), also across the antimeridian + """ + + from pyproj import Geod + + import grid_process.grid_interp as gri + + def check(grid: xr.Dataset, pois: gpd.GeoDataFrame) -> None: + result = gri.interp2poi(grid, pois, "nearest", try_reg=False)["var"].values + lon_2d, lat_2d = np.meshgrid(grid.longitude.values, grid.latitude.values) + n_poi, n_grid = len(pois), lon_2d.size + _, _, dist = Geod(ellps="WGS84").inv( + np.repeat(pois.geometry.x.values, n_grid), + np.repeat(pois.geometry.y.values, n_grid), + np.tile(lon_2d.ravel(), n_poi), + np.tile(lat_2d.ravel(), n_poi), + ) + dist = dist.reshape(n_poi, n_grid) + expected = np.argmin(dist, axis=1) + # ties (nearly equidistant grid points) may be resolved differently between chord and geodesic distances + sorted_dist = np.sort(dist, axis=1) + tie = sorted_dist[:, 1] - sorted_dist[:, 0] < 1e-6 * sorted_dist[:, 1] + np.testing.assert_array_equal(result[~tie], expected[~tie]) + + rng = np.random.default_rng(1) + # Europe, up to 70°N, where 1° of longitude is about a third of 1° of latitude + grid = _latlon_grid(np.arange(30.0, 70.1, 1.0), np.arange(-20.0, 40.1, 1.0)) + check(grid, _latlon_pois(rng.uniform(-19, 39, 300), rng.uniform(31, 69, 300))) + + # global grid: POIs close to the antimeridian (the POIs span all longitudes, so that the grid is not sliced) + grid = _latlon_grid(np.arange(-80.0, 80.1, 2.0), np.arange(-180.0, 179.0, 2.0)) + lon = np.array([179.6, -179.6, 178.9, -178.9, 0.0]) + lat = np.array([0.3, -10.2, 60.4, -61.0, 0.0]) + check(grid, _latlon_pois(lon, lat)) + result = gri.interp2poi(grid, _latlon_pois(lon, lat), "nearest", try_reg=False)["var"].values + # e.g. 179.6°E is closer to 180°W (index 0 along longitude) than to 178°E + assert result[0] == grid["var"].sel(latitude=0.0, longitude=-180.0).item() + + +@pytest.mark.parametrize( + "method,kwargs", + [ + ("nearest", {}), + ("idw", {"k": 4, "weight_power": 2}), + ("upscaled", {"k": 4}), + ("nearest_3D", {}), + ("idw_3D", {"k": 4}), + ("upscaled_3D", {"k": 4}), + ("temp", {"k": 20, "weight_power": 0.5}), + ("temp_fixgrad", {"r_search": 1.5}), + ("nearest_alt", {"r_search": 1.5}), + ("snowlim", {"k": 9}), + ], +) +def test_interp2poi_ecef_vs_lv95(method, kwargs, model_data, poi_def, monkeypatch): + """ + On a Swiss grid, forcing ECEF coordinates gives (almost) the same results as LV95 for all KDTree methods, + including the methods with altitude (altitude as last column of the coordinates) + """ + + import grid_process.grid_interp as gri + from grid_process import CONFIG + + lv95 = gri.interp2poi(model_data, poi_def, method, try_reg=False, **kwargs) + monkeypatch.setattr(CONFIG, "lv95_max_offset", 1.0) + ecef = gri.interp2poi(model_data, poi_def, method, try_reg=False, **kwargs) + # LV95 (projection scale error) and ECEF (chord) distances differ slightly, which changes the inverse distance + # weights by ~1e-6 of the data range; absolute tolerance as "temp" can extrapolate values close to 0 + for var in lv95.data_vars: + atol = 1e-5 * float(np.ptp(model_data[var].values)) + np.testing.assert_allclose(lv95[var].values, ecef[var].values, rtol=1e-4, atol=atol, err_msg=var) + + +@pytest.mark.parametrize("method", ["linear", "linear_3D", "cubic"]) +def test_griddata_ecef_error(method, model_data): + """ + griddata methods (Delaunay triangulation) raise an error beyond the LV95 range (ECEF coordinates) + """ + + import grid_process.grid_interp as gri + + grid = _latlon_grid(np.arange(55.0, 70.1, 0.5), np.arange(5.0, 25.1, 0.5)) + pois = _latlon_pois(np.array([10.0, 20.0]), np.array([60.0, 65.0])) + with pytest.raises(ValueError, match="earth-centered earth-fixed"): + gri.interp2poi(grid, pois, method, try_reg=False) + dst_grid = _latlon_grid(np.arange(56.0, 69.1, 1.0), np.arange(6.0, 24.1, 1.0)).drop_vars("var") + with pytest.raises(ValueError, match="earth-centered earth-fixed"): + gri.interp2grid(grid, dst_grid, method, try_reg=False) + + +def test_interp2grid_unstructured_dst_error(model_data): + """ + The destination grid of interp2grid must be structured (two spatial dimensions) + """ + + import grid_process.grid_interp as gri + + dst_flat = model_data.stack({"cell": ("y", "x")}).drop_vars(("cell", "y", "x")) + with pytest.raises(ValueError, match="structured grid"): + gri.interp2grid(model_data, dst_flat, "nearest") + + +def test_kdtree_native_metric_coords(data_native_lv95, poi_def_lv95): + """ + Native coordinates in meters (here LV03) are used instead of the latitude/longitude coordinates, and a grid + with only latitude/longitude coordinates gives the same nearest neighbors as with its native LV95 coordinates + """ + + import grid_process.grid_interp as gri + from grid_process.ds_utils import ds_to_da + + # LV03 grid with latitude/longitude coordinates: nearest neighbor search in LV03 + data_lv03 = data_native_lv95.assign_coords(x=data_native_lv95.x - 2e6, y=data_native_lv95.y - 1e6) + data_lv03.attrs["grid_mapping"] = '{"epsg_code": "EPSG:21781"}' + da, _ = ds_to_da(data_lv03) + coords_flat, _, pois_coords = gri._flatten_concat(da.transpose("y", "x", ...), ("y", "x"), poi_def_lv95, "idw") + for coords in (coords_flat, pois_coords): + assert (coords[:, 0] > 5e5).all() and (coords[:, 0] < 9e5).all() # LV03 easting + assert (coords[:, 1] > 5e4).all() and (coords[:, 1] < 3.5e5).all() # LV03 northing + + # the same LV95 grid with only latitude/longitude coordinates + data_latlon = data_native_lv95.drop_vars(["x", "y"]).assign_attrs(crs="EPSG:4326") + data_latlon.attrs.pop("grid_mapping") + for method, kwargs in (("nearest", {}), ("idw", {"k": 4, "weight_power": 2})): + native = gri.interp2poi(data_native_lv95, poi_def_lv95, method, try_reg=False, **kwargs) + latlon = gri.interp2poi(data_latlon, poi_def_lv95, method, try_reg=False, **kwargs) + xr.testing.assert_allclose(native.drop_vars(["x", "y"], errors="ignore"), latlon, rtol=1e-4) diff --git a/test/test_grid_interp_utils.py b/test/test_grid_interp_utils.py index af720a2..de32708 100644 --- a/test/test_grid_interp_utils.py +++ b/test/test_grid_interp_utils.py @@ -15,6 +15,7 @@ import grid_process.grid_interp_utils as gru from grid_process.data_model import get_grid_crs +from grid_process.helpers import reproject, sphere_as_wgs84 def test_create_dst_grid(data_native_lv95): @@ -77,7 +78,8 @@ def test_create_dst_grid_from_src(data_native_rotlatlon): original = gru.create_dst_grid_from_src(results, get_grid_crs(data_native_rotlatlon)) assert original.sizes["x"] == data_native_rotlatlon.sizes["x"] assert original.sizes["y"] == data_native_rotlatlon.sizes["y"] - assert original.crs == CRS.from_cf(json.loads(data_native_rotlatlon.grid_mapping)) + # rotated pole on a sphere: considered on WGS84 (see grid_process.helpers.sphere_as_wgs84) + assert original.crs == sphere_as_wgs84(CRS.from_cf(json.loads(data_native_rotlatlon.grid_mapping))) assert original.x.dtype == data_native_rotlatlon.x.dtype np.testing.assert_allclose(original.x.values, data_native_rotlatlon.x.values, atol=5e-2) np.testing.assert_allclose(original.y.values, data_native_rotlatlon.y.values, atol=5e-2) @@ -122,3 +124,86 @@ def test_create_dst_grid_topright_resolution(start, topright, res, expected_n): np.testing.assert_allclose(np.diff(coords), res) assert coords[0] == start assert coords[-1] <= topright + 1e-9 + + +def test_create_dst_grid_missing_args(): + """ + Two out of the three arguments topright, nrowcol, resolution are required + """ + + with pytest.raises(ValueError, match="At least two"): + gru.create_dst_grid("EPSG:2056", (1.2e6, 2.6e6), resolution=1000.0) + + +def _lv95_points(grid: xr.Dataset) -> tuple[np.ndarray, np.ndarray]: + """LV95 coordinates of all latitude/longitude points of a grid""" + + return reproject(grid.longitude.values.ravel(), grid.latitude.values.ravel(), "EPSG:4326", "EPSG:2056") + + +@pytest.mark.parametrize("src_name", ["data_native_lv95", "data_native_rotlatlon", "model_data", "unstructured"]) +def test_create_dst_grid_from_src_bounds(src_name, request): + """ + The destination grid spans exactly the bounding box of the source domain reprojected in the destination CRS, + using native metric coordinates when available (identity in the same CRS) + """ + + if src_name == "unstructured": + src = request.getfixturevalue("data_native_rotlatlon").stack({"cell": ("y", "x")}) + else: + src = request.getfixturevalue(src_name) + results = gru.create_dst_grid_from_src(src, "EPSG:2056") + + if src_name == "data_native_lv95": + np.testing.assert_allclose(results.x.values, src.x.values, rtol=0, atol=1e-6) + np.testing.assert_allclose(results.y.values, src.y.values, rtol=0, atol=1e-6) + return + + # all source points are within the destination grid, which is not larger than needed + x_lv95, y_lv95 = _lv95_points(src) + atol = 1.0 # float32 coordinates + np.testing.assert_allclose([results.x.min(), results.x.max()], [x_lv95.min(), x_lv95.max()], rtol=0, atol=atol) + np.testing.assert_allclose([results.y.min(), results.y.max()], [y_lv95.min(), y_lv95.max()], rtol=0, atol=atol) + if src_name == "unstructured": + assert abs(results.sizes["x"] * results.sizes["y"] - src.sizes["cell"]) <= max(results.sizes.values()) + + +@pytest.mark.filterwarnings("ignore:You will likely lose important projection information") # to_proj4() +@pytest.mark.parametrize("method,kwargs", [("nearest", {}), ("idw", {"k": 4})]) +def test_interp2grid_on_created_dst_grid(method, kwargs, data_native_lv95, data_native_rotlatlon): + """ + interp2grid gives the same results on destination grids created by the utilities, whatever the way their CRS + is given (string, int, CRS object, WKT, rotated pole on a sphere) and with or without latitude/longitude + (same values, NaN included) + """ + + import grid_process.grid_interp as gri + + def interp(src: xr.Dataset, dst: xr.Dataset) -> xr.Dataset: + return gri.interp2grid(src[["test_var_1"]], dst, method, try_reg=False, **kwargs) + + # LV95 destination from a rotated pole source + grid_kwargs = {"bottomleft": (1.17e6, 2.65e6), "nrowcol": (8, 10), "resolution": 4000.0} + expected = interp(data_native_rotlatlon, gru.create_dst_grid("EPSG:2056", **grid_kwargs)) + assert expected["test_var_1"].notnull().any() + lv95 = CRS.from_epsg(2056) + for crs, add_latlon in [("EPSG:2056", True), (2056, False), (lv95, False), (lv95.to_wkt(), False)]: + dst = gru.create_dst_grid(crs, add_latlon=add_latlon, **grid_kwargs) + np.testing.assert_array_equal( + interp(data_native_rotlatlon, dst)["test_var_1"].values, expected["test_var_1"].values + ) + + # rotated pole destination from an LV95 source + rotated = get_grid_crs(data_native_rotlatlon) + dst = gru.create_dst_grid_from_src(data_native_rotlatlon, rotated) + expected = interp(data_native_lv95, dst) + assert expected["test_var_1"].notnull().any() + rotated_sphere = CRS.from_cf(json.loads(data_native_rotlatlon.grid_mapping)) + for other in ( + gru.create_dst_grid_from_src(data_native_rotlatlon, rotated, add_latlon=True), + dst.assign_attrs(crs=rotated_sphere.to_wkt()), + dst.assign_attrs(crs=rotated_sphere.to_proj4()), + ): + np.testing.assert_array_equal( + interp(data_native_lv95, other)["test_var_1"].values, expected["test_var_1"].values + ) diff --git a/test/test_helpers.py b/test/test_helpers.py index 5da5820..981850e 100644 --- a/test/test_helpers.py +++ b/test/test_helpers.py @@ -64,6 +64,55 @@ def test_reproject(): assert np.allclose(grid_coords_wgs84, results_wgs84) +def test_sphere_as_wgs84(): + """ + Geographic CRS on a sphere are considered based on WGS84 (with the datum shift to other datums), other CRS + are unchanged + """ + + from pyproj import CRS + + rotated = CRS.from_cf( + { + "grid_mapping_name": "rotated_latitude_longitude", + "grid_north_pole_latitude": 43.0, + "grid_north_pole_longitude": -170.0, + "earth_radius": 6371229.0, + } + ) + lonlat_sphere = CRS.from_cf({"grid_mapping_name": "latitude_longitude", "earth_radius": 6371229.0}) + for crs in ("EPSG:2056", CRS.from_epsg(21781), CRS.from_epsg(4326)): + assert hlp.sphere_as_wgs84(crs) == CRS.from_user_input(crs) + assert hlp.sphere_as_wgs84(lonlat_sphere) == CRS.from_epsg(4326) + assert hlp.sphere_as_wgs84(rotated).to_json_dict()["base_crs"]["name"] == "WGS 84" + # sphere given otherwise than by a CF grid mapping (e.g. "crs" attribute) + assert hlp.sphere_as_wgs84("+proj=longlat +R=6371229 +no_defs") == CRS.from_epsg(4326) + rotated_proj = "+proj=ob_tran +o_proj=longlat +o_lat_p=43 +o_lon_p=0 +lon_0=10 +R=6371229 +no_defs" + assert hlp.sphere_as_wgs84(rotated_proj).to_json_dict()["base_crs"]["name"] == "WGS 84" + + # rotated pole to LV95 is the same as rotated pole to latitude/longitude, then WGS84 to LV95 + x_rot, y_rot = np.linspace(-2.0, 1.0, 50), np.linspace(-1.5, 1.5, 50) + lon, lat = hlp.reproject(x_rot, y_rot, rotated, "EPSG:4326") + expected = hlp.reproject(lon, lat, "EPSG:4326", "EPSG:2056") + result = hlp.reproject(x_rot, y_rot, rotated, "EPSG:2056") + np.testing.assert_allclose(result, expected, rtol=0, atol=1e-3) + # and back + np.testing.assert_allclose(hlp.reproject(*result, "EPSG:2056", rotated), (x_rot, y_rot), rtol=0, atol=1e-8) + + +def test_outer_ring(): + """ + Outer ring of a structured grid from 1D or 2D coordinates + """ + + x, y = np.arange(4.0), np.arange(3.0) * 10 + x_ring, y_ring = hlp.outer_ring(x, y) + expected = {(xi, yi) for xi in x for yi in y if xi in (0, 3) or yi in (0, 20)} + assert set(zip(x_ring, y_ring)) == expected and len(x_ring) == len(expected) + x_2d, y_2d = np.meshgrid(x, y) + np.testing.assert_array_equal(hlp.outer_ring(x_2d, y_2d), (x_ring, y_ring)) + + def test_update_vars_attrs(model_data_units): """Simple testcase for the update_vars_attrs function from helpers module""" From 7cd4b6220bfa45caeef5141e6270c4b22f3c4a9f Mon Sep 17 00:00:00 2001 From: Mathieu Schaer Date: Mon, 9 Nov 2026 21:32:32 +0100 Subject: [PATCH 02/10] Handle the longitude convention and wrap of lat/lon source grids With latitude/longitude source grids whose longitude convention (0..360 or -180..180) differs from the one of the POIs or destination grid, the source was sliced to the wrong longitudes (wrong values in the generic path, e.g. ERA5 or GFS to a -180..180 global grid) and the regular grid interpolation (try_reg=True) returned NaN. - target longitudes are brought into the convention of the source grid before slicing and before the regular grid interpolation; - global grids are padded with one column across their 360 degree seam (only when targets fall in it) for the regular grid interpolation; - when slicing a lat/lon source, the longitude buffer is widened by 1 / cos(latitude), since neighbors are searched in meters, and all longitudes are kept close to the poles or for targets spanning more than 180 degrees. Operational cases (47, data4web POIs and seamless 2 km grid): same runtime; values identical except tie-breaking between equidistant neighbors (ICON-CH2 LV95 to the 2 km grid offset by half a cell). Co-Authored-By: Claude Opus 5.5 --- CHANGELOG.rst | 7 +++ grid_process/grid_interp.py | 119 +++++++++++++++++++++++++++++++----- test/test_grid_interp.py | 118 ++++++++++++++++++++++++++++++----- 3 files changed, 214 insertions(+), 30 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 34629f5..3bdeef5 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -31,6 +31,13 @@ Unreleased * changed the methods with altitude, which no longer reproject to Swiss coordinates when the source grid or the destination has another CRS projected in meters (e.g. UTM) * added an explicit error when the destination grid of ``interp2grid`` is unstructured, which is not supported +* fixed ``interp2poi`` and ``interp2grid`` with