PyBathSpectra/postprocessing_config_HailExample1.yaml at main · birdbathDWD/PyBathSpectra · GitHub

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## Settings for spectral postprocessing of DWD birdbath scans (2024)
# to start postprocessing with these settings, run: python3 postprocessing.py


##### Input file(s)

# Selected time of birdbath scan (= timestamp in filename)
# Format: 'yyyy-mm-dd HH:MM:SS'
birdbath_time:
- '2021-04-30 15:09:41'
#- '2021-06-29 14:06:20'
#- '2020-01-18 00:25:02'
#- '2023-06-22 14:00:00'

# Data directory
# Relative path from where postprocessing.py is executed
data_dir: './input/'

# Filename pattern for DWD birdbath scan Doppler spectra and moments
birdbath_pattern:
  # For research radar MHP
  birdbath_spectra: 'dft-Sc+90Grad-STAR-NG-10+MHP_10962_%Y%m%d%H%M%S_00.00.h5'
  birdbath_moments: 'Sc+90Grad-STAR-NG-10+MHP_10962_%Y%m%d%H%M%S_00.h5'
  # # Alternative pattern for research radar MHP
  # birdbath_spectra: 'dft-Sc+M1ds-90Grad-STAR-NG-02+MHP_10962_%Y%m%d%H%M%S_00.00.h5'
  # birdbath_moments: 'Sc+M1ds-90Grad-STAR-NG-02+MHP_10962_%Y%m%d%H%M%S_00.h5'
  # # For operational radar FLD (ras13 instead of ras11 starting from summer 2023???)
  # birdbath_spectra: 'ras20-90gradstarng10dft_sweepdft_dfthv_00-%Y%m%d%H%M%S-REPLdefld-10440-hd5'
  # birdbath_moments: 'ras13-90gradstarng10_sweeph5allm_any_00-%Y%m%d%H%M%S-REPLdefld-10440-hd5'


##### Thresholding

# Thresholding method for separating weather signal from clutter and background
# 'adaptive' thresholding by hdbscan clustering, 'fixed' preset thresholds,
# or 'none_expanded' for no thresholding but expands Doppler velocity range
# for manual dealiasing later on (then use mask below)
thresholding: 'none_expanded'

# Threshold values for 'fixed' threholding method
# (not used for 'adaptive' or 'none_expanded')
fixed_thresholds:
  # Absolute difference in Doppler power between H and V channel [dB]
  power_difference: 1.256  # 1.4
  # Variability of power_difference per Doppler velocity and range bin [dB]
  power_variability: 1.169  # 1.4
  # Lowest maximum power value for (contiguous) weather signal [dB]
  power_minimum: 58.76  # 60.0

# Relevant for 'none_expanded' only: expand and cut Doppler spectra
# (e.g. for manual dealiasing in strong convection, i.e. hail)
# Parameters for cutting/masking spurious data in expanded Doppler spectra
# have to be determined by trial and error
mask:
  # Mask 'h(v)' is cut along the lines of height = a * velocity + b;
  # 'v(h)' is cut along lines of velocity = a * height + b
  mask_flavor: 'h(v)'
  # Parameters [a, b] for masked areas below and above ('h(v)')
  # or to right and left ('v(h)') of reasonable weather signal
  mask_up: [0.38, 0.5]
  mask_down: [0.38, 11]


##### Peak finding

# Find peak intervals for Doppler spectra on linear or dB scale:
# 'linear' or 'dBlin'
# best to use 'dBlin': find peaks on dB scale but still calculate
# Doppler moments on linear power scale
power_mode: 'dBlin'

# Method for finding peaks in Doppler spectra at every height level
# use 'adaptive' for UniDip clustering (only useful for narrow spectra
# i.e. in snow and not in intense (convective) rain or even hail) or
# use 'fixed' threshold value for defining required peak prominence (robust)
peak_finder: 'fixed'

# Peak prominence [dB] for identifying multiple peaks in Doppler spectra
# only relevant for 'fixed' peak_finder with
# reasonable values of 6 to 8 for hail, and generally 8
peak_to_trough: 8


##### Plotting and outputs

# Plot radar data and postprocessing results?
plot_all: True

# Save postprocessing results as .txt files?
save_results: True

# Directory where to save output plots
# Relative path from where postprocessing.py is executed
plot_dir: './output/plots/'

# Directory where to save results as .txt files
# Relative path from where postprocessing.py is executed
results_dir: './output/results/'

# Scaling factors for Pseudo-RGB plots of birdbath scan data
# R: h-pol power, G: h-v power difference, B: h-v variability
rgb_scaling: [1, 1.8, 1.8]

# Expected maximum number of modes in birdbath Doppler spectra
# Generally a value of 10 is high enough, or 20 in convection
# if more spectral modes are seemingly found, the analysis results are
# super noisy or analysis can crash altogether
maximum_modes: 20

# Also provide simple uncertainty estimates of results?
# Difference between postprocessing for smoothed and unsmoothed spectra
estimate_uncertainty: True

# Also give full spectrally resolved reflectivities as output?
# results in large .txt files; not used for this publication
reflectivity_spectra: False


##### Miscellaneous

# Lowest valid range bin, i.e. minimum height [m] above radar, for analysis
# (i.e. start of radar far field, depending on analysis, radar, and weather)
# good values: generally 625 m; for hail often already 425 m
analysis_minheight: 425

# Maximum height for analysis [m];
# saves time if only shallow region is of interest
analysis_maxheight: 9000

# Use interpolated spectra or simply masked mean of measured spectra
# as output for isolated spectra
# if set to 'True': can recover weather signal across clutter region,
# but can also introduce grainy artifacts
# Not relevant for 'thresholding' of 'none_expanded'.
interpolate_isolated: True

# Minimum pixel area for individual region(s) of interest [px]
# Null (-> Python NoneType) = largest only; everything else not used, so far
dfth_minarea: Null

# Number of (height, velocity) bins for structuring element
# used for spectral filtering and interpolation
structuring_element: [3, 7]

# Reflectivity scale
# Use either 'Zh' (mostly) or 'UZh' (hail) to transform power to reflectivity
# UZh has fewer missing values (Zh is more thresholded in signal processor)
reflectivity_scale: 'UZh'