Use wrf-python, xarray, and pyproj to post-process WRF output

wrf_python_extraction.py

from wrf import getvar
from netCDF4 import Dataset
import xarray as xr
import pyproj

# Extract the variables of interest at time index 17
ds = Dataset('../wrfout_d02_2015-07-12_1200.nc')
variables = [getvar(ds, var, 17) for var in ('z', 'dbz', 'pressure', 'ter', 'ua',
                                             'va', 'wa', 'temp', 'rh')]
data = xr.merge(variables)

# Define the WRF projection (see https://fabienmaussion.info/2018/01/06/wrf-projection/)
wrf_proj = pyproj.Proj(proj='lcc',
                       lat_1=ds.TRUELAT1, lat_2=ds.TRUELAT2,
                       lat_0=ds.MOAD_CEN_LAT, lon_0=ds.STAND_LON,
                       a=6370000, b=6370000)

# Easting and Northing of the domain center point
wgs_proj = pyproj.Proj(proj='latlong', datum='WGS84')
e, n = pyproj.transform(wgs_proj, wrf_proj, ds.CEN_LON, ds.CEN_LAT)

# Grid parameters
dx, dy = ds.DX, ds.DY
nx, ny = ds.dimensions['west_east'].size, ds.dimensions['south_north'].size

# Lower left corner of the domain
x0 = -(nx-1) / 2. * dx + e
y0 = -(ny-1) / 2. * dy + n

# Get grid values
x, y = np.arange(nx) * dx + x0, np.arange(ny) * dy + y0

# Add in dimension coordinates
eta_attrs = {attr: ds['ZNU'].getncattr(attr) for attr in ds['ZNU'].ncattrs()}
eta_attrs['axis'] = 'Z'
data['bottom_top'] = xr.DataArray(ds['ZNU'][17], dims='bottom_top', attrs=eta_attrs)
data['south_north'] = xr.DataArray(y, dims='south_north', attrs={'axis': 'Y', 'units': 'm'})
data['west_east'] = xr.DataArray(x, dims='west_east', attrs={'axis': 'X', 'units': 'm'})

# Define the grid_mapping
data['LambertConformal'] = xr.DataArray(np.array(0), attrs={
    'grid_mapping_name': 'lambert_conformal_conic',
    'earth_radius': 6370000,
    'standard_parallel': (ds.TRUELAT1, ds.TRUELAT2),
    'longitude_of_central_meridian': ds.STAND_LON,
    'latitude_of_projection_origin': ds.MOAD_CEN_LAT
})
for var in data.data_vars:
    data[var].attrs['grid_mapping'] = 'LambertConformal'
    if 'projection' in data[var].attrs:
        del data[var].attrs['projection']
    if 'coordinates' in data[var].attrs:
        del data[var].attrs['coordinates']
        
# Save result
data.to_netcdf('wrfout_d02_2015-07-12_1200_processed.nc')

is it possible to extract all time steps in the same command

@fipoucat Yes, you should be able to alter the getvar call on line 8 to use ALL_TIMES (when imported from wrf-python with from wrf import ALL_TIMES). Just note that wrf-python tends to load all data into memory (at least when I've last used it), so this could easily become slow or even fill your available RAM (giving a MemoryError).

Ok, thank you.

I just tried to do your python script. There was the next error.

TypeError: Invalid value for attr 'projection': LambertConformal(stand_lon=137.0, moad_cen_lat=38.0000114440918, truelat1=30.0, truelat2=60.0, pole_lat=90.0, pole_lon=0.0). For serialization to netCDF files, its value must be of one of the following types: str, Number, ndarray, number, list, tuple

It seems that 'coordinates' and 'projection' in global attributes has not been deleted.