latitude/longitude source grids whose longitude convention + (0..360 or -180..180) differs from the one of the POIs or destination grid: the source was sliced to the + wrong longitudes (wrong values in the generic path, e.g. for a global destination grid) and the regular grid + interpolation (``try_reg=True``) returned NaN. Global latitude/longitude grids are also interpolated across + their 360 degree seam. When slicing a latitude/longitude source grid, the longitude buffer is widened by + 1 / cos(latitude) (as nearest neighbors are now searched in meters) and all longitudes are kept close to the + poles * fixed ``create_dst_grid_from_src``: the destination grid now spans exactly the bounding box of the source domain reprojected in the destination CRS (from native coordinates projected in meters when available), instead of the reprojected corners of its latitude/longitude bounding box, which could be much larger (e.g. +80 km for ICON-CH2 diff --git a/grid_process/grid_interp.py b/grid_process/grid_interp.py index f9af7fa..2e7767f 100644 --- a/grid_process/grid_interp.py +++ b/grid_process/grid_interp.py @@ -1058,7 +1058,11 @@ def _interp_cast_dtype( data = _y_increasing(data, y_dim) # Reproject POIs coordinates in the grid CRS - pois_coords = _convert_poi(poi_info, dm.get_grid_crs(data), data[y_dim].dtype) + grid_crs = dm.get_grid_crs(data) + pois_coords = _convert_poi(poi_info, grid_crs, data[y_dim].dtype) + if grid_crs.is_geographic: # longitudes in the convention of the grid, across the 360 degree seam + pois_coords = (_wrap_lon(pois_coords[0], _lon_center(data[x_dim].values)), pois_coords[1]) + data = _pad_global_lon(data, x_dim, pois_coords[0]) poi_dim_coord = poi_info[POI_COORD_COL].to_numpy(dtype=str) pois_coords = ( xr.DataArray(pois_coords[0], coords=[(POI_DIM, poi_dim_coord)]), @@ -1734,9 +1738,15 @@ def _coarsen_interp() -> xr.Dataset: interp_data = interp_data.coarsen(coarsen_specs, boundary="trim").reduce(np.mean) interp_data = _y_increasing(interp_data, src_spatial_dims[0]) + x_targets = x_coords + if src_crs.is_geographic: # longitudes in the convention of the grid, across the 360 degree seam + x_targets = x_coords.copy( + data=_wrap_lon(x_coords.values, _lon_center(interp_data[src_spatial_dims[1]].values)) + ) + interp_data = _pad_global_lon(interp_data, src_spatial_dims[1], x_targets.values) interp_data = interp_data.interp( - {src_spatial_dims[1]: x_coords, src_spatial_dims[0]: y_coords}, + {src_spatial_dims[1]: x_targets, src_spatial_dims[0]: y_coords}, method=method, assume_sorted=True, ).drop_vars(src_spatial_dims) @@ -1876,14 +1886,13 @@ def slice_boundingbox( spatial_dims = dm.get_spatial_dims(data) grid_y, grid_x, grid_crs = dm.get_coords_name_crs(data, spatial_dims) - pois_coords = _convert_poi(poi_info, grid_crs, data[grid_y].dtype) - yx_bnds = ( - (pois_coords[1].min().item(), pois_coords[1].max().item()), - (pois_coords[0].min().item(), pois_coords[0].max().item()), - ) + pois_x, pois_y = _convert_poi(poi_info, grid_crs, data[grid_y].dtype) + if grid_crs.is_geographic: # longitudes in the convention of the grid (e.g. 0..360 or -180..180) + pois_x = _wrap_lon(pois_x, _lon_center(data[grid_x].values)) + yx_bnds = ((pois_y.min().item(), pois_y.max().item()), (pois_x.min().item(), pois_x.max().item())) buffer = _get_buffer(method, **kwargs) - sub_domain, _ = _get_sub_domain(data, spatial_dims, (grid_y, grid_x), yx_bnds, buffer) + sub_domain, _ = _get_sub_domain(data, spatial_dims, (grid_y, grid_x), yx_bnds, buffer, grid_crs.is_geographic) return data.isel(sub_domain) @@ -1930,11 +1939,13 @@ def extra_buffer(ratio: int) -> int: # Reproject destination coordinates in the grid CRS x_coords, y_coords = reproject(x_coords, y_coords, dst_grid_crs, grid_crs) + if grid_crs.is_geographic: # longitudes in the convention of the grid (e.g. 0..360 or -180..180) + x_coords = _wrap_lon(x_coords, _lon_center(data[coords_crs[1]].values)) yx_bnds = ((y_coords.min(), y_coords.max()), (x_coords.min(), x_coords.max())) buffer = _get_buffer(method, **kwargs) - sub_domain, res = _get_sub_domain(data, spatial_dims, coords_crs[:2], yx_bnds, buffer) + sub_domain, res = _get_sub_domain(data, spatial_dims, coords_crs[:2], yx_bnds, buffer, grid_crs.is_geographic) data_sub = data.isel(sub_domain) # coarsen input dataset if needed (downsampling) @@ -2000,20 +2011,38 @@ def _get_sub_domain( # pylint: disable=too-many-locals spatial_coords: tuple[str, str], yx_bnds: tuple[tuple[float, float], tuple[float, float]], buffer: int, + geographic: bool = False, ) -> tuple[dict[str, slice], None] | tuple[dict[str, NDArray], float]: """ Get a dict to index ``data`` in ``spatial_dims`` according to ``yx_bnds`` plus a ``buffer``. Also return an estimate of the resolution if input grid is unstructured. + + For a ``geographic`` grid (latitude/longitude or rotated pole, in degrees), the x bounds must be in the + longitude convention of the grid (see :func:`_wrap_lon`). The x buffer is widened by 1 / cos(latitude), as + a degree of longitude shrinks towards the poles, and all longitudes are kept when the target spans more + than half of them or reaches the poles, whose neighbors can be at any longitude. """ - def get_cond_xy(buffer_crs: float) -> NDArray[np.bool_]: + def lon_buffer_factor(res_y: float) -> float | None: + """ + Factor to widen the x buffer of a geographic grid, or None to keep all longitudes. + """ + + lat_reach = max(abs(y_bnds[0]), abs(y_bnds[1])) + (buffer + 1) * res_y + cos_lat = np.cos(np.radians(min(lat_reach, 90.0))) + if cos_lat < 0.01 or x_bnds[1] - x_bnds[0] > 180.0: + return None + return 1.0 / cos_lat + + def get_cond_xy(buffer_crs: float, x_factor: float = 1.0) -> NDArray[np.bool_]: """ Get a boolean array to indicate which points are within x_bnds and y_bnds - +- ``buffer_crs`` to index data on the unstructured grid dimension. + +- ``buffer_crs`` (times ``x_factor`` in x) to index data on the unstructured grid dimension. """ - cond_x = (data[grid_x].values > x_bnds[0] - buffer_crs) & (data[grid_x].values < x_bnds[1] + buffer_crs) + x_buffer = buffer_crs * x_factor + cond_x = (data[grid_x].values > x_bnds[0] - x_buffer) & (data[grid_x].values < x_bnds[1] + x_buffer) cond_y = (data[grid_y].values > y_bnds[0] - buffer_crs) & (data[grid_y].values < y_bnds[1] + buffer_crs) return cond_x & cond_y @@ -2046,21 +2075,30 @@ def get_ind_startend(coordname: str, dim: str, bnds: tuple[float, float]) -> tup grid_y, grid_x = spatial_coords y_bnds, x_bnds = yx_bnds + x_factor = 1.0 if len(spatial_dims) == 1: res_start = max(abs(np.diff(data[grid_x][:2])[0]), abs(np.diff(data[grid_y][:2])[0])) res_end = max(abs(np.diff(data[grid_x][-2:])[0]), abs(np.diff(data[grid_y][-2:])[0])) res_approx = max(res_start, res_end) - cond_xy = get_cond_xy(2 * res_approx) + if geographic: + factor = lon_buffer_factor(res_approx) + x_bnds, x_factor = ((-np.inf, np.inf), 1.0) if factor is None else (x_bnds, factor) + cond_xy = get_cond_xy(2 * res_approx, x_factor) res = _get_flat_grid_res(data[grid_x][cond_xy], data[grid_y][cond_xy]) - cond_xy = get_cond_xy(buffer * res * 1.5) + cond_xy = get_cond_xy(buffer * res * 1.5, x_factor) return {spatial_dims[0]: cond_xy}, res y_dim, x_dim = spatial_dims + if geographic: + res_y = float(np.nanmedian(np.abs(np.diff(data[grid_y].values, axis=data[grid_y].get_axis_num(y_dim))))) + factor = lon_buffer_factor(res_y) + x_bnds, x_factor = ((-np.inf, np.inf), 1.0) if factor is None else (x_bnds, factor) idx_start, idx_end = get_ind_startend(grid_x, x_dim, x_bnds) idy_start, idy_end = get_ind_startend(grid_y, y_dim, y_bnds) - idx_start = max(idx_start - buffer, 0) - idx_end += buffer + x_buffer = max(buffer, ceil(buffer * (x_factor - 0.01))) # widened beyond 1 % of distortion (latitudes > ~8°) + idx_start = max(idx_start - x_buffer, 0) + idx_end += x_buffer idy_start = max(idy_start - buffer, 0) idy_end += buffer return {x_dim: slice(idx_start, idx_end), y_dim: slice(idy_start, idy_end)}, None @@ -2078,6 +2116,55 @@ def dcoords(coords: xr.DataArray) -> float: return max(dcoords(coords_x), dcoords(coords_y)) +def _lon_center(lon: np.ndarray) -> float: + """ + Center of the longitude range [degrees] of a geographic grid, see :func:`_wrap_lon`. + """ + + return (float(np.nanmin(lon)) + float(np.nanmax(lon))) / 2 + + +def _wrap_lon(lon: np.ndarray, center: float) -> np.ndarray: + """ + Longitudes [degrees] brought within [center - 180, center + 180), i.e. in the convention (e.g. 0..360 or + -180..180) of a geographic grid whose longitude range is centered on ``center`` (see :func:`_lon_center`). + Longitudes already in that range are unchanged. + """ + + lon = np.asarray(lon) + wrapped = center - 180.0 + np.mod(lon - (center - 180.0), 360.0) + return wrapped.astype(lon.dtype, copy=False) if np.issubdtype(lon.dtype, np.floating) else wrapped + + +def _pad_global_lon(data: xr.Dataset, x_dim: str, x_targets: np.ndarray) -> xr.Dataset: + """ + For a regular latitude/longitude grid covering all longitudes (1D increasing longitudes), add the last + column before the first one (at -360) and/or the first column after the last one (at +360) when target + longitudes (in the grid convention, see :func:`_wrap_lon`) fall between them, so that the regular grid + interpolation does not return NaN across the 360 degree seam. The data are only copied when needed. + + Grids without rows at the poles are not padded across the poles: targets beyond their first or last + latitude get NaN. + """ + + lon = data[x_dim].values + if lon.size < 2: + return data + spacing = float(np.median(np.diff(lon))) + if abs(lon[-1] - lon[0] + spacing - 360.0) > 0.01 * spacing: # not global in longitude + return data + + parts = [] + if np.nanmin(x_targets) < lon[0]: + parts.append(data.isel({x_dim: [-1]}).assign_coords({x_dim: [lon[-1] - 360.0]})) + parts.append(data) + if np.nanmax(x_targets) > lon[-1]: + parts.append(data.isel({x_dim: [0]}).assign_coords({x_dim: [lon[0] + 360.0]})) + if len(parts) == 1: + return data + return xr.concat(parts, dim=x_dim, data_vars="minimal", coords="minimal", compat="override", join="exact") + + def _get_coarsen_ratio( # pylint: disable=too-many-locals data: xr.Dataset, spatial_dims: tuple[str, ...], diff --git a/test/test_grid_interp.py b/test/test_grid_interp.py index 27ff77d..fe1aaca 100644 --- a/test/test_grid_interp.py +++ b/test/test_grid_interp.py @@ -904,20 +904,15 @@ def test_slice_boundingbox(model_data_2dflat, poi_def, data_native_lv95_2dflat, import grid_process.grid_interp as gri - # check that with a single POI the sliced grid has correct sizes - sliced_data = gri.slice_boundingbox(model_data_2dflat, poi_def.loc[[0]], method="nearest") - if "cell" in sliced_data.dims: - assert 4 <= sliced_data.sizes["cell"] <= 16 - else: - assert sliced_data.sizes["x"] == 2 - assert sliced_data.sizes["y"] == 2 - - sliced_data = gri.slice_boundingbox(data_native_lv95_2dflat, poi_def_lv95.loc[[0]], method="nearest") - if "cell" in sliced_data.dims: - assert 4 <= sliced_data.sizes["cell"] <= 16 - else: - assert sliced_data.sizes["x"] == 2 - assert sliced_data.sizes["y"] == 2 + # check that with a single POI the sliced grid has correct sizes. Both grids are sliced in latitude/longitude + # (~46°N), where the x buffer is widened by 1 / cos(latitude): 1 -> 2 columns on each side + for data, pois in ((model_data_2dflat, poi_def), (data_native_lv95_2dflat, poi_def_lv95)): + sliced_data = gri.slice_boundingbox(data, pois.loc[[0]], method="nearest") + if "cell" in sliced_data.dims: + assert 4 <= sliced_data.sizes["cell"] <= 24 + else: + assert sliced_data.sizes["x"] == 4 + assert sliced_data.sizes["y"] == 2 @pytest.mark.parametrize("method", ["nearest", "linear"]) @@ -1540,3 +1535,98 @@ def test_kdtree_native_metric_coords(data_native_lv95, poi_def_lv95): native = gri.interp2poi(data_native_lv95, poi_def_lv95, method, try_reg=False, **kwargs) latlon = gri.interp2poi(data_latlon, poi_def_lv95, method, try_reg=False, **kwargs) xr.testing.assert_allclose(native.drop_vars(["x", "y"], errors="ignore"), latlon, rtol=1e-4) + + +def _sphere_field(lon: np.ndarray, lat: np.ndarray) -> np.ndarray: + """Smooth field on the sphere, continuous across the 360 degree seam and at the poles""" + + lon, lat = np.radians(lon), np.radians(lat) + return 10 * np.cos(lat) * np.cos(lon) + 5 * np.sin(lat) + + +def _global_grid(res: float, lon_start: float, lon_span: float = 360.0, lat_bnds=(90.0, -90.0), var=True): + """Regular latitude/longitude grid (decreasing latitudes, as ERA5 or GFS) with the field of _sphere_field""" + + lat = np.arange(lat_bnds[0], lat_bnds[1] - res / 2, -res) + lon = np.arange(lon_start, lon_start + lon_span - res / 2, res) + grid = xr.Dataset(coords={"latitude": lat, "longitude": lon}, attrs={"crs": "EPSG:4326"}) + if var: + lon_2d, lat_2d = np.meshgrid(lon, lat) + grid["var"] = (("latitude", "longitude"), _sphere_field(lon_2d, lat_2d).astype(np.float32)) + return grid + + +@pytest.mark.parametrize( + "method,kwargs", [("nearest", {}), ("linear", {}), ("nearest", {"try_reg": False}), ("idw", {})] +) +@pytest.mark.parametrize( + "src_lon,dst_lon,dst_span,dst_lat", + [ + (0.0, -180.0, 360.0, (90.0, -90.0)), # 0..360 to -180..180 + (-180.0, 0.0, 360.0, (90.0, -90.0)), # -180..180 to 0..360 + (-180.0, 170.0, 20.0, (30.0, -30.0)), # destination across the antimeridian, 170..190 + (0.0, -190.0, 20.0, (30.0, -30.0)), # destination across the source seam, -190..