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02_map_products_precipitation.py
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02_map_products_precipitation.py
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#!/usr/bin/env python
import h5py
import numpy as np
import warnings
from constants import *
from plot_utils import plot_rr
# read the TB data
gmi_precipitation_file_path = f'{DATA_DIR}/{DATA_FILENAME_PRECIPITATION}'
# open the data file
# todo: apply a filter to only keep data with the lat and lon values that are within the area interest
# don't do it for this first experiment because we want to play with different areas of interest
hf = h5py.File(gmi_precipitation_file_path,'r')
# fetch TB data from Swath S1
lat_S1 = hf['S1/Latitude'][:]
lon_S1 = hf['S1/Longitude'][:]
# fetch surface precipitation
# use the "rr" variable to denote "rainfall rate"
rr = hf['S1/surfacePrecipitation'][:]
rr[rr < 0] = np.NaN # NaN the -9999.9 missing values
# plot the data on a map
plot_rr(
RR = rr,
lat = lat_S1,
lon = lon_S1,
lat_bounds = LAT_BOUNDS_IONIAN_SEA,
lon_bounds = LON_BOUNDS_IONIAN_SEA,
colorAxisMin = 0,
colorAxisMax = 40,
title = "Estimated Rainfall Rate",
#show = False,
#filepath = "figures/fig5_aoi_sea_rr.png"
)