I am trying to access the gridded satellite swath data which is available in hdf from https://disc.gsfc.nasa.gov/datasets?keywords=LATENT%20HEATING&page=1 You can see any level 3 data which is what I am trying to use. I am familiar with netcdf visualization libraries such as ferret and netcdf operating softwares such as cdo and nco. I want to convert this swath gridded data to netcdf for smooth analysis using cdo and nco.
I downloaded the file 3B-ORBIT.GPM.DPRGMI.3GCSHv6-0.20190626-S064516-E081748.030252.V06A.HDF5 from the link you have shared and have created a program which takes the file name as argument and generates a netcdf file with the variables latentHeating, surfacePrecipRate and stratiformFraction. Let me clarify that this file was a gridded satellite swath data. You may need to do something else to make a gridded data out of the pixel wise swath data
import h5py
import numpy as np
import datetime as dt
import xarray as xr
import sys
input_file = sys.argv[1]
f = h5py.File(input_file, 'r')
yy = np.max(f['Grid']['GridTime']['Year'][:])
mm = np.max(f['Grid']['GridTime']['Month'][:])
dd = np.max(f['Grid']['GridTime']['DayOfMonth'][:])
time_ = dt.datetime(yy, mm, dd)
lat = np.linspace(-67+0.25/2,67-0.25/2,536)
lon = np.linspace(-180+0.25/2,180-0.25/2,1440)
lev = np.arange(0.125,0.125+0.25*80,0.25)
lh = f['Grid']['latentHeating'][:]
spr = f['Grid']['surfacePrecipRate'][:]
stratfrac = f['Grid']['stratiformFraction'][:]
lh[lh==-9999.9] = np.nan
spr[spr==-9999.9] = np.nan
stratfrac[stratfrac==-9999.9] = np.nan
stratfrac = stratfrac.T
spr = spr.T
lh = lh.swapaxes(1,2)
lh = lh[np.newaxis,...]
stratfrac = stratfrac[np.newaxis,...]
spr = spr[np.newaxis,...]
df = xr.Dataset(
data_vars={'latentHeating': (('time', 'level', 'latitude',
'longitude' ), lh),
'surfacePrecipRate': (('time', 'latitude',
'longitude' ), spr),
'stratiformFraction': (('time', 'latitude',
'longitude'), stratfrac)},
coords={ 'time':np.atleast_1d((time_-
dt.datetime(1,1,1)).days+2),
'level': lev,
'longitude': lon,
'latitude': lat,
})
df.latentHeating.attrs['long_name'] = 'Latent Heating'
df.surfacePrecipRate.attrs['long_name'] = 'Surface Precipitation Rate'
df.stratiformFraction.attrs['long_name'] = 'Stratiform Fraction'
df.time.attrs['units'] = 'days since 01-01-01 00:00:00'
df.time.attrs['standard_name'] = 'time'
df.time.attrs['long_name'] = 'Year AD'
df.time.attrs['calendar'] = 'standard'
df.longitude.attrs['standard_name'] = 'longitude'
df.longitude.attrs['long_name'] = 'longitude'
df.longitude.attrs['units'] = 'degrees_east'
df.longitude.attrs['axis'] = 'X'
df.latitude.attrs['standard_name'] = 'latitude'
df.latitude.attrs['long_name'] = 'latitude'
df.latitude.attrs['units'] = 'degrees_north'
df.latitude.attrs['axis'] = 'Y'
df.level.attrs['standard_name'] = 'air_pressure'
df.level.attrs['long_name'] = 'pressure_level'
df.level.attrs['units'] = 'km'
df.level.attrs['axis'] = 'Z'
df.level.attrs['positive'] = 'up'
df.to_netcdf(input_file[:-4]+'nc')
You can just save this as a python file and pass the hdf file as an argument.