-170 + ], +) +def test_interp2grid_longitude_convention(method, kwargs, src_lon, dst_lon, dst_span, dst_lat): + """ + Latitude/longitude source and destination grids with different longitude conventions (0..360 or -180..180): + all destination cells get the value of their location, including across the 360 degree seam + """ + + import grid_process.grid_interp as gri + + src = _global_grid(1.0, src_lon) + dst = _global_grid(0.5, dst_lon, dst_span, dst_lat, var=False) + out = gri.interp2grid(src, dst, method, **kwargs)["var"].transpose("latitude", "longitude").values + expected = _sphere_field(*np.meshgrid(dst.longitude.values, dst.latitude.values)) + assert not np.isnan(out).any() + # max gradient of the field ~0.2 per degree: nearest is within half a grid spacing + np.testing.assert_allclose(out, expected, atol=0.01 if method == "linear" else 0.15) + + +@pytest.mark.parametrize("src_lon", [0.0, -180.0]) +def test_interp2poi_longitude_convention(src_lon): + """ + POIs at any longitude (incl. the 360 degree seam and near the poles) on a global latitude/longitude grid in + either longitude convention, through the regular path (try_reg=True) and the generic (ECEF) path + """ + + import grid_process.grid_interp as gri + + src = _global_grid(1.0, src_lon) + rng = np.random.default_rng(0) + src = src.assign_coords(surface_altitude=(("latitude", "longitude"), rng.uniform(0, 3000, src["var"].shape))) + lon = np.array([-179.9, 179.9, 359.6, -70.0, 0.0, 0.2, 139.7, -0.1, 290.0, -120.0]) + lat = np.array([-18.0, 0.0, 10.0, -16.5, -89.99, 89.9, 35.7, 51.5, 60.0, 40.0]) + pois = gpd.GeoDataFrame( + {"name": [f"p{i}" for i in range(lon.size)], "height_masl": 500.0}, + geometry=gpd.points_from_xy(lon, lat), + crs="EPSG:4326", + ) + expected = _sphere_field(lon, lat) + for method, kwargs, atol in [ + ("nearest", {}, 0.15), + ("linear", {}, 0.01), + ("nearest", {"try_reg": False}, 0.15), + ("temp", {"k": 9}, 0.5), + ]: + out = gri.interp2poi(src, pois, method, **kwargs)["var"].values + np.testing.assert_allclose(out, expected, atol=atol, err_msg=f"{method} {kwargs}") + + +def test_interp2grid_regional_latlon_source_unchanged(): + """ + A regional latitude/longitude source is not wrapped around the globe: the regular path gives NaN outside of + its domain and the expected values inside + """ + + import grid_process.grid_interp as gri + + src = _global_grid(1.0, -10.0, 41.0, (70.0, 30.0)) + dst = _global_grid(0.5, -180.0, 360.0, (80.0, 20.0), var=False) + out = gri.interp2grid(src, dst, "linear")["var"].transpose("latitude", "longitude").values + lon_2d, lat_2d = np.meshgrid(dst.longitude.values, dst.latitude.values) + inside = (lon_2d >= -10) & (lon_2d <= 30) & (lat_2d <= 70) & (lat_2d >= 30) + assert np.isnan(out[~inside]).all() + np.testing.assert_allclose(out[inside], _sphere_field(lon_2d, lat_2d)[inside], atol=0.01) From 0d767b318b835db3fcc6031a873f3d862e9aec1f Mon Sep 17 00:00:00 2001 From: Mathieu Schaer Date: Mon, 9 Nov 2026 23:27:53 +0100 Subject: [PATCH 03/10] Fall back to a fixed lapse rate in temp for flat neighborhoods The temp method raised a rank-deficient error when all the k nearest grid points of a POI have the same altitude, which happens close to the poles of global latitude/longitude grids (their pole rows repeat the same point, e.g. GFS, ERA5) or on flat orography. Such POIs now use the existing fixed lapse rate fallback (nearest grid point corrected with 6 K/km), like POIs far above or below their neighborhood. Co-Authored-By: Claude Opus 5.5 --- CHANGELOG.rst | 3 +++ grid_process/grid_interp.py | 13 ++++++++----- test/test_grid_interp.py | 39 ++++++++++++++++++++++++++++++++----- 3 files changed, 45 insertions(+), 10 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 3bdeef5..5ec488e 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -38,6 +38,9 @@ Unreleased their 360 degree seam. When slicing a latitude/longitude source grid, the longitude buffer is widened by 1 / cos(latitude) (as nearest neighbors are now searched in meters) and all longitudes are kept close to the poles +* changed the ``temp`` method of ``interp2poi``: POIs whose nearest grid points all have the same altitude (e.g. + close to the poles of a latitude/longitude grid, whose pole rows repeat the same point, or on flat orography) + use the fixed lapse rate fallback (as POIs far above or below their neighborhood) instead of raising an error * fixed ``create_dst_grid_from_src``: the destination grid now spans exactly the bounding box of the source domain reprojected in the destination CRS (from native coordinates projected in meters when available), instead of the reprojected corners of its latitude/longitude bounding box, which could be much larger (e.g. +80 km for ICON-CH2 diff --git a/grid_process/grid_interp.py b/grid_process/grid_interp.py index 2e7767f..e6b5ed5 100644 --- a/grid_process/grid_interp.py +++ b/grid_process/grid_interp.py @@ -133,10 +133,6 @@ def interp_temp( # pylint: disable=too-many-locals array containing the interpolated values and the same trailing dimensions as the input ``data_flat`` array - Raises - ------ - ValueError : Raised if the orography is flat, as no sounding can be estimated. - See also -------- :func:`interp2poi` @@ -145,7 +141,9 @@ def interp_temp( # pylint: disable=too-many-locals ---- For POIs with altitude higher than 300m above the highest gridpoint or lower than 500m below the lowest gridpoint in the local neighborhood used for the fit, - the fixgrad method is used with default gradient of 0.006 [K / m]. + or whose neighbors all have the same altitude (no sounding can be fitted, e.g. at + the poles of a latitude/longitude grid or on flat orography), the fixgrad method + is used with default gradient of 0.006 [K / m]. """ # make sure k is smaller or equal to the number of grid points @@ -183,11 +181,15 @@ def interp_temp( # pylint: disable=too-many-locals # to classify fixgrad vs sounding POIs. alt_minmax = np.quantile(all_alt, [0, 1], axis=1) # shape (2, n_pois) fixgrad_mask = (poi_alts < alt_minmax[0] - 600) | (poi_alts > alt_minmax[1] + 200) + fixgrad_mask |= alt_minmax[1] - alt_minmax[0] <= FLAT_NEIGHBORHOOD sounding_groups = [(np.nonzero(~fixgrad_mask)[0], 1, True)] else: # classify each POI into three groups: fixgrad, deg1 sounding, deg3 sounding alt_q = np.quantile(all_alt, [0, 0.15, 0.85, 1], axis=1) # shape (4, n_pois) fixgrad_mask = (poi_alts < alt_q[0] - 600) | (poi_alts > alt_q[-1] + 200) + # no sounding can be fitted when all neighbors have the same altitude (e.g. copies of the pole point of a + # latitude/longitude grid, flat orography) + fixgrad_mask |= alt_q[-1] - alt_q[0] <= FLAT_NEIGHBORHOOD sounding_mask = ~fixgrad_mask deg3_mask = sounding_mask & (alt_q[1] < poi_alts) & (poi_alts < alt_q[2]) deg1_mask = sounding_mask & ~deg3_mask @@ -2233,6 +2235,7 @@ def _combine_attrs(src_attrs: dict, dst_attrs: dict) -> dict: # methods relying on scipy.interpolate.griddata (Delaunay triangulation), not supported in ECEF coordinates GRIDDATA_METHODS = ("linear", "linear_3D", "cubic") +FLAT_NEIGHBORHOOD = 1e-6 # [m] max altitude range of the neighbors of a POI for which interp_temp uses fixgrad HEIGHT_METHODS = ( "temp", diff --git a/test/test_grid_interp.py b/test/test_grid_interp.py index fe1aaca..ed07809 100644 --- a/test/test_grid_interp.py +++ b/test/test_grid_interp.py @@ -151,18 +151,22 @@ def test_interp_temp(): # compare assert np.allclose(expected, results) - # ***flat orography: all neighbor altitudes identical → deg=1 rank-deficient*** - # must raise ValueError without infinite recursion + # ***flat orography: all neighbor altitudes identical (e.g. copies of the pole point of a lat/lon grid)*** + # no sounding can be fitted: fixgrad fallback (nearest grid point corrected with 6 K/km), for both branches x_flat = np.tile(np.arange(0, 10), (10, 1)) y_flat = x_flat.T z_flat = np.zeros(x_flat.shape) # completely flat coords_flat_flat = np.c_[x_flat.flatten(), y_flat.flatten(), z_flat.flatten()] data_flat_flat = (x_flat + y_flat).flatten().astype(np.float32)[:, None] - poi_coords_flat = np.array([[2.5, 2.5, 0.0], [7.0, 7.0, 0.0]]) + poi_coords_flat = np.array([[2.0, 3.0, 0.0], [7.0, 7.0, 100.0]]) # on grid points: unambiguous nearest - with pytest.raises(ValueError, match="rank-deficient"): - gri.interp_temp(coords_flat_flat, data_flat_flat, poi_coords_flat, z_fact=1, k=9) + expected_flat = np.array([[5.0], [14.0 - 0.006 * 100]]) + for grad_only in (False, True): + results_flat = gri.interp_temp( + coords_flat_flat, data_flat_flat, poi_coords_flat, z_fact=1, k=9, grad_only=grad_only + ) + np.testing.assert_allclose(results_flat, expected_flat, rtol=1e-6) # ***chunk_size invariance: results must not depend on chunk_size*** @@ -1630,3 +1634,28 @@ def test_interp2grid_regional_latlon_source_unchanged(): inside = (lon_2d >= -10) & (lon_2d <= 30) & (lat_2d <= 70) & (lat_2d >= 30) assert np.isnan(out[~inside]).all() np.testing.assert_allclose(out[inside], _sphere_field(lon_2d, lat_2d)[inside], atol=0.01) + + +def test_interp2poi_temp_at_poles(): + """ + On a latitude/longitude grid with rows at the poles, all points of a pole row are copies of the same point + (same altitude and value): the k nearest neighbors of a POI close to a pole may all be such copies, for which + no sounding can be fitted. temp falls back to the nearest grid point corrected with a fixed lapse rate + """ + + import grid_process.grid_interp as gri + + src = _global_grid(1.0, 0.0) + rng = np.random.default_rng(0) + alt = rng.uniform(0, 3000, src["var"].shape) + alt[[0, -1], :] = alt[[0, -1], :1] # one altitude per pole + src = src.assign_coords(surface_altitude=(("latitude", "longitude"), alt)) + lon, lat = np.array([0.0, 120.0, -45.0]), np.array([-90.0, -89.99, 89.95]) + pois = gpd.GeoDataFrame( + {"name": ["south_pole", "near_south_pole", "near_north_pole"], "height_masl": alt[[-1, -1, 0], 0]}, + geometry=gpd.points_from_xy(lon, lat), + crs="EPSG:4326", + ) + out = gri.interp2poi(src, pois, "temp", z_fact=15, k=50, weight_power=0.75)["var"].values + # same altitude as the pole point: its value + np.testing.assert_allclose(out, _sphere_field(lon, lat), atol=0.01) From 8d2fda6a21aee625d2fde439160b4a3c72316f8a Mon Sep 17 00:00:00 2001 From: Mathieu Schaer Date: Mon, 9 Nov 2026 23:38:15 +0100 Subject: [PATCH 04/10] =?UTF-8?q?Add=20global=20example=20notebooks=20(GFS?= =?UTF-8?q?=20to=20stations,=20ERA5=20to=20the=20IFS=200.1=C2=B0=20grid)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 05_interp2poi_gfs_global_temperature: GFS 0.25° 2 m temperature (single run and lead time, only the needed fields downloaded through the index byte ranges) interpolated to 33 stations worldwide in varied terrain with nearest, linear and temp, compared with NOAA GHCN-hourly observations; - 06_interp2grid_era5_global_temperature: ERA5 0.25° 2 m temperature at a single time regridded to the global IFS 0.1° open data grid with nearest (regular and generic path), linear and temp_fixgrad. Both illustrate the ECEF neighbor search, the 0..360 longitudes of the source grids and the poles. Co-Authored-By: Claude Opus 5.5 --- CHANGELOG.rst | 3 +- doc/examples.rst | 14 +- ...05_interp2poi_gfs_global_temperature.ipynb | 2690 +++++++++++++++++ ..._interp2grid_era5_global_temperature.ipynb | 2374 +++++++++++++++ 4 files changed, 5074 insertions(+), 7 deletions(-) create mode 100644 doc/examples/05_interp2poi_gfs_global_temperature.ipynb create mode 100644 doc/examples/06_interp2grid_era5_global_temperature.ipynb diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 5ec488e..440fcf9 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -7,7 +7,8 @@ Unreleased * added a ``py.typed`` marker so that type checkers use the inline type hints of ``grid-process`` (PEP 561) * added example notebooks to the documentation illustrating ``interp2poi`` and ``interp2grid`` with open data, with - their dependencies in the optional ``example`` dependency group + their dependencies in the optional ``example`` dependency group. Two of them are global: GFS 2 m temperature to + stations worldwide (compared with GHCN-hourly observations) and ERA5 2 m temperature to the IFS 0.1° grid * fixed ``interp2grid`` and ``interp2poi`` truncating reprojected coordinates when the grid coordinates are integers (e.g. an LV95 destination grid built with ``np.arange``), which gave wrong values or NaN * fixed ``create_dst_grid`` with ``topright`` and ``resolution``: the resolution is now exact and ``topright`` diff --git a/doc/examples.rst b/doc/examples.rst index 8b108be..7a83dce 100644 --- a/doc/examples.rst +++ b/doc/examples.rst @@ -3,8 +3,8 @@ Examples ======== The following notebooks illustrate the spatial interpolation functions of :mod:`grid_process.grid_interp` with open -data from MeteoSwiss, ECMWF, Copernicus and swisstopo. Each one compares several interpolation methods visually and -with a runtime benchmark. +data from MeteoSwiss, ECMWF, Copernicus, swisstopo and NOAA. Each one compares several interpolation methods visually +and with a runtime benchmark. The first four examples cover Switzerland, the last two the whole globe. The notebooks are rendered with the outputs of their last execution. Since open forecast data are only kept for a few days, re-running them retrieves more recent forecasts. To run them, install the extra dependencies with:: @@ -17,12 +17,12 @@ keep them between sessions, enable its cache once, e.g. in ``~/.config/earthkit/ cache-policy: user user-cache-directory: /path/to/a/large/disk/earthkit-cache -The ERA5-Land example additionally needs a free account on the +The ERA5-Land and ERA5 examples additionally need a free account on the `Copernicus Climate Data Store `_, with the API token in ``~/.cdsapirc`` and the -licence of the dataset accepted once on its download page. +licences of the datasets accepted once on their download pages. -All data are open data under the CC BY 4.0 licence (MeteoSwiss, ECMWF, Copernicus Climate Change Service) or the -open data terms of swisstopo. The data sources are credited in each notebook. +All data are open data under the CC BY 4.0 licence (MeteoSwiss, ECMWF, Copernicus Climate Change Service), the +open data terms of swisstopo, or in the public domain (NOAA). The data sources are credited in each notebook. .. toctree:: :maxdepth: 1 @@ -31,3 +31,5 @@ open data terms of swisstopo. The data sources are credited in each notebook. examples/02_interp2poi_ifs_temperature examples/03_interp2grid_era5land_precipitation examples/04_interp2grid_valais_temperature + examples/05_interp2poi_gfs_global_temperature + examples/06_interp2grid_era5_global_temperature diff --git a/doc/examples/05_interp2poi_gfs_global_temperature.ipynb b/doc/examples/05_interp2poi_gfs_global_temperature.ipynb new file mode 100644 index 0000000..0aa39e7 --- /dev/null +++ b/doc/examples/05_interp2poi_gfs_global_temperature.ipynb @@ -0,0 +1,2690 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8c17eb59", + "metadata": {}, + "source": [ + "# Interpolation to stations worldwide: GFS 2 m temperature\n", + "\n", + "This example illustrates {func}`~grid_process.grid_interp.interp2poi` on a global grid with three methods:\n", + "\n", + "- `nearest`: value of the nearest grid point;\n", + "- `linear`: bilinear interpolation on the regular latitude/longitude grid;\n", + "- `temp`: altitude-aware interpolation designed for temperature. It fits a local sounding on the `k`\n", + " nearest grid points (with a distance penalizing altitude differences by `z_fact`) and evaluates it at\n", + " the station altitude.\n", + "\n", + "The 2 m temperature forecast of the NOAA Global Forecast System (GFS, regular 0.25° grid, about 28 km) is\n", + "interpolated to 33 stations around the world, and compared with their observations. They cover a wide\n", + "range of terrain:\n", + "\n", + "- high mountains: Jungfraujoch, Säntis, Sonnblick, Mauna Loa and El Alto (La Paz);\n", + "- high cities and plateaus: Mexico City, Bogotá, Quito, Nairobi, Johannesburg, Denver,\n", + " Salt Lake City, Ulaanbaatar and Tamanrasset;\n", + "- valleys: Innsbruck, Sion, Kathmandu and Santiago de Chile;\n", + "- coasts and islands, including three near the antimeridian: Nadi (Fiji), Fua'amotu (Tonga), Pago Pago,\n", + " Honolulu, Dakar, Tokyo, Ushuaia, Christchurch and London;\n", + "- tropical lowland and desert: Manaus and Alice Springs;\n", + "- polar: Amundsen-Scott (South Pole), Alert, Utqiaġvik and Longyearbyen.\n", + "\n", + "Since the grid and the stations are both in latitude/longitude and cover the globe, the nearest neighbor\n", + "searches of `nearest` and `temp` run in earth-centered earth-fixed coordinates (ECEF): distances are\n", + "measured in meters through the Earth, so they are correct everywhere, across the antimeridian and at the\n", + "poles.\n", + "\n", + "*Data: NOAA Global Forecast System (GFS) and NOAA NCEI Global Historical Climatology Network hourly\n", + "(GHCNh), public domain. Coastlines: Natural Earth.*" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "28c62853", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-09T22:29:41.230114Z", + "iopub.status.busy": "2026-11-09T22:29:41.229875Z", + "iopub.status.idle": "2026-11-09T22:29:43.981983Z", + "shell.execute_reply": "2026-11-09T22:29:43.980470Z" + } + }, + "outputs": [], + "source": [ + "import contextlib\n", + "import datetime as dt\n", + "import io\n", + "import os\n", + "import time\n", + "import timeit\n", + "from collections.abc import Callable\n", + "from concurrent.futures import ThreadPoolExecutor\n", + "from functools import partial\n", + "from typing import Any\n", + "\n", + "# import pyproj before earthkit.data to avoid a crash at exit (earthkit-data#1202)\n", + "import pyproj # noqa: F401 # pylint: disable=unused-import\n", + "\n", + "# grid_process data model (read at import): the GHCNh station list columns give the POI labels and altitudes\n", + "os.environ[\"GRID_PROCESS__data_model__poi__coord_from\"] = \"GHCN_ID\"\n", + "os.environ[\"GRID_PROCESS__data_model__poi__alt_from\"] = '[\"ELEVATION\"]'\n", + "\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import earthkit.data as ekd\n", + "import geopandas as gpd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import requests\n", + "import xarray as xr\n", + "from earthkit.data.utils import download_and_cache\n", + "\n", + "import grid_process.grid_interp as gri\n", + "import grid_process.units_conv as uconv\n", + "\n", + "# GHCNh station identifier: (short name, terrain)\n", + "STATIONS: dict[str, tuple[str, str]] = {\n", + " \"SZM00006730\": (\"Jungfraujoch\", \"high mountain\"),\n", + " \"SZM00006680\": (\"Säntis\", \"high mountain\"),\n", + " \"AUM00011343\": (\"Sonnblick\", \"high mountain\"),\n", + " \"USW00021514\": (\"Mauna Loa\", \"high mountain\"),\n", + " \"BLI0000SLLP\": (\"El Alto\", \"high mountain\"),\n", + " \"MXI0000MMMX\": (\"Mexico City\", \"high city\"),\n", + " \"COI0000SKBO\": (\"Bogotá\", \"high city\"),\n", + " \"ECI0000SEQM\": (\"Quito\", \"high city\"),\n", + " \"KEI0000HKJK\": (\"Nairobi\", \"high city\"),\n", + " \"SFI0000FAOR\": (\"Johannesburg\", \"high city\"),\n", + " \"USW00003017\": (\"Denver\", \"high city\"),\n", + " \"USW00024127\": (\"Salt Lake City\", \"high city\"),\n", + " \"MGI0000ZMUB\": (\"Ulaanbaatar\", \"high city\"),\n", + " \"AGI0000DAAT\": (\"Tamanrasset\", \"high city\"),\n", + " \"AUI0000LOWI\": (\"Innsbruck\", \"valley\"),\n", + " \"SZI0000LSGS\": (\"Sion\", \"valley\"),\n", + " \"NPI0000VNKT\": (\"Kathmandu\", \"valley\"),\n", + " \"CIN00330021\": (\"Santiago\", \"valley\"),\n", + " \"FJI0000NFFN\": (\"Nadi\", \"coast/island\"),\n", + " \"TNI0000NFTF\": (\"Fua'amotu\", \"coast/island\"),\n", + " \"AQW00061705\": (\"Pago Pago\", \"coast/island\"),\n", + " \"USW00022521\": (\"Honolulu\", \"coast/island\"),\n", + " \"SGI0000GOOY\": (\"Dakar\", \"coast/island\"),\n", + " \"JAI0000RJTT\": (\"Tokyo\", \"coast/island\"),\n", + " \"ARI0000SAWH\": (\"Ushuaia\", \"coast/island\"),\n", + " \"NZI0000NZCH\": (\"Christchurch\", \"coast/island\"),\n", + " \"UKI0000EGLL\": (\"London\", \"coast/island\"),\n", + " \"BRM00081730\": (\"Manaus\", \"lowland/desert\"),\n", + " \"ASI0000YBAS\": (\"Alice Springs\", \"lowland/desert\"),\n", + " \"AYU089009-1\": (\"South Pole\", \"polar\"),\n", + " \"CAMU0019939\": (\"Alert\", \"polar\"),\n", + " \"USW00027502\": (\"Utqiaġvik\", \"polar\"),\n", + " \"SVI0000ENSB\": (\"Longyearbyen\", \"polar\"),\n", + "}\n", + "METHODS: dict[str, dict[str, Any]] = {\n", + " \"nearest\": {\"method\": \"nearest\", \"try_reg\": False},\n", + " \"linear\": {\"method\": \"linear\"},\n", + " \"temp\": {\"method\": \"temp\", \"z_fact\": 15, \"k\": 50, \"weight_power\": 0.75},\n", + "}\n", + "RUN_DAYS_AGO = 3 # GHCNh observations are published with a delay of about two days\n", + "LEAD_H = 12 # lead time [h]" + ] + }, + { + "cell_type": "markdown", + "id": "14639c9a", + "metadata": {}, + "source": [ + "## Source data\n", + "\n", + "GFS forecasts are archived on the NOAA Open Data Dissemination program (AWS). We take the 00 UTC run of\n", + "three days ago at the lead time of 12 hours, so that the observations of the valid time are already\n", + "published. A GFS file holds all variables (about 500 MB): with its index file, only the 2 m temperature\n", + "and the model orography (`HGT` at the surface, needed by `temp`) are downloaded, as byte ranges. The grid\n", + "keeps the GFS longitudes from 0° to 360°, while the stations use -180° to 180°: `grid_process` handles\n", + "both conventions." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7e8d765a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-09T22:29:43.985253Z", + "iopub.status.busy": "2026-11-09T22:29:43.984678Z", + "iopub.status.idle": "2026-11-09T22:29:45.842085Z", + "shell.execute_reply": "2026-11-09T22:29:45.840652Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GFS run 2026-11-06 00 UTC, lead time 12 h, valid 2026-11-06 12:00 UTC\n" + ] + }, + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 8MB\n",
+       "Dimensions:           (latitude: 721, longitude: 1440)\n",
+       "Coordinates:\n",
+       "  * latitude          (latitude) float64 6kB 90.0 89.75 89.5 ... -89.75 -90.0\n",
+       "  * longitude         (longitude) float64 12kB 0.0 0.25 0.5 ... 359.5 359.8\n",
+       "    surface_altitude  (latitude, longitude) float32 4MB 0.05937 ... 2.775e+03\n",
+       "Data variables:\n",
+       "    2t                (latitude, longitude) float32 4MB -17.29 -17.29 ... -39.52\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.8\n",
+       "    institution:  ECMWF
" + ], + "text/plain": [ + " Size: 8MB\n", + "Dimensions: (latitude: 721, longitude: 1440)\n", + "Coordinates:\n", + " * latitude (latitude) float64 6kB 90.0 89.75 89.5 ... -89.75 -90.0\n", + " * longitude (longitude) float64 12kB 0.0 0.25 0.5 ... 359.5 359.8\n", + " surface_altitude (latitude, longitude) float32 4MB 0.05937 ... 2.775e+03\n", + "Data variables:\n", + " 2t (latitude, longitude) float32 4MB -17.29 -17.29 ... -39.52\n", + "Attributes:\n", + " Conventions: CF-1.8\n", + " institution: ECMWF" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "run = dt.datetime.combine(dt.date.today() - dt.timedelta(days=RUN_DAYS_AGO), dt.time(0))\n", + "valid_time = pd.Timestamp(run + dt.timedelta(hours=LEAD_H))\n", + "url = (\n", + " f\"https://noaa-gfs-bdp-pds.s3.amazonaws.com/gfs.{run:%Y%m%d}/{run:%H}/atmos/gfs.t{run:%H}z.pgrb2.0p25.f{LEAD_H:03d}\"\n", + ")\n", + "print(f\"GFS run {run:%Y-%m-%d %H} UTC, lead time {LEAD_H} h, valid {valid_time:%Y-%m-%d %H:%M} UTC\")\n", + "\n", + "# index lines \"number:offset:date:variable:level:forecast:\": byte ranges of the needed fields\n", + "index = [line.split(\":\") for line in requests.get(f\"{url}.idx\", timeout=60).text.splitlines()]\n", + "offsets = [int(line[1]) for line in index] + [None]\n", + "parts = [\n", + " (offsets[i], offsets[i + 1] - offsets[i])\n", + " for i, line in enumerate(index)\n", + " if (line[3], line[4]) in ((\"TMP\", \"2 m above ground\"), (\"HGT\", \"surface\"))\n", + "]\n", + "\n", + "# hide the download progress bars that earthkit always shows on stderr\n", + "with contextlib.redirect_stderr(io.StringIO()):\n", + " gfs = ekd.from_source(\"url\", url, parts=parts).to_xarray()\n", + "ds = uconv.ds_to(gfs[[\"2t\"]].astype(\"float32\"), {\"2t\": \"degC\"})\n", + "ds = ds.assign_coords(surface_altitude=gfs[\"orog\"].astype(\"float32\").assign_attrs(units=\"m\"))\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "id": "ba309689", + "metadata": {}, + "source": [ + "## Stations and observations\n", + "\n", + "The stations and their observations come from the Global Historical Climatology Network hourly (GHCNh)\n", + "of NOAA NCEI, which collects SYNOP and METAR reports worldwide. Its station list provides the coordinates\n", + "and the altitude used by `temp`. Instead of renaming its columns, the environment variables set in the\n", + "setup cell adapt the `grid_process` data model: the `GHCN_ID` column becomes the `poi` coordinate of the\n", + "output and `ELEVATION` gives the altitude.\n", + "\n", + "The observation is the 2 m temperature report closest to the valid time, within one hour: some stations\n", + "do not report every hour (e.g. the South Pole only at the main synoptic hours, while GHCNh lacks some of\n", + "these hours for Jungfraujoch and Säntis). Values flagged by the quality control of GHCNh (suspect or\n", + "erroneous) are discarded, and stations without a valid report are left out." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a09ed7ce", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-09T22:29:45.845349Z", + "iopub.status.busy": "2026-11-09T22:29:45.844750Z", + "iopub.status.idle": "2026-11-09T22:29:53.061197Z", + "shell.execute_reply": "2026-11-09T22:29:53.060278Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No valid observation at 2026-11-06 12:00 UTC for: none\n" + ] + }, + { + "data": { + "text/html": [ + "
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nameterrainLATITUDELONGITUDEELEVATIONobservation
GHCN_ID
SZM00006730Jungfraujochhigh mountain46.55007.98333576.0-6.50
SZM00006680Säntishigh mountain47.25009.35002500.0-0.20
AUM00011343Sonnblickhigh mountain47.050012.95003113.8-2.80
USW00021514Mauna Loahigh mountain19.5353-155.57613407.47.10
BLI0000SLLPEl Altohigh mountain-16.5133-68.19234061.510.00
MXI0000MMMXMexico Cityhigh city19.4363-99.07212229.910.00
COI0000SKBOBogotáhigh city4.7016-74.14692548.412.00
ECI0000SEQMQuitohigh city-0.1167-78.36672400.013.00
KEI0000HKJKNairobihigh city-1.319236.92781623.724.00
SFI0000FAORJohannesburghigh city-26.139228.24601694.129.00
USW00003017Denverhigh city39.8467-104.65611647.14.40
USW00024127Salt Lake Cityhigh city40.7706-111.96501288.43.90
MGI0000ZMUBUlaanbaatarhigh city47.8431106.76661330.1-3.00
AGI0000DAATTamanrassethigh city22.81155.45111377.127.00
AUI0000LOWIInnsbruckvalley47.260211.3440580.910.00
SZI0000LSGSSionvalley46.21967.3268481.910.00
NPI0000VNKTKathmanduvalley27.696685.35921338.119.00
CIN00330021Santiagovalley-33.3783-70.7964482.012.00
FJI0000NFFNNadicoast/island-17.7554177.443418.024.00
TNI0000NFTFFua'amotucoast/island-21.2412-175.149638.422.00
AQW00061705Pago Pagocoast/island-14.3306-170.71363.725.70
USW00022521Honolulucoast/island21.3239-157.93941.826.00
SGI0000GOOYDakarcoast/island14.7397-17.490225.931.00
JAI0000RJTTTokyocoast/island35.5523139.779710.720.00
ARI0000SAWHUshuaiacoast/island-54.8433-68.295821.68.00
NZI0000NZCHChristchurchcoast/island-43.4894172.532237.520.00
UKI0000EGLLLondoncoast/island51.4775-0.461425.311.22
BRM00081730Manauslowland/desert-3.1333-59.950068.524.00
ASI0000YBASAlice Springslowland/desert-23.8067133.9022545.326.00
AYU089009-1South Polepolar-90.00000.00002835.0-49.40
CAMU0019939Alertpolar82.5000-62.330062.0-14.70
USW00027502Utqiaġvikpolar71.2869-156.73948.2-3.00
SVI0000ENSBLongyearbyenpolar78.246115.465626.8-4.00
\n", + "
" + ], + "text/plain": [ + " name terrain LATITUDE LONGITUDE ELEVATION \\\n", + "GHCN_ID \n", + "SZM00006730 Jungfraujoch high mountain 46.5500 7.9833 3576.0 \n", + "SZM00006680 Säntis high mountain 47.2500 9.3500 2500.0 \n", + "AUM00011343 Sonnblick high mountain 47.0500 12.9500 3113.8 \n", + "USW00021514 Mauna Loa high mountain 19.5353 -155.5761 3407.4 \n", + "BLI0000SLLP El Alto high mountain -16.5133 -68.1923 4061.5 \n", + "MXI0000MMMX Mexico City high city 19.4363 -99.0721 2229.9 \n", + "COI0000SKBO Bogotá high city 4.7016 -74.1469 2548.4 \n", + "ECI0000SEQM Quito high city -0.1167 -78.3667 2400.0 \n", + "KEI0000HKJK Nairobi high city -1.3192 36.9278 1623.7 \n", + "SFI0000FAOR Johannesburg high city -26.1392 28.2460 1694.1 \n", + "USW00003017 Denver high city 39.8467 -104.6561 1647.1 \n", + "USW00024127 Salt Lake City high city 40.7706 -111.9650 1288.4 \n", + "MGI0000ZMUB Ulaanbaatar high city 47.8431 106.7666 1330.1 \n", + "AGI0000DAAT Tamanrasset high city 22.8115 5.4511 1377.1 \n", + "AUI0000LOWI Innsbruck valley 47.2602 11.3440 580.9 \n", + "SZI0000LSGS Sion valley 46.2196 7.3268 481.9 \n", + "NPI0000VNKT Kathmandu valley 27.6966 85.3592 1338.1 \n", + "CIN00330021 Santiago valley -33.3783 -70.7964 482.0 \n", + "FJI0000NFFN Nadi coast/island -17.7554 177.4434 18.0 \n", + "TNI0000NFTF Fua'amotu coast/island -21.2412 -175.1496 38.4 \n", + "AQW00061705 Pago Pago coast/island -14.3306 -170.7136 3.7 \n", + "USW00022521 Honolulu coast/island 21.3239 -157.9394 1.8 \n", + "SGI0000GOOY Dakar coast/island 14.7397 -17.4902 25.9 \n", + "JAI0000RJTT Tokyo coast/island 35.5523 139.7797 10.7 \n", + "ARI0000SAWH Ushuaia coast/island -54.8433 -68.2958 21.6 \n", + "NZI0000NZCH Christchurch coast/island -43.4894 172.5322 37.5 \n", + "UKI0000EGLL London coast/island 51.4775 -0.4614 25.3 \n", + "BRM00081730 Manaus lowland/desert -3.1333 -59.9500 68.5 \n", + "ASI0000YBAS Alice Springs lowland/desert -23.8067 133.9022 545.3 \n", + "AYU089009-1 South Pole polar -90.0000 0.0000 2835.0 \n", + "CAMU0019939 Alert polar 82.5000 -62.3300 62.0 \n", + "USW00027502 Utqiaġvik polar 71.2869 -156.7394 8.2 \n", + "SVI0000ENSB Longyearbyen polar 78.2461 15.4656 26.8 \n", + "\n", + " observation \n", + "GHCN_ID \n", + "SZM00006730 -6.50 \n", + "SZM00006680 -0.20 \n", + "AUM00011343 -2.80 \n", + "USW00021514 7.10 \n", + "BLI0000SLLP 10.00 \n", + "MXI0000MMMX 10.00 \n", + "COI0000SKBO 12.00 \n", + "ECI0000SEQM 13.00 \n", + "KEI0000HKJK 24.00 \n", + "SFI0000FAOR 29.00 \n", + "USW00003017 4.40 \n", + "USW00024127 3.90 \n", + "MGI0000ZMUB -3.00 \n", + "AGI0000DAAT 27.00 \n", + "AUI0000LOWI 10.00 \n", + "SZI0000LSGS 10.00 \n", + "NPI0000VNKT 19.00 \n", + "CIN00330021 12.00 \n", + "FJI0000NFFN 24.00 \n", + "TNI0000NFTF 22.00 \n", + "AQW00061705 25.70 \n", + "USW00022521 26.00 \n", + "SGI0000GOOY 31.00 \n", + "JAI0000RJTT 20.00 \n", + "ARI0000SAWH 8.00 \n", + "NZI0000NZCH 20.00 \n", + "UKI0000EGLL 11.22 \n", + "BRM00081730 24.00 \n", + "ASI0000YBAS 26.00 \n", + "AYU089009-1 -49.40 \n", + "CAMU0019939 -14.70 \n", + "USW00027502 -3.00 \n", + "SVI0000ENSB -4.00 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "GHCNH_URL = \"https://www.ncei.noaa.gov/oa/global-historical-climatology-network/hourly\"\n", + "FAILED_QC = (\"2\", \"3\", \"6\", \"7\") # legacy codes: suspect or erroneous; lower case letters: failed checks\n", + "\n", + "stations = pd.read_csv(f\"{GHCNH_URL}/doc/ghcnh-station-list.csv\").set_index(\"GHCN_ID\", drop=False)\n", + "stations = stations.loc[list(STATIONS)]\n", + "stations[\"name\"] = [name for name, _ in STATIONS.values()]\n", + "stations[\"terrain\"] = [terrain for _, terrain in STATIONS.values()]\n", + "\n", + "\n", + "def observation(ghcn_id: str) -> float:\n", + " \"\"\"Valid 2 m temperature report closest to the valid time (within one hour), NaN if there is none.\"\"\"\n", + " url = f\"{GHCNH_URL}/access/by-year/{valid_time.year}/psv/GHCNh_{ghcn_id}_{valid_time.year}.psv\"\n", + " if not requests.head(url, timeout=60, allow_redirects=True).ok: # no data this year\n", + " return np.nan\n", + " # the yearly files are downloaded once into the earthkit cache (hide the progress bar on stderr)\n", + " with contextlib.redirect_stderr(io.StringIO()):\n", + " path = download_and_cache(url)\n", + " columns = [\"DATE\", \"temperature\", \"temperature_Quality_Code\"]\n", + " df = pd.read_csv(path, sep=\"|\", usecols=columns, dtype={\"temperature_Quality_Code\": str})\n", + " code = df[\"temperature_Quality_Code\"].fillna(\"\")\n", + " df = df[df[\"temperature\"].notna() & ~code.isin(FAILED_QC) & ~code.str.islower()]\n", + " offset = (pd.to_datetime(df[\"DATE\"]) - valid_time).abs()\n", + " df = df[offset <= pd.Timedelta(hours=1)]\n", + " return np.nan if df.empty else float(df.loc[offset[df.index].idxmin(), \"temperature\"])\n", + "\n", + "\n", + "with ThreadPoolExecutor(8) as executor:\n", + " stations[\"observation\"] = list(executor.map(observation, stations.index))\n", + "\n", + "missing = stations[stations[\"observation\"].isna()]\n", + "print(f\"No valid observation at {valid_time:%Y-%m-%d %H:%M} UTC for: {', '.join(missing['name']) or 'none'}\")\n", + "stations = stations.dropna(subset=[\"observation\"])\n", + "pois = gpd.GeoDataFrame(\n", + " stations, geometry=gpd.points_from_xy(stations[\"LONGITUDE\"], stations[\"LATITUDE\"]), crs=\"EPSG:4326\"\n", + ")\n", + "pois[[\"name\", \"terrain\", \"LATITUDE\", \"LONGITUDE\", \"ELEVATION\", \"observation\"]]" + ] + }, + { + "cell_type": "markdown", + "id": "84d2aa1c", + "metadata": {}, + "source": [ + "## Interpolation\n", + "\n", + "Each method is called once, and the duration of this first call is kept for the benchmark below.\n", + "\n", + "The grid and the stations are both in latitude/longitude, and the stations are far beyond the range of\n", + "the Swiss coordinates LV95 (`lv95_max_offset`): `nearest` and `temp` search their neighbors in ECEF\n", + "coordinates. `linear` cannot triangulate points on a sphere in ECEF and raises an error with the generic\n", + "path (`try_reg=False`), as shown below. On a regular grid like GFS, it uses the regular grid\n", + "interpolation instead (`try_reg=True`, the default), bilinear in latitude and longitude." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ed552366", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-09T22:29:53.063206Z", + "iopub.status.busy": "2026-11-09T22:29:53.063007Z", + "iopub.status.idle": "2026-11-09T22:29:54.611154Z", + "shell.execute_reply": "2026-11-09T22:29:54.610760Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "linear with try_reg=False: Method 'linear' is not supported for geographic grids beyond 700 km of the Swiss LV95 central northing, which are interpolated in earth-centered earth-fixed (ECEF) coordinates: a Delaunay triangulation of points close to the Earth's surface is degenerate in ECEF. Use e.g. method='idw' instead, or try_reg=True for a regular grid with 1D coordinates (or adjust GRID_PROCESS__lv95_max_offset).\n" + ] + }, + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 2kB\n",
+       "Dimensions:  (poi: 33)\n",
+       "Coordinates:\n",
+       "  * poi      (poi) <U11 1kB 'SZM00006730' 'SZM00006680' ... 'SVI0000ENSB'\n",
+       "Data variables:\n",
+       "    2t       (poi) float32 132B -7.609 2.084 -1.002 ... -20.62 -4.915 -5.307\n",
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+       "    institution:  ECMWF
" + ], + "text/plain": [ + " Size: 2kB\n", + "Dimensions: (poi: 33)\n", + "Coordinates:\n", + " * poi (poi) " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "diff = pois[list(METHODS)].sub(pois[\"observation\"], axis=0)\n", + "diff_lim = np.ceil(np.nanmax(np.abs(diff.to_numpy())))\n", + "obs_lim = np.ceil(np.nanmax(np.abs(pois[\"observation\"])))\n", + "panels = [(\"observation\", pois[\"observation\"], \"RdYlBu_r\", -obs_lim, obs_lim)]\n", + "panels += [(f\"method={label} − observation\", diff[label], \"RdBu_r\", -diff_lim, diff_lim) for label in METHODS]\n", + "\n", + "fig, axes = plt.subplots(\n", + " 2, 2, figsize=(14, 7.5), layout=\"constrained\", subplot_kw={\"projection\": ccrs.Robinson(central_longitude=0)}\n", + ")\n", + "for ax, (title, values, cmap, vmin, vmax) in zip(axes.flat, panels):\n", + " ax.set_global()\n", + " ax.add_feature(cfeature.COASTLINE, linewidth=0.5, edgecolor=\"0.4\")\n", + " scatter = ax.scatter(\n", + " pois[\"LONGITUDE\"],\n", + " pois[\"LATITUDE\"],\n", + " c=values,\n", + " cmap=cmap,\n", + " vmin=vmin,\n", + " vmax=vmax,\n", + " s=45,\n", + " edgecolor=\"0.2\",\n", + " linewidth=0.4,\n", + " transform=ccrs.PlateCarree(),\n", + " )\n", + " ax.set_title(title)\n", + " if title == \"observation\":\n", + " fig.colorbar(scatter, ax=ax, location=\"left\", shrink=0.8, label=\"observed 2 m temperature [°C]\")\n", + "fig.colorbar(scatter, ax=axes, shrink=0.6, label=\"model − observation [°C]\")\n", + "fig.suptitle(f\"GFS 0.25°, run {run:%Y-%m-%d %H} UTC, valid {valid_time:%Y-%m-%d %H:%M} UTC\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "bcd071d0", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-09T22:29:55.728315Z", + "iopub.status.busy": "2026-11-09T22:29:55.728013Z", + "iopub.status.idle": "2026-11-09T22:29:55.747929Z", + "shell.execute_reply": "2026-11-09T22:29:55.747563Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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terrainstation altitude [m]model altitude [m]observed [°C]nearestlineartemp
name
Jungfraujochhigh mountain3576.02483.0-6.5-1.0-0.1-7.6
Säntishigh mountain2500.01108.0-0.27.47.92.1
Sonnblickhigh mountain3113.81945.0-2.86.06.8-1.0
Mauna Loahigh mountain3407.42667.07.110.912.16.4
El Altohigh mountain4061.54002.010.09.39.18.8
Mexico Cityhigh city2229.92276.010.015.914.514.2
Bogotáhigh city2548.42477.012.013.412.412.7
Quitohigh city2400.02978.013.010.710.413.3
Nairobihigh city1623.71528.024.023.823.423.7
Johannesburghigh city1694.11610.029.032.532.431.5
Denverhigh city1647.11675.04.44.84.75.1
Salt Lake Cityhigh city1288.41404.03.95.65.16.7
Ulaanbaatarhigh city1330.11424.0-3.0-6.2-6.5-6.2
Tamanrassethigh city1377.11360.027.028.127.727.9
Innsbruckvalley580.91540.010.09.59.910.5
Sionvalley481.91519.010.02.51.98.6
Kathmanduvalley1338.11447.019.015.214.915.3
Santiagovalley482.0514.012.012.412.011.9
Nadicoast/island18.0207.024.018.820.123.8
Fua'amotucoast/island38.45.022.022.222.221.8
Pago Pagocoast/island3.730.025.725.625.525.6
Honolulucoast/island1.818.026.024.824.125.2
Dakarcoast/island25.90.031.027.928.135.0
Tokyocoast/island10.76.020.019.619.619.8
Ushuaiacoast/island21.6443.08.04.04.47.7
Christchurchcoast/island37.551.020.014.714.815.2
Londoncoast/island25.343.011.211.211.211.4
Manauslowland/desert68.521.024.029.029.630.2
Alice Springslowland/desert545.3571.026.029.629.729.9
South Polepolar2835.02775.0-49.4-39.5-39.5-39.9
Alertpolar62.0101.0-14.7-21.0-21.0-20.6
Utqiaġvikpolar8.28.0-3.0-4.9-4.7-4.9
Longyearbyenpolar26.8249.0-4.0-8.5-8.4-5.3
\n", + "
" + ], + "text/plain": [ + " terrain station altitude [m] model altitude [m] \\\n", + "name \n", + "Jungfraujoch high mountain 3576.0 2483.0 \n", + "Säntis high mountain 2500.0 1108.0 \n", + "Sonnblick high mountain 3113.8 1945.0 \n", + "Mauna Loa high mountain 3407.4 2667.0 \n", + "El Alto high mountain 4061.5 4002.0 \n", + "Mexico City high city 2229.9 2276.0 \n", + "Bogotá high city 2548.4 2477.0 \n", + "Quito high city 2400.0 2978.0 \n", + "Nairobi high city 1623.7 1528.0 \n", + "Johannesburg high city 1694.1 1610.0 \n", + "Denver high city 1647.1 1675.0 \n", + "Salt Lake City high city 1288.4 1404.0 \n", + "Ulaanbaatar high city 1330.1 1424.0 \n", + "Tamanrasset high city 1377.1 1360.0 \n", + "Innsbruck valley 580.9 1540.0 \n", + "Sion valley 481.9 1519.0 \n", + "Kathmandu valley 1338.1 1447.0 \n", + "Santiago valley 482.0 514.0 \n", + "Nadi coast/island 18.0 207.0 \n", + "Fua'amotu coast/island 38.4 5.0 \n", + "Pago Pago coast/island 3.7 30.0 \n", + "Honolulu coast/island 1.8 18.0 \n", + "Dakar coast/island 25.9 0.0 \n", + "Tokyo coast/island 10.7 6.0 \n", + "Ushuaia coast/island 21.6 443.0 \n", + "Christchurch coast/island 37.5 51.0 \n", + "London coast/island 25.3 43.0 \n", + "Manaus lowland/desert 68.5 21.0 \n", + "Alice Springs lowland/desert 545.3 571.0 \n", + "South Pole polar 2835.0 2775.0 \n", + "Alert polar 62.0 101.0 \n", + "Utqiaġvik polar 8.2 8.0 \n", + "Longyearbyen polar 26.8 249.0 \n", + "\n", + " observed [°C] nearest linear temp \n", + "name \n", + "Jungfraujoch -6.5 -1.0 -0.1 -7.6 \n", + "Säntis -0.2 7.4 7.9 2.1 \n", + "Sonnblick -2.8 6.0 6.8 -1.0 \n", + "Mauna Loa 7.1 10.9 12.1 6.4 \n", + "El Alto 10.0 9.3 9.1 8.8 \n", + "Mexico City 10.0 15.9 14.5 14.2 \n", + "Bogotá 12.0 13.4 12.4 12.7 \n", + "Quito 13.0 10.7 10.4 13.3 \n", + "Nairobi 24.0 23.8 23.4 23.7 \n", + "Johannesburg 29.0 32.5 32.4 31.5 \n", + "Denver 4.4 4.8 4.7 5.1 \n", + "Salt Lake City 3.9 5.6 5.1 6.7 \n", + "Ulaanbaatar -3.0 -6.2 -6.5 -6.2 \n", + "Tamanrasset 27.0 28.1 27.7 27.9 \n", + "Innsbruck 10.0 9.5 9.9 10.5 \n", + "Sion 10.0 2.5 1.9 8.6 \n", + "Kathmandu 19.0 15.2 14.9 15.3 \n", + "Santiago 12.0 12.4 12.0 11.9 \n", + "Nadi 24.0 18.8 20.1 23.8 \n", + "Fua'amotu 22.0 22.2 22.2 21.8 \n", + "Pago Pago 25.7 25.6 25.5 25.6 \n", + "Honolulu 26.0 24.8 24.1 25.2 \n", + "Dakar 31.0 27.9 28.1 35.0 \n", + "Tokyo 20.0 19.6 19.6 19.8 \n", + "Ushuaia 8.0 4.0 4.4 7.7 \n", + "Christchurch 20.0 14.7 14.8 15.2 \n", + "London 11.2 11.2 11.2 11.4 \n", + "Manaus 24.0 29.0 29.6 30.2 \n", + "Alice Springs 26.0 29.6 29.7 29.9 \n", + "South Pole -49.4 -39.5 -39.5 -39.9 \n", + "Alert -14.7 -21.0 -21.0 -20.6 \n", + "Utqiaġvik -3.0 -4.9 -4.7 -4.9 \n", + "Longyearbyen -4.0 -8.5 -8.4 -5.3 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "table = pois[[\"name\", \"terrain\", \"ELEVATION\", \"model altitude\", \"observation\", *METHODS]].rename(\n", + " columns={\n", + " \"ELEVATION\": \"station altitude [m]\",\n", + " \"model altitude\": \"model altitude [m]\",\n", + " \"observation\": \"observed [°C]\",\n", + " }\n", + ")\n", + "table = table.astype({label: \"float64\" for label in METHODS}) # float32 values display with spurious digits\n", + "table.round({\"model altitude [m]\": 0, \"observed [°C]\": 1, **dict.fromkeys(METHODS, 1)}).set_index(\"name\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7c656fdd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-09T22:29:55.751308Z", + "iopub.status.busy": "2026-11-09T22:29:55.750995Z", + "iopub.status.idle": "2026-11-09T22:29:55.997013Z", + "shell.execute_reply": "2026-11-09T22:29:55.996609Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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first call [s]subsequent calls [s]
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linear0.0170.005 ± 0.000
temp0.6280.213 ± 0.003
\n", + "
" + ], + "text/plain": [ + " first call [s] subsequent calls [s]\n", + "method \n", + "nearest 0.623 0.167 ± 0.003\n", + "linear 0.017 0.005 ± 0.000\n", + "temp 0.628 0.213 ± 0.003" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def benchmark(\n", + " func: Callable[..., xr.Dataset],\n", + " data: xr.Dataset,\n", + " target: gpd.GeoDataFrame | xr.Dataset,\n", + " methods: dict[str, dict[str, Any]],\n", + " first_call_times: dict[str, float],\n", + " repeat: int = 5,\n", + ") -> pd.DataFrame:\n", + " \"\"\"Runtime of the first call and mean ± standard deviation of the subsequent calls of each method.\"\"\"\n", + " rows: dict[str, dict[str, Any]] = {}\n", + " for method, kw in methods.items():\n", + " times = np.array(timeit.repeat(partial(func, data, target, **kw), number=1, repeat=repeat))\n", + " rows[method] = {\n", + " \"first call [s]\": f\"{first_call_times[method]:.3f}\",\n", + " \"subsequent calls [s]\": f\"{times.mean():.3f} ± {times.std():.3f}\",\n", + " }\n", + " return pd.DataFrame.from_dict(rows, orient=\"index\").rename_axis(\"method\")\n", + "\n", + "\n", + "print(f\"{ds.latitude.size} × {ds.longitude.size} grid points -> {len(pois)} stations\")\n", + "benchmark(gri.interp2poi, ds, pois, METHODS, first_call)" + ] + }, + { + "cell_type": "markdown", + "id": "a115db9d", + "metadata": {}, + "source": [ + "## Takeaways\n", + "\n", + "- At 0.25° the model orography smooths out the mountains: on Säntis, Sonnblick, Jungfraujoch and Mauna Loa\n", + " the nearest grid point is 700 to 1400 m below the station, and `nearest` and `linear` are about 4 to 10 °C\n", + " too warm. In the valley of Sion the grid point is 1000 m above the station, and they are about 8 °C too cold.\n", + " `temp` corrects most of these differences: its errors stay within about 2.5 °C at these stations.\n", + "- Where the model altitude is close to the station altitude, the three methods agree. The remaining\n", + " differences to the observations (e.g. in Mexico City, Manaus, Christchurch or at the South Pole) come from\n", + " the model itself, not from the interpolation. Observations taken up to an hour from the valid time also\n", + " add some differences.\n", + "- Coastal stations are more mixed: the neighborhood of `temp` contains land and sea grid points. At Nadi it\n", + " removes most of the error of `nearest`, while at Dakar it is warmer than the observation.\n", + "- The ECEF neighbor search handles the stations next to the antimeridian (Nadi, Fua'amotu, Pago Pago) and\n", + " close to the poles like any other. At the South Pole, all neighbors are the same pole point repeated in the\n", + " first grid row: `temp` cannot fit a sounding and falls back to a fixed lapse rate.\n", + "- `linear` is not available in ECEF: on a regular latitude/longitude grid such as GFS it goes through the\n", + " regular grid interpolation (`try_reg=True`, the default), which also handles the 0..360° longitudes of GFS\n", + " and the stations across its 360° seam.\n", + "- The first call of `nearest` and `temp` includes converting the 1 million grid points to ECEF and building\n", + " the neighbor search, about 0.6 s. Subsequent calls take about 0.2 s, and `linear` a few milliseconds." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.13.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/examples/06_interp2grid_era5_global_temperature.ipynb b/doc/examples/06_interp2grid_era5_global_temperature.ipynb new file mode 100644 index 0000000..2038584 --- /dev/null +++ b/doc/examples/06_interp2grid_era5_global_temperature.ipynb @@ -0,0 +1,2374 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "71613b2a", + "metadata": {}, + "source": [ + "# Regridding: ERA5 2 m temperature on the global IFS 0.1° grid\n", + "\n", + "This example illustrates {func}`~grid_process.grid_interp.interp2grid` on the whole globe with three\n", + "methods:\n", + "\n", + "- `nearest`: value of the nearest source grid point;\n", + "- `linear`: bilinear interpolation on the regular latitude/longitude grid;\n", + "- `temp_fixgrad`: among the source grid points within `r_search` grid spacings, the one with the most\n", + " similar altitude, corrected for the remaining altitude difference with a fixed lapse rate\n", + " (6 K/km by default).\n", + "\n", + "The 2 m temperature of the ERA5 reanalysis (regular 0.25° grid, about 28 km, 1.04 million points) at a\n", + "single time is regridded to the global 0.1° grid of the IFS open data (about 9 km, 6.5 million cells),\n", + "whose orography resolves the mountains in much more detail.\n", + "\n", + "Both grids are in latitude/longitude and cover the globe, so the nearest neighbor searches of `nearest`\n", + "(with `try_reg=False`) and `temp_fixgrad` run in earth-centered earth-fixed coordinates (ECEF): distances\n", + "are measured in meters through the Earth, so they are correct everywhere, across the antimeridian and at\n", + "the poles.\n", + "\n", + "Running this notebook needs a free account on the\n", + "[Copernicus Climate Data Store](https://cds.climate.copernicus.eu): your API token in `~/.cdsapirc`\n", + "and the licence of the ERA5 dataset accepted once on its download page. It uses about 8 GB of memory.\n", + "\n", + "*Data: contains modified Copernicus Climate Change Service information 2026, ERA5 hourly data on single\n", + "levels (DOI: 10.24381/cds.adbb2d47), CC BY 4.0. ECMWF open data, © ECMWF, CC BY 4.0. Coastlines: Natural\n", + "Earth.*" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ea621b3d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-09T22:35:03.558711Z", + "iopub.status.busy": "2026-11-09T22:35:03.558472Z", + "iopub.status.idle": "2026-11-09T22:35:05.851140Z", + "shell.execute_reply": "2026-11-09T22:35:05.849994Z" + } + }, + "outputs": [], + "source": [ + "import contextlib\n", + "import io\n", + "import logging\n", + "import re\n", + "import time\n", + "import timeit\n", + "from collections.abc import Callable\n", + "from functools import partial\n", + "from typing import Any\n", + "\n", + "# import pyproj before earthkit.data to avoid a crash at exit (earthkit-data#1202)\n", + "import pyproj # noqa: F401 # pylint: disable=unused-import\n", + "\n", + "import cartopy.crs as ccrs\n", + "import earthkit.data as ekd\n", + "import earthkit.plots as ekp\n", + "import geopandas as gpd\n", + "import numpy as np\n", + "import pandas as pd\n", + "import requests\n", + "import xarray as xr\n", + "from requests.adapters import HTTPAdapter\n", + "from urllib3.util import Retry\n", + "\n", + "import grid_process.grid_interp as gri\n", + "import grid_process.units_conv as uconv\n", + "\n", + "METHODS: dict[str, dict[str, Any]] = {\n", + " \"nearest\": {\"method\": \"nearest\"},\n", + " \"linear\": {\"method\": \"linear\"},\n", + " \"temp_fixgrad\": {\"method\": \"temp_fixgrad\"},\n", + "}\n", + "ERA5_TIME = \"2025-01-15T12:00\" # single time of the ERA5 reanalysis\n", + "# zooms on mountain regions: (longitude, latitude) bounds\n", + "REGIONS = {\n", + " \"Himalaya\": ((75.0, 100.0), (40.0, 25.0)),\n", + " \"Andes\": ((-80.0, -60.0), (-10.0, -35.0)),\n", + " \"Alps\": ((5.0, 16.5), (48.5, 43.5)),\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "6dec146e", + "metadata": {}, + "source": [ + "## Destination grid\n", + "\n", + "The destination grid is the global 0.1° latitude/longitude grid of the IFS open data (3600 × 1801 cells,\n", + "longitudes from -180° to 180°). Its orography, the surface geopotential `z` at step 0, gives the\n", + "altitude in a `surface_altitude` coordinate, which `temp_fixgrad` needs. The grid is fixed, so any run\n", + "does: we take the latest one on the ECMWF open data portal." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "913f81d8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-09T22:35:05.854409Z", + "iopub.status.busy": "2026-11-09T22:35:05.853931Z", + "iopub.status.idle": "2026-11-09T22:35:08.172845Z", + "shell.execute_reply": "2026-11-09T22:35:08.171599Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IFS-HRES run 20261109 12 UTC (orography only)\n" + ] + }, + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 26MB\n",
+       "Dimensions:           (latitude: 1801, longitude: 3600)\n",
+       "Coordinates:\n",
+       "  * latitude          (latitude) float64 14kB 90.0 89.9 89.8 ... -89.9 -90.0\n",
+       "  * longitude         (longitude) float64 29kB -180.0 -179.9 ... 179.8 179.9\n",
+       "    surface_altitude  (latitude, longitude) float32 26MB 0.2591 ... 2.765e+03\n",
+       "Data variables:\n",
+       "    *empty*\n",
+       "Attributes:\n",
+       "    crs:      EPSG:4326
" + ], + "text/plain": [ + " Size: 26MB\n", + "Dimensions: (latitude: 1801, longitude: 3600)\n", + "Coordinates:\n", + " * latitude (latitude) float64 14kB 90.0 89.9 89.8 ... -89.9 -90.0\n", + " * longitude (longitude) float64 29kB -180.0 -179.9 ... 179.8 179.9\n", + " surface_altitude (latitude, longitude) float32 26MB 0.2591 ... 2.765e+03\n", + "Data variables:\n", + " *empty*\n", + "Attributes:\n", + " crs: EPSG:4326" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "OD_ROOT = \"https://data.ecmwf.int/forecasts\"\n", + "\n", + "# the portal sometimes times out: retry with an increasing delay (earthkit already retries the field downloads)\n", + "session = requests.Session()\n", + "retry = Retry(total=5, backoff_factor=2, status_forcelist=[429, 500, 502, 503, 504])\n", + "session.mount(\"https://\", HTTPAdapter(max_retries=retry))\n", + "# hide the notice about the connection limit of the portal that the ECMWF open data client logs\n", + "logging.getLogger(\"ecmwf.opendata.utils\").addFilter(lambda record: \"500 simultaneous\" not in record.getMessage())\n", + "\n", + "\n", + "def index_url(day: str, hour: int, step: int) -> str:\n", + " return f\"{OD_ROOT}/{day}/{hour:02d}z/ifs/0p1/oper/{day}{hour:02d}0000-{step}h-oper-fc-sfc.index\"\n", + "\n", + "\n", + "def list_days() -> list[str]:\n", + " \"\"\"Days with forecasts on the portal, oldest first.\"\"\"\n", + " return sorted(set(re.findall(r'href=\"/forecasts/(\\d{8})/\"', session.get(f\"{OD_ROOT}/\", timeout=30).text)))\n", + "\n", + "\n", + "def latest_ifs_run(step: int) -> tuple[str, int]:\n", + " \"\"\"Latest IFS-HRES run on the ECMWF open data portal with the given step already published.\"\"\"\n", + " for day in reversed(list_days()):\n", + " for hour in (18, 12, 6, 0):\n", + " if session.head(index_url(day, hour, step), timeout=30).ok:\n", + " return day, hour\n", + " raise RuntimeError(\"No IFS-HRES run found on the open data portal\")\n", + "\n", + "\n", + "run_date, run_hour = latest_ifs_run(0)\n", + "# hide the download progress bars that earthkit always shows on stderr\n", + "with contextlib.redirect_stderr(io.StringIO()):\n", + " z_surf = ekd.from_source(\n", + " \"ecmwf-open-data\",\n", + " model=\"ifs\",\n", + " resol=\"0p1\",\n", + " stream=\"oper\",\n", + " type=\"fc\",\n", + " date=int(run_date),\n", + " time=run_hour,\n", + " param=\"z\",\n", + " levtype=\"sfc\",\n", + " step=0,\n", + " ).to_xarray()[\"z\"]\n", + "z_surf = z_surf.squeeze().reset_coords(drop=True)\n", + "dst_grid = xr.Dataset(\n", + " coords={\"surface_altitude\": (z_surf / 9.80665).astype(\"float32\").assign_attrs(units=\"m\")},\n", + " attrs={\"crs\": \"EPSG:4326\"},\n", + ")\n", + "print(f\"IFS-HRES run {run_date} {run_hour:02d} UTC (orography only)\")\n", + "dst_grid" + ] + }, + { + "cell_type": "markdown", + "id": "d80bd3dd", + "metadata": {}, + "source": [ + "## Source data\n", + "\n", + "The ERA5 2 m temperature and its orography (the surface geopotential `z`) are retrieved from the Climate\n", + "Data Store for a single time, on the regular 0.25° grid of the CDS (721 × 1440 points). ERA5 longitudes\n", + "run from 0° to 360°, those of the destination grid from -180° to 180°: `grid_process` handles both\n", + "conventions, including across the 360° seam of the source grid." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "11607a27", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-09T22:35:08.175765Z", + "iopub.status.busy": "2026-11-09T22:35:08.175088Z", + "iopub.status.idle": "2026-11-09T22:35:08.317722Z", + "shell.execute_reply": "2026-11-09T22:35:08.316603Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 8MB\n",
+       "Dimensions:           (latitude: 721, longitude: 1440)\n",
+       "Coordinates:\n",
+       "  * latitude          (latitude) float64 6kB 90.0 89.75 89.5 ... -89.75 -90.0\n",
+       "  * longitude         (longitude) float64 12kB 0.0 0.25 0.5 ... 359.5 359.8\n",
+       "    surface_altitude  (latitude, longitude) float32 4MB -0.1264 ... 2.826e+03\n",
+       "Data variables:\n",
+       "    t2m               (latitude, longitude) float32 4MB -27.09 -27.09 ... -26.01\n",
+       "Attributes:\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    Conventions:             CF-1.7\n",
+       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
+       "    history:                 2026-11-09T20:06 GRIB to CDM+CF via cfgrib-0.9.1...
" + ], + "text/plain": [ + " Size: 8MB\n", + "Dimensions: (latitude: 721, longitude: 1440)\n", + "Coordinates:\n", + " * latitude (latitude) float64 6kB 90.0 89.75 89.5 ... -89.75 -90.0\n", + " * longitude (longitude) float64 12kB 0.0 0.25 0.5 ... 359.5 359.8\n", + " surface_altitude (latitude, longitude) float32 4MB -0.1264 ... 2.826e+03\n", + "Data variables:\n", + " t2m (latitude, longitude) float32 4MB -27.09 -27.09 ... -26.01\n", + "Attributes:\n", + " GRIB_centre: ecmf\n", + " GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n", + " GRIB_subCentre: 0\n", + " Conventions: CF-1.7\n", + " institution: European Centre for Medium-Range Weather Forecasts\n", + " history: 2026-11-09T20:06 GRIB to CDM+CF via cfgrib-0.9.1..." + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "request = {\n", + " \"product_type\": [\"reanalysis\"],\n", + " \"variable\": [\"2m_temperature\", \"geopotential\"],\n", + " \"date\": [ERA5_TIME[:10]],\n", + " \"time\": [ERA5_TIME[11:]],\n", + " \"data_format\": \"netcdf\",\n", + " \"download_format\": \"unarchived\",\n", + "}\n", + "era5 = ekd.from_source(\"cds\", \"reanalysis-era5-single-levels\", request).to_xarray()\n", + "era5 = era5.squeeze(\"valid_time\").reset_coords(drop=True)\n", + "\n", + "ds = uconv.ds_to(era5[[\"t2m\"]], {\"t2m\": \"degC\"})\n", + "ds = ds.assign_coords(surface_altitude=(era5[\"z\"] / 9.80665).astype(\"float32\").assign_attrs(units=\"m\"))\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "id": "61ed656a", + "metadata": {}, + "source": [ + "## Interpolation\n", + "\n", + "Each method is called once, and the duration of this first call is kept for the benchmark below. On a\n", + "regular grid with 1D coordinates, `nearest` and `linear` use xarray's interpolation by default\n", + "(`try_reg=True`), in latitude and longitude. `nearest` is also run through the generic path\n", + "(`try_reg=False`), which searches the nearest source grid point in ECEF coordinates. On a regular grid\n", + "both select the same point: a destination cell lies in one source grid box, and the corner closest in\n", + "latitude and in longitude is also the closest one on the sphere. The generic path matters for the\n", + "methods using a neighborhood (e.g. `idw`, `temp_fixgrad`), whose extent must be measured in meters,\n", + "and for irregular grids. `linear` cannot triangulate points on a sphere in ECEF: on global\n", + "latitude/longitude grids it is only available through the regular grid interpolation." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a7d4c1c4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-09T22:35:08.320446Z", + "iopub.status.busy": "2026-11-09T22:35:08.320054Z", + "iopub.status.idle": "2026-11-09T22:35:18.540527Z", + "shell.execute_reply": "2026-11-09T22:35:18.538698Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nearest: 0.0 s, 0 NaN cells\n", + "linear: 0.1 s, 0 NaN cells\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp_fixgrad: 7.2 s, 0 NaN cells\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nearest (try_reg=False): 2.8 s, 0 NaN cells\n", + "nearest, try_reg=True vs. False: 0 of 6483600 cells differ\n" + ] + }, + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 52MB\n",
+       "Dimensions:           (latitude: 1801, longitude: 3600)\n",
+       "Coordinates:\n",
+       "  * latitude          (latitude) float64 14kB 90.0 89.9 89.8 ... -89.9 -90.0\n",
+       "  * longitude         (longitude) float64 29kB -180.0 -179.9 ... 179.8 179.9\n",
+       "    surface_altitude  (latitude, longitude) float32 26MB 0.2591 ... 2.765e+03\n",
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+       "    institution:  European Centre for Medium-Range Weather Forecasts\n",
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" + ], + "text/plain": [ + " Size: 52MB\n", + "Dimensions: (latitude: 1801, longitude: 3600)\n", + "Coordinates:\n", + " * latitude (latitude) float64 14kB 90.0 89.9 89.8 ... -89.9 -90.0\n", + " * longitude (longitude) float64 29kB -180.0 -179.9 ... 179.8 179.9\n", + " surface_altitude (latitude, longitude) float32 26MB 0.2591 ... 2.765e+03\n", + "Data variables:\n", + " t2m (latitude, longitude) float32 26MB -27.1 -27.1 ... -25.64\n", + "Attributes:\n", + " institution: European Centre for Medium-Range Weather Forecasts\n", + " history: 2026-11-09T20:06 GRIB to CDM+CF via cfgrib-0.9.15.1/ecCodes...\n", + " crs: EPSG:4326" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "runs = dict(METHODS)\n", + "runs[\"nearest (try_reg=False)\"] = METHODS[\"nearest\"] | {\"try_reg\": False}\n", + "\n", + "results: dict[str, xr.Dataset] = {}\n", + "first_call: dict[str, float] = {}\n", + "for label, kwargs in runs.items():\n", + " t0 = time.perf_counter()\n", + " results[label] = gri.interp2grid(ds, dst_grid, **kwargs)\n", + " first_call[label] = time.perf_counter() - t0\n", + " n_nan = int(results[label][\"t2m\"].isnull().sum())\n", + " print(f\"{label}: {first_call[label]:.1f} s, {n_nan} NaN cells\")\n", + "\n", + "n_diff = int((results[\"nearest\"][\"t2m\"] != results[\"nearest (try_reg=False)\"][\"t2m\"]).sum())\n", + "print(f\"nearest, try_reg=True vs. False: {n_diff} of {dst_grid.surface_altitude.size} cells differ\")\n", + "results[\"temp_fixgrad\"]" + ] + }, + { + "cell_type": "markdown", + "id": "0358b684", + "metadata": {}, + "source": [ + "## Visual comparison\n", + "\n", + "The global maps show the three methods and the correction of `temp_fixgrad` with respect to `linear`\n", + "(differences below 1 °C in white). The zooms on three mountain regions compare `linear` and\n", + "`temp_fixgrad`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "14e68913", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-09T22:35:18.542907Z", + "iopub.status.busy": "2026-11-09T22:35:18.542670Z", + "iopub.status.idle": "2026-11-09T22:35:58.278270Z", + "shell.execute_reply": "2026-11-09T22:35:58.277029Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "style = ekp.styles.Style(levels=np.arange(-50, 46, 5), colors=\"RdYlBu_r\", extend=\"both\", units=\"°C\")\n", + "diff_style = ekp.styles.Style(levels=[-12, -8, -4, -2, -1, 1, 2, 4, 8, 12], colors=\"RdBu_r\", extend=\"both\", units=\"°C\")\n", + "correction = results[\"temp_fixgrad\"][\"t2m\"] - results[\"linear\"][\"t2m\"]\n", + "panels = [(f\"method={label}\", results[label][\"t2m\"], style) for label in METHODS]\n", + "panels.append((\"temp_fixgrad − linear\", correction, diff_style))\n", + "\n", + "fig = ekp.Figure(rows=2, columns=2, figsize=(14, 8), crs=ccrs.PlateCarree())\n", + "for i, (title, field, panel_style) in enumerate(panels):\n", + " subplot = fig.add_map(i // 2, i % 2)\n", + " subplot.pcolormesh(\n", + " field.values, x=field.longitude.values, y=field.latitude.values, style=panel_style, transform=ccrs.PlateCarree()\n", + " )\n", + " subplot.coastlines(linewidth=0.4)\n", + " subplot.title(title)\n", + " subplot.legend(label=\"2 m temperature [°C]\" if panel_style is style else \"difference [°C]\")\n", + "fig.title(f\"ERA5 2 m temperature {ERA5_TIME.replace('T', ' ')} UTC on the IFS 0.1° grid\")\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "04b5e638", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-09T22:35:58.280269Z", + "iopub.status.busy": "2026-11-09T22:35:58.280050Z", + "iopub.status.idle": "2026-11-09T22:35:59.110432Z", + "shell.execute_reply": "2026-11-09T22:35:59.108342Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = ekp.Figure(rows=len(REGIONS), columns=2, figsize=(12, 4.2 * len(REGIONS)), crs=ccrs.PlateCarree())\n", + "for row, (region, (lon_bnds, lat_bnds)) in enumerate(REGIONS.items()):\n", + " box = {\"longitude\": slice(*lon_bnds), \"latitude\": slice(*lat_bnds)}\n", + " fields = [results[label][\"t2m\"].sel(box) for label in (\"linear\", \"temp_fixgrad\")]\n", + " region_style = ekp.styles.Style(\n", + " levels=np.arange(np.floor(min(f.min() for f in fields)), np.ceil(max(f.max() for f in fields)) + 2, 2),\n", + " colors=\"RdYlBu_r\",\n", + " extend=\"both\",\n", + " units=\"°C\",\n", + " )\n", + " for col, (label, field) in enumerate(zip((\"linear\", \"temp_fixgrad\"), fields)):\n", + " subplot = fig.add_map(row, col)\n", + " subplot.pcolormesh(\n", + " field.values,\n", + " x=field.longitude.values,\n", + " y=field.latitude.values,\n", + " style=region_style,\n", + " transform=ccrs.PlateCarree(),\n", + " )\n", + " subplot.coastlines(linewidth=0.5)\n", + " subplot.borders(linewidth=0.3)\n", + " subplot.title(f\"{region}, method={label}\")\n", + " subplot.legend(label=\"2 m temperature [°C]\")\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "109cc7ef", + "metadata": {}, + "source": [ + "## Performance benchmark\n", + "\n", + "The first call includes building the neighbor search structures and converting the coordinates, which\n", + "`grid_process` caches. Subsequent calls on the same grids (e.g. for other variables or times) reuse them:\n", + "their runtime is the mean ± standard deviation over 5 repetitions." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4e49db89", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-09T22:35:59.112985Z", + "iopub.status.busy": "2026-11-09T22:35:59.112752Z", + "iopub.status.idle": "2026-11-09T22:36:23.436338Z", + "shell.execute_reply": "2026-11-09T22:36:23.435118Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "721 × 1440 source grid points -> 1801 × 3600 cells\n" + ] + }, + { + "data": { + "text/html": [ + "
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first call [s]subsequent calls [s]
method
nearest0.0340.020 ± 0.001
linear0.1490.127 ± 0.001
temp_fixgrad7.2003.266 ± 0.022
nearest (try_reg=False)2.7771.448 ± 0.003
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" + ], + "text/plain": [ + " first call [s] subsequent calls [s]\n", + "method \n", + "nearest 0.034 0.020 ± 0.001\n", + "linear 0.149 0.127 ± 0.001\n", + "temp_fixgrad 7.200 3.266 ± 0.022\n", + "nearest (try_reg=False) 2.777 1.448 ± 0.003" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def benchmark(\n", + " func: Callable[..., xr.Dataset],\n", + " data: xr.Dataset,\n", + " target: gpd.GeoDataFrame | xr.Dataset,\n", + " methods: dict[str, dict[str, Any]],\n", + " first_call_times: dict[str, float],\n", + " repeat: int = 5,\n", + ") -> pd.DataFrame:\n", + " \"\"\"Runtime of the first call and mean ± standard deviation of the subsequent calls of each method.\"\"\"\n", + " rows: dict[str, dict[str, Any]] = {}\n", + " for method, kw in methods.items():\n", + " times = np.array(timeit.repeat(partial(func, data, target, **kw), number=1, repeat=repeat))\n", + " rows[method] = {\n", + " \"first call [s]\": f\"{first_call_times[method]:.3f}\",\n", + " \"subsequent calls [s]\": f\"{times.mean():.3f} ± {times.std():.3f}\",\n", + " }\n", + " return pd.DataFrame.from_dict(rows, orient=\"index\").rename_axis(\"method\")\n", + "\n", + "\n", + "print(\n", + " f\"{ds.latitude.size} × {ds.longitude.size} source grid points -> \"\n", + " f\"{dst_grid.latitude.size} × {dst_grid.longitude.size} cells\"\n", + ")\n", + "benchmark(gri.interp2grid, ds, dst_grid, runs, first_call)" + ] + }, + { + "cell_type": "markdown", + "id": "d7a2f3ba", + "metadata": {}, + "source": [ + "## Takeaways\n", + "\n", + "- `nearest` and `linear` keep the 0.25° (28 km) structure of ERA5: they ignore the more detailed\n", + " orography of the 0.1° grid. `temp_fixgrad` corrects for the altitude difference between the source grid\n", + " point and the destination cell, which brings back the terrain: cold ridges and warmer valleys in the\n", + " Himalaya, the Andes and the Alps.\n", + "- The correction exceeds 1 °C in only 3 % of the cells worldwide, but in 14 to 31 % of the cells of the\n", + " three mountain regions, mostly within ±5 °C. Over the oceans and plains the methods agree within\n", + " about 1 °C; there `temp_fixgrad` takes a single source grid point, like `nearest`, while `linear`\n", + " blends four.\n", + "- All cells get a value with every method, including across the 360° seam of the ERA5 grid (0 to 360°)\n", + " in the -180 to 180° destination grid and at the poles.\n", + "- On a regular grid, `nearest` selects the same source point with the regular grid interpolation\n", + " (`try_reg=True`, in degrees) and with the generic path in ECEF (`try_reg=False`), but about 70 times\n", + " faster. The generic path is needed for the neighborhood methods (here `temp_fixgrad`) and irregular\n", + " grids, whose neighbors must be searched in meters.\n", + "- Regridding 1 million source points to 6.5 million cells takes a fraction of a second with the regular\n", + " grid interpolation. The generic path converts both grids to ECEF and builds the neighbor search at the\n", + " first call (about 3 s for `nearest`, 7 s for `temp_fixgrad`), which subsequent calls reuse." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.13.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From f37179a21f0c5dd6dd28c4e4f77a966fc6a43b90 Mon Sep 17 00:00:00 2001 From: Mathieu Schaer Date: Tue, 10 Nov 2026 00:56:20 +0100 Subject: [PATCH 05/10] Add nearest with try_reg=True to the benchmark of the GFS station notebook - benchmark nearest through both the regular grid interpolation and the ECEF neighbor search, and compare their grid points at the stations - label the stations whose model altitude differs by more than 300 m - mention masking the sea grid points for coastal stations Co-Authored-By: Claude Opus 5.5 --- ...05_interp2poi_gfs_global_temperature.ipynb | 141 ++++++++++-------- 1 file changed, 82 insertions(+), 59 deletions(-) diff --git a/doc/examples/05_interp2poi_gfs_global_temperature.ipynb b/doc/examples/05_interp2poi_gfs_global_temperature.ipynb index 0aa39e7..d5181b7 100644 --- a/doc/examples/05_interp2poi_gfs_global_temperature.ipynb +++ b/doc/examples/05_interp2poi_gfs_global_temperature.ipynb @@ -43,10 +43,10 @@ "id": "28c62853", "metadata": { "execution": { - "iopub.execute_input": "2026-11-09T22:29:41.230114Z", - "iopub.status.busy": "2026-11-09T22:29:41.229875Z", - "iopub.status.idle": "2026-11-09T22:29:43.981983Z", - "shell.execute_reply": "2026-11-09T22:29:43.980470Z" + "iopub.execute_input": "2026-11-09T23:51:07.026291Z", + "iopub.status.busy": "2026-11-09T23:51:07.026099Z", + "iopub.status.idle": "2026-11-09T23:51:09.132490Z", + "shell.execute_reply": "2026-11-09T23:51:09.130371Z" } }, "outputs": [], @@ -149,10 +149,10 @@ "id": "7e8d765a", "metadata": { "execution": { - "iopub.execute_input": "2026-11-09T22:29:43.985253Z", - "iopub.status.busy": "2026-11-09T22:29:43.984678Z", - "iopub.status.idle": "2026-11-09T22:29:45.842085Z", - "shell.execute_reply": "2026-11-09T22:29:45.840652Z" + "iopub.execute_input": "2026-11-09T23:51:09.135329Z", + "iopub.status.busy": "2026-11-09T23:51:09.134889Z", + "iopub.status.idle": "2026-11-09T23:51:11.124389Z", + "shell.execute_reply": "2026-11-09T23:51:11.123522Z" } }, "outputs": [ @@ -718,8 +718,8 @@ " 2t (latitude, longitude) float32 4MB -17.29 -17.29 ... -39.52\n", "Attributes:\n", " Conventions: CF-1.8\n", - " institution: ECMWF
  • Conventions :
    CF-1.8
    institution :
    ECMWF
    • " ], "text/plain": [ " Size: 8MB\n", @@ -814,10 +814,10 @@ "id": "a09ed7ce", "metadata": { "execution": { - "iopub.execute_input": "2026-11-09T22:29:45.845349Z", - "iopub.status.busy": "2026-11-09T22:29:45.844750Z", - "iopub.status.idle": "2026-11-09T22:29:53.061197Z", - "shell.execute_reply": "2026-11-09T22:29:53.060278Z" + "iopub.execute_input": "2026-11-09T23:51:11.128444Z", + "iopub.status.busy": "2026-11-09T23:51:11.127325Z", + "iopub.status.idle": "2026-11-09T23:51:16.485730Z", + "shell.execute_reply": "2026-11-09T23:51:16.484972Z" } }, "outputs": [ @@ -1299,7 +1299,8 @@ "the Swiss coordinates LV95 (`lv95_max_offset`): `nearest` and `temp` search their neighbors in ECEF\n", "coordinates. `linear` cannot triangulate points on a sphere in ECEF and raises an error with the generic\n", "path (`try_reg=False`), as shown below. On a regular grid like GFS, it uses the regular grid\n", - "interpolation instead (`try_reg=True`, the default), bilinear in latitude and longitude." + "interpolation instead (`try_reg=True`, the default), bilinear in latitude and longitude. For the\n", + "benchmark, `nearest` is also run through the regular grid interpolation (`try_reg=True`)." ] }, { @@ -1308,10 +1309,10 @@ "id": "ed552366", "metadata": { "execution": { - "iopub.execute_input": "2026-11-09T22:29:53.063206Z", - "iopub.status.busy": "2026-11-09T22:29:53.063007Z", - "iopub.status.idle": "2026-11-09T22:29:54.611154Z", - "shell.execute_reply": "2026-11-09T22:29:54.610760Z" + "iopub.execute_input": "2026-11-09T23:51:16.489021Z", + "iopub.status.busy": "2026-11-09T23:51:16.488762Z", + "iopub.status.idle": "2026-11-09T23:51:18.077515Z", + "shell.execute_reply": "2026-11-09T23:51:18.076199Z" } }, "outputs": [ @@ -1319,7 +1320,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "linear with try_reg=False: Method 'linear' is not supported for geographic grids beyond 700 km of the Swiss LV95 central northing, which are interpolated in earth-centered earth-fixed (ECEF) coordinates: a Delaunay triangulation of points close to the Earth's surface is degenerate in ECEF. Use e.g. method='idw' instead, or try_reg=True for a regular grid with 1D coordinates (or adjust GRID_PROCESS__lv95_max_offset).\n" + "linear with try_reg=False: Method 'linear' is not supported for geographic grids beyond 700 km of the Swiss LV95 central northing, which are interpolated in earth-centered earth-fixed (ECEF) coordinates: a Delaunay triangulation of points close to the Earth's surface is degenerate in ECEF. Use e.g. method='idw' instead, or try_reg=True for a regular grid with 1D coordinates (or adjust GRID_PROCESS__lv95_max_offset).\n", + "nearest, try_reg=True vs. False: 0 of 33 stations differ\n" ] }, { @@ -1874,7 +1876,7 @@ "Data variables:\n", " 2t (poi) float32 132B -7.609 2.084 -1.002 ... -20.62 -4.915 -5.307\n", "Attributes:\n", - " institution: ECMWF
  • institution :
    ECMWF
    • " ], "text/plain": [ " Size: 2kB\n", @@ -1920,6 +1922,13 @@ "except ValueError as error:\n", " print(f\"linear with try_reg=False: {error}\")\n", "\n", + "# nearest through the regular grid interpolation, for the benchmark\n", + "t0 = time.perf_counter()\n", + "nearest_reg = gri.interp2poi(ds, pois, \"nearest\", try_reg=True)\n", + "first_call[\"nearest (try_reg=True)\"] = time.perf_counter() - t0\n", + "n_diff = int((nearest_reg[\"2t\"] != results[\"nearest\"][\"2t\"]).sum())\n", + "print(f\"nearest, try_reg=True vs. False: {n_diff} of {len(pois)} stations differ\")\n", + "\n", "# model altitude at the nearest grid point, to compare with the station altitude\n", "alt = ds[\"surface_altitude\"].reset_coords(drop=True).to_dataset(name=\"alt\")\n", "pois[\"model altitude\"] = gri.interp2poi(alt, pois, \"nearest\", try_reg=False)[\"alt\"].to_series()\n", @@ -1945,10 +1954,10 @@ "id": "243afce5", "metadata": { "execution": { - "iopub.execute_input": "2026-11-09T22:29:54.614936Z", - "iopub.status.busy": "2026-11-09T22:29:54.614616Z", - "iopub.status.idle": "2026-11-09T22:29:55.721553Z", - "shell.execute_reply": "2026-11-09T22:29:55.720888Z" + "iopub.execute_input": "2026-11-09T23:51:18.079289Z", + "iopub.status.busy": "2026-11-09T23:51:18.079099Z", + "iopub.status.idle": "2026-11-09T23:51:18.633572Z", + "shell.execute_reply": "2026-11-09T23:51:18.632834Z" } }, "outputs": [ @@ -2002,10 +2011,10 @@ "id": "bcd071d0", "metadata": { "execution": { - "iopub.execute_input": "2026-11-09T22:29:55.728315Z", - "iopub.status.busy": "2026-11-09T22:29:55.728013Z", - "iopub.status.idle": "2026-11-09T22:29:55.747929Z", - "shell.execute_reply": "2026-11-09T22:29:55.747563Z" + "iopub.execute_input": "2026-11-09T23:51:18.639897Z", + "iopub.status.busy": "2026-11-09T23:51:18.639597Z", + "iopub.status.idle": "2026-11-09T23:51:18.667336Z", + "shell.execute_reply": "2026-11-09T23:51:18.666560Z" } }, "outputs": [ @@ -2481,16 +2490,16 @@ "id": "7c656fdd", "metadata": { "execution": { - "iopub.execute_input": "2026-11-09T22:29:55.751308Z", - "iopub.status.busy": "2026-11-09T22:29:55.750995Z", - "iopub.status.idle": "2026-11-09T22:29:55.997013Z", - "shell.execute_reply": "2026-11-09T22:29:55.996609Z" + "iopub.execute_input": "2026-11-09T23:51:18.669132Z", + "iopub.status.busy": "2026-11-09T23:51:18.668915Z", + "iopub.status.idle": "2026-11-09T23:51:18.932179Z", + "shell.execute_reply": "2026-11-09T23:51:18.931384Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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    " ] @@ -2507,7 +2516,7 @@ "for label in METHODS:\n", " ax.scatter(alt_diff, diff[label], color=COLORS[label], s=30, label=label, alpha=0.8)\n", "for name, x, y in zip(pois[\"name\"], alt_diff, diff[\"nearest\"]):\n", - " if abs(x) > 500:\n", + " if abs(x) > 300:\n", " ax.annotate(name, (x, y), textcoords=\"offset points\", xytext=(4, 4), fontsize=8)\n", "ax.axhline(0, color=\"0.5\", lw=0.8)\n", "ax.axvline(0, color=\"0.5\", lw=0.8)\n", @@ -2537,10 +2546,10 @@ "id": "16e34e9a", "metadata": { "execution": { - "iopub.execute_input": "2026-11-09T22:29:55.999868Z", - "iopub.status.busy": "2026-11-09T22:29:55.999571Z", - "iopub.status.idle": "2026-11-09T22:29:57.931957Z", - "shell.execute_reply": "2026-11-09T22:29:57.931070Z" + "iopub.execute_input": "2026-11-09T23:51:18.934452Z", + "iopub.status.busy": "2026-11-09T23:51:18.934262Z", + "iopub.status.idle": "2026-11-09T23:51:20.882612Z", + "shell.execute_reply": "2026-11-09T23:51:20.881835Z" } }, "outputs": [ @@ -2583,30 +2592,36 @@ " \n", " \n", " \n", - " nearest\n", - " 0.623\n", - " 0.167 ± 0.003\n", + " nearest (try_reg=False)\n", + " 0.627\n", + " 0.165 ± 0.005\n", + " \n", + " \n", + " nearest (try_reg=True)\n", + " 0.006\n", + " 0.004 ± 0.000\n", " \n", " \n", " linear\n", - " 0.017\n", - " 0.005 ± 0.000\n", + " 0.013\n", + " 0.004 ± 0.000\n", " \n", " \n", " temp\n", - " 0.628\n", - " 0.213 ± 0.003\n", + " 0.653\n", + " 0.215 ± 0.006\n", " \n", " \n", "\n", "" ], "text/plain": [ - " first call [s] subsequent calls [s]\n", - "method \n", - "nearest 0.623 0.167 ± 0.003\n", - "linear 0.017 0.005 ± 0.000\n", - "temp 0.628 0.213 ± 0.003" + " first call [s] subsequent calls [s]\n", + "method \n", + "nearest (try_reg=False) 0.627 0.165 ± 0.005\n", + "nearest (try_reg=True) 0.006 0.004 ± 0.000\n", + "linear 0.013 0.004 ± 0.000\n", + "temp 0.653 0.215 ± 0.006" ] }, "execution_count": 8, @@ -2635,7 +2650,9 @@ "\n", "\n", "print(f\"{ds.latitude.size} × {ds.longitude.size} grid points -> {len(pois)} stations\")\n", - "benchmark(gri.interp2poi, ds, pois, METHODS, first_call)" + "runs = {\"nearest\": METHODS[\"nearest\"], \"nearest (try_reg=True)\": {\"method\": \"nearest\", \"try_reg\": True}}\n", + "runs |= {label: kwargs for label, kwargs in METHODS.items() if label != \"nearest\"}\n", + "benchmark(gri.interp2poi, ds, pois, runs, first_call).rename(index={\"nearest\": \"nearest (try_reg=False)\"})" ] }, { @@ -2654,15 +2671,21 @@ " the model itself, not from the interpolation. Observations taken up to an hour from the valid time also\n", " add some differences.\n", "- Coastal stations are more mixed: the neighborhood of `temp` contains land and sea grid points. At Nadi it\n", - " removes most of the error of `nearest`, while at Dakar it is warmer than the observation.\n", + " removes most of the error of `nearest`, while at Dakar it is warmer than the observation. If needed, the\n", + " sea grid points can be masked, e.g. with `ds.where(land_fraction > 0.5)` and the GFS land-sea mask\n", + " (`LAND`): grid points that are NaN for all variables are ignored in the neighbor search.\n", "- The ECEF neighbor search handles the stations next to the antimeridian (Nadi, Fua'amotu, Pago Pago) and\n", " close to the poles like any other. At the South Pole, all neighbors are the same pole point repeated in the\n", " first grid row: `temp` cannot fit a sounding and falls back to a fixed lapse rate.\n", "- `linear` is not available in ECEF: on a regular latitude/longitude grid such as GFS it goes through the\n", " regular grid interpolation (`try_reg=True`, the default), which also handles the 0..360° longitudes of GFS\n", " and the stations across its 360° seam.\n", - "- The first call of `nearest` and `temp` includes converting the 1 million grid points to ECEF and building\n", - " the neighbor search, about 0.6 s. Subsequent calls take about 0.2 s, and `linear` a few milliseconds." + "- The first call of `nearest` (`try_reg=False`) and `temp` includes converting the 1 million grid points to\n", + " ECEF and building the neighbor search, about 0.6 s. Subsequent calls take about 0.2 s. On a regular grid\n", + " such as GFS, the regular grid interpolation (`try_reg=True`) is 40 to 100 times faster: `nearest` and\n", + " `linear` take a few milliseconds, and `nearest` picks the same grid point as in ECEF at all stations. The\n", + " ECEF neighbor search remains needed by the methods using a neighborhood, such as `temp`, and by\n", + " unstructured grids." ] } ], From 05b80cabe8b03f8e6ca0417de46c3deedc561eee Mon Sep 17 00:00:00 2001 From: Mathieu Schaer Date: Wed, 11 Nov 2026 00:03:57 +0100 Subject: [PATCH 06/10] Split the get_coords_name_crs tests per fixture - test_get_coords_name_crs and test_get_coords_name_crs_prefer_projected checked independent fixtures in one test, through a parametrization with repeated values. One test per fixture, assertions unchanged. Co-Authored-By: Claude Opus 5.5 --- test/test_data_model.py | 68 ++++++++++++++++++++++++----------------- 1 file changed, 40 insertions(+), 28 deletions(-) diff --git a/test/test_data_model.py b/test/test_data_model.py index 6197b13..212d6fb 100644 --- a/test/test_data_model.py +++ b/test/test_data_model.py @@ -392,14 +392,10 @@ def test_get_poi_alt(poi_def): dm.get_poi_alt(poi_broken) -@pytest.mark.parametrize( - "data_native_lv95_2dflat,data_native_rotlatlon_2dflat,data_cf_2dflat", - [("2d_grid", "2d_grid", "2d_grid"), ("flat_grid", "flat_grid", "flat_grid")], - indirect=True, -) -def test_get_coords_name_crs(data_native_lv95_2dflat, data_native_rotlatlon_2dflat, data_cf_2dflat): +@pytest.mark.parametrize("data_native_lv95_2dflat", ["2d_grid", "flat_grid"], indirect=True) +def test_get_coords_name_crs_lv95(data_native_lv95_2dflat): """ - Simple testcase for the get_coords_name_crs function + get_coords_name_crs with native LV95 coordinates: latitude/longitude when present, otherwise LV95 """ grid_y_x_crs = dm.get_coords_name_crs(data_native_lv95_2dflat) @@ -411,6 +407,22 @@ def test_get_coords_name_crs(data_native_lv95_2dflat, data_native_rotlatlon_2dfl grid_y_x_crs = dm.get_coords_name_crs(data_native_lv95_2dflat.drop_vars(("latitude", "longitude"))) assert grid_y_x_crs == ("y", "x", CRS.from_string("EPSG:2056")) + if "cell" in data_native_lv95_2dflat.dims: + pattern = "Could not find non-dimension spatial coordinates" + else: + pattern = "Missing spatial coordinates" + with pytest.raises(ValueError, match=pattern): + dm.get_coords_name_crs( + data_native_lv95_2dflat.drop_vars(("latitude", "longitude", "y", "x", "cell"), errors="ignore") + ) + + +@pytest.mark.parametrize("data_native_rotlatlon_2dflat", ["2d_grid", "flat_grid"], indirect=True) +def test_get_coords_name_crs_rotlatlon(data_native_rotlatlon_2dflat): + """ + get_coords_name_crs with native rotated pole coordinates: latitude/longitude when present, otherwise rotated pole + """ + spatial_dims = dm.get_spatial_dims(data_native_rotlatlon_2dflat) grid_y_x_crs = dm.get_coords_name_crs(data_native_rotlatlon_2dflat, spatial_dims) assert grid_y_x_crs == ("latitude", "longitude", CRS.from_string("EPSG:4326")) @@ -424,6 +436,13 @@ def test_get_coords_name_crs(data_native_lv95_2dflat, data_native_rotlatlon_2dfl assert grid_y_x_crs[:2] == ("y", "x") assert grid_y_x_crs[2].is_geographic + +@pytest.mark.parametrize("data_cf_2dflat", ["2d_grid", "flat_grid"], indirect=True) +def test_get_coords_name_crs_cf(data_cf_2dflat): + """ + get_coords_name_crs with a CF grid mapping: latitude/longitude coordinates + """ + grid_y_x_crs = dm.get_coords_name_crs(data_cf_2dflat) if "points" in data_cf_2dflat.dims: lat, lon = "lat", "lon" @@ -434,27 +453,13 @@ def test_get_coords_name_crs(data_native_lv95_2dflat, data_native_rotlatlon_2dfl da = data_cf_2dflat[list(data_cf_2dflat)[0]] assert grid_y_x_crs == dm.get_coords_name_crs(da) - if "cell" in data_native_lv95_2dflat.dims: - pattern = "Could not find non-dimension spatial coordinates" - else: - pattern = "Missing spatial coordinates" - with pytest.raises(ValueError, match=pattern): - dm.get_coords_name_crs( - data_native_lv95_2dflat.drop_vars(("latitude", "longitude", "y", "x", "cell"), errors="ignore") - ) - -@pytest.mark.parametrize( - "data_native_lv95_2dflat,data_native_rotlatlon_2dflat", - [("2d_grid", "2d_grid"), ("flat_grid", "flat_grid")], - indirect=True, -) -def test_get_coords_name_crs_prefer_projected(data_native_lv95_2dflat, data_native_rotlatlon_2dflat): +@pytest.mark.parametrize("data_native_lv95_2dflat", ["2d_grid", "flat_grid"], indirect=True) +def test_get_coords_name_crs_prefer_projected_lv95(data_native_lv95_2dflat): """ - get_coords_name_crs with prefer_projected=True returns native coordinates in meters before latitude/longitude + get_coords_name_crs with prefer_projected=True and native LV95 coordinates: LV95 before latitude/longitude """ - # native LV95 with latitude/longitude: LV95 lv95 = dm.get_coords_name_crs(data_native_lv95_2dflat, prefer_projected=True) assert lv95 == ("y", "x", CRS.from_string("EPSG:2056")) da = data_native_lv95_2dflat[list(data_native_lv95_2dflat)[0]] @@ -465,16 +470,23 @@ def test_get_coords_name_crs_prefer_projected(data_native_lv95_2dflat, data_nati # default unchanged: latitude/longitude assert dm.get_coords_name_crs(data_native_lv95_2dflat)[2] == CRS.from_string("EPSG:4326") - # native rotated pole (in degrees) with latitude/longitude: latitude/longitude - rotated = dm.get_coords_name_crs(data_native_rotlatlon_2dflat, prefer_projected=True) - assert rotated == ("latitude", "longitude", CRS.from_string("EPSG:4326")) - # no CRS attribute: only latitude/longitude can be used no_crs = data_native_lv95_2dflat.copy() no_crs.attrs.pop("grid_mapping") assert dm.get_coords_name_crs(no_crs, prefer_projected=True) == ("latitude", "longitude", CRS.from_epsg(4326)) +@pytest.mark.parametrize("data_native_rotlatlon_2dflat", ["2d_grid", "flat_grid"], indirect=True) +def test_get_coords_name_crs_prefer_projected_rotlatlon(data_native_rotlatlon_2dflat): + """ + get_coords_name_crs with prefer_projected=True and native rotated pole coordinates (in degrees): + latitude/longitude + """ + + rotated = dm.get_coords_name_crs(data_native_rotlatlon_2dflat, prefer_projected=True) + assert rotated == ("latitude", "longitude", CRS.from_string("EPSG:4326")) + + def test_parse_grid_mapping(monkeypatch): """ CF latitude/longitude and rotated pole mappings on a sphere are built directly on WGS84 (fast, without From e0c45182232ad34d684186ee73595e010be2763a Mon Sep 17 00:00:00 2001 From: Mathieu Schaer Date: Wed, 11 Nov 2026 00:06:24 +0100 Subject: [PATCH 07/10] Drop unneeded float64 cast in create_dst_grid_from_src - pyproj already converts float32 and integer coordinates to float64, so the cast only copied the full 2D source coordinates before outer_ring. The destination grids are unchanged. Co-Authored-By: Claude Opus 5.5 --- grid_process/grid_interp_utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/grid_process/grid_interp_utils.py b/grid_process/grid_interp_utils.py index 5c3f5d9..a81a0cc 100755 --- a/grid_process/grid_interp_utils.py +++ b/grid_process/grid_interp_utils.py @@ -169,8 +169,8 @@ def create_dst_grid_from_src( # pylint: disable=too-many-locals # bounding box of the source domain in the destination CRS: of the outer ring of a structured grid (which # encloses the reprojected grid), of all points of an unstructured one - src_x_coords = src_grid[src_x].values.astype(np.float64) - src_y_coords = src_grid[src_y].values.astype(np.float64) + src_x_coords = src_grid[src_x].values + src_y_coords = src_grid[src_y].values if len(spatial_dims) == 2: src_x_coords, src_y_coords = outer_ring(src_x_coords, src_y_coords) x_src_dst, y_src_dst = reproject(src_x_coords, src_y_coords, src_crs, dst_crs) From 6288db0525cfa07f04373648f14689af1a071df9 Mon Sep 17 00:00:00 2001 From: Mathieu Schaer Date: Wed, 11 Nov 2026 18:08:33 +0100 Subject: [PATCH 08/10] Fix thresholds and z_fact default in the interp_temp docstring - The fixgrad fallback applies to POIs more than 200 m above the highest or 600 m below the lowest neighbor (the docstring said 300 m and 500 m). - The z_fact default is 25 (the docstring said 50). Co-Authored-By: Claude Opus 5.5 --- grid_process/grid_interp.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/grid_process/grid_interp.py b/grid_process/grid_interp.py index e6b5ed5..eddb4c6 100644 --- a/grid_process/grid_interp.py +++ b/grid_process/grid_interp.py @@ -105,7 +105,7 @@ def interp_temp( # pylint: disable=too-many-locals z_fact : positive int, optional factor to multiply the z coordinates to find nearest neighbors based on a non-euclidean distance that penalizes more altitude differences. Default - value is 50. A value of 1 will use regular euclidean distances. + value is 25. A value of 1 will use regular euclidean distances. k : positive int, optional number of considered nearest neighbors for the local sounding. Default is 36 @@ -139,8 +139,8 @@ def interp_temp( # pylint: disable=too-many-locals Note ---- - For POIs with altitude higher than 300m above the highest gridpoint or lower - than 500m below the lowest gridpoint in the local neighborhood used for the fit, + For POIs with altitude higher than 200m above the highest gridpoint or lower + than 600m below the lowest gridpoint in the local neighborhood used for the fit, or whose neighbors all have the same altitude (no sounding can be fitted, e.g. at the poles of a latitude/longitude grid or on flat orography), the fixgrad method is used with default gradient of 0.006 [K / m]. From 07ca7eaf849c86fd5a3f9ba60b66b4b8a78af988 Mon Sep 17 00:00:00 2001 From: Mathieu Schaer Date: Wed, 11 Nov 2026 18:22:39 +0100 Subject: [PATCH 09/10] Detect meter axes of projected CRSs by their unit conversion factor - is_metric_crs compared the axis unit names to "metre"/"meter". PROJ normalizes known units to "metre", but keeps custom names as written (e.g. UNIT["m",1] or UNIT["Meter",1] in a WKT without authority), which were missed. The conversion factor to the meter is now compared, so km, feet and the German legal metre are still excluded. - test_is_metric_crs is parametrized, with the custom WKT units, km and the German legal metre added. Co-Authored-By: Claude Opus 5.5 --- grid_process/data_model.py | 6 ++- test/test_data_model.py | 86 +++++++++++++++++++++++++++++--------- 2 files changed, 71 insertions(+), 21 deletions(-) diff --git a/grid_process/data_model.py b/grid_process/data_model.py index ba40b05..ad63139 100644 --- a/grid_process/data_model.py +++ b/grid_process/data_model.py @@ -531,10 +531,12 @@ def _parse_grid_mapping_dict(grid_mapping: dict[str, Any]) -> CRS: def is_metric_crs(crs: CRS) -> bool: """ Whether ``crs`` is a projected CRS with all axes in meters (e.g. LV95, LV03 or UTM), i.e. in which euclidean - distances can be computed directly from the coordinates. + distances can be computed directly from the coordinates. The axis units are compared by their conversion factor + to the meter, so that units written with another name (e.g. "m" or "Meter" in a WKT without authority) are + recognized, while kilometers or feet are not. """ - return bool(crs.is_projected) and all(axis.unit_name in ("metre", "meter") for axis in crs.axis_info) + return bool(crs.is_projected) and all(axis.unit_conversion_factor == 1.0 for axis in crs.axis_info) def get_coords_name_crs( diff --git a/test/test_data_model.py b/test/test_data_model.py index 212d6fb..4ef3aca 100644 --- a/test/test_data_model.py +++ b/test/test_data_model.py @@ -487,6 +487,73 @@ def test_get_coords_name_crs_prefer_projected_rotlatlon(data_native_rotlatlon_2d assert rotated == ("latitude", "longitude", CRS.from_string("EPSG:4326")) +LV95_WKT1 = CRS.from_epsg(2056).to_wkt("WKT1_GDAL") +LV95_WKT1_UNIT = 'UNIT["metre",1,AUTHORITY["EPSG","9001"]]' + + +@pytest.mark.parametrize( + "crs,expected", + [ + ("EPSG:2056", True), # LV95 + ("EPSG:21781", True), # LV03 + ("EPSG:32632", True), # UTM 32N + ("EPSG:3035", True), # LAEA Europe + ( + CRS.from_cf( + { + "grid_mapping_name": "lambert_conformal_conic", + "standard_parallel": [45.0, 50.0], + "longitude_of_central_meridian": 10.0, + "latitude_of_projection_origin": 47.0, + "earth_radius": 6371229.0, + } + ), + True, + ), + # hand-written WKT with a unit without authority: the unit name is kept as written + (LV95_WKT1.replace(LV95_WKT1_UNIT, 'UNIT["m",1]'), True), + (LV95_WKT1.replace(LV95_WKT1_UNIT, 'UNIT["Meter",1]'), True), + ("EPSG:4326", False), + ( + CRS.from_cf( + { + "grid_mapping_name": "rotated_latitude_longitude", + "grid_north_pole_latitude": 43.0, + "grid_north_pole_longitude": -170.0, + "earth_radius": 6371229.0, + } + ), + False, + ), + ("EPSG:4978", False), # ECEF + ("+proj=stere +lat_0=90 +lon_0=0 +units=km", False), + ("EPSG:2263", False), # US survey foot + ("EPSG:29371", False), # German legal metre + ], + ids=[ + "lv95", + "lv03", + "utm32n", + "laea_europe", + "cf_lambert", + "wkt_unit_m", + "wkt_unit_Meter", + "latlon", + "cf_rotated_pole", + "ecef", + "km", + "us_survey_foot", + "german_legal_metre", + ], +) +def test_is_metric_crs(crs, expected): + """ + is_metric_crs: projected CRS with axes in meters, whatever the unit name + """ + + assert dm.is_metric_crs(CRS.from_user_input(crs)) is expected + + def test_parse_grid_mapping(monkeypatch): """ CF latitude/longitude and rotated pole mappings on a sphere are built directly on WGS84 (fast, without @@ -531,25 +598,6 @@ def fail(*args, **kwargs): assert dm.parse_grid_mapping(json.dumps(lambert)) is dm.parse_grid_mapping(json.dumps(lambert)) -def test_is_metric_crs(): - """ - Simple testcase for is_metric_crs - """ - - rotated = CRS.from_cf( - { - "grid_mapping_name": "rotated_latitude_longitude", - "grid_north_pole_latitude": 43.0, - "grid_north_pole_longitude": -170.0, - "earth_radius": 6371229.0, - } - ) - for epsg in (2056, 21781, 32632, 3035): # LV95, LV03, UTM 32N, LAEA Europe - assert dm.is_metric_crs(CRS.from_epsg(epsg)) - for crs in (CRS.from_epsg(4326), CRS.from_epsg(4978), CRS.from_epsg(2263), rotated): # lat/lon, ECEF, US feet - assert not dm.is_metric_crs(crs) - - def test_config_extended(): """ Simple testcase to make sure that extending the data model by exporting From c6dfed56ca105674d3d42338b37434ee6be78db3 Mon Sep 17 00:00:00 2001 From: Mathieu Schaer Date: Wed, 11 Nov 2026 18:37:26 +0100 Subject: [PATCH 10/10] Note in create_dst_grid_from_src that a global source needs a geographic CRS - A projected CRS (e.g. LV95 or UTM) cannot represent the whole globe, so the destination grid of a global source would be meaningless. Co-Authored-By: Claude Opus 5.5 --- grid_process/grid_interp_utils.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/grid_process/grid_interp_utils.py b/grid_process/grid_interp_utils.py index a81a0cc..c56b733 100755 --- a/grid_process/grid_interp_utils.py +++ b/grid_process/grid_interp_utils.py @@ -148,7 +148,9 @@ def create_dst_grid_from_src( # pylint: disable=too-many-locals dst_grid_crs : string, int, dict or a CRS object Anything accepted by pyproj.crs.CRS.from_user_input: https://pyproj4.github.io/pyproj/dev/api/crs/crs.html#pyproj.crs.CRS.from_user_input - (e.g. EPSG:4326) + (e.g. EPSG:4326). The source domain must be representable in this CRS: a global source needs a + geographic destination CRS (e.g. EPSG:4326), since a projected CRS (e.g. LV95 or UTM) cannot represent + the whole globe. add_latlon : bool If True, add latitude and longitude 2d arrays as coordinates