wradlib Documentation - Bitbucket

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wradlib Documentation, Release 0.1.1 nearest class wich underscores 1% of highest frequency class and cuts all values beyond these classes by setting them Nan. As the result you see an histogram with a more narrow span of precipitation amounts and an updated clutter mask. This procedure is repeated iteratively until, the changes fall below a threshold value. The result are the final histogram and cluttermask: Now the clutter bins should be substituted by some meaningful values derived by nearest neighbour or linear interpolation: # set the location of your data datadir = ’D:/THIS/IS/MY/DATA/DIRECTORY/’ # get the radar data which should be clutter corrected data, attrs = wrl.io.read_EDGE_netcdf(datadir + ’SUB-20110927-050748-01-Z.nc’, range_lim = 100000) 32 Chapter 2. Tutorials
wradlib Documentation, Release 0.1.1 # define clutter-indices on the basis of the clutter-mask clutter_indices = np.where(cluttermask.ravel()) # compute bin centroid coordinates in cartesian map projection r = attrs[’r’] / 1000. az = attrs[’az’] sitecoords = attrs[’sitecoords’][0:-1] projstr = ’+proj=utm +zone=51 +ellps=WGS84’ binx, biny = wrl.georef.projected_bincoords_from_radarspecs(r, az, sitecoords, projstr) binx = binx.ravel() biny = biny.ravel() # coordinate pairs without clutter good_coordinates_list = np.array([(np.delete(binx,clutter_indices)), (np.delete(biny,clutter_indices) # list of the entire coordinate pairs from radarcircle trgcoord = np.array([binx, biny]).transpose() # building object class for nearest neighbour interpolation either for directly requested interpolati # by nearest neighbours or for filling the boundary gap caused by linear interpolation intpol_nearest = wrl.ipol.Nearest(good_coordinates_list, trgcoord) # building object class for linear interpolation intpol_linear = wrl.ipol.Linear(good_coordinates_list, trgcoord) # substitute clutter by interpolation methods # Conversion from dBZ to rainintensity rain_intensity = wrl.zr.z2r(wrl.trafo.idecibel(data)).ravel() # Substitute Nan rain_intensity = np.where(np.isnan(rain_intensity), 0, rain_intensity ) # rain intensity data without clutter values_list = np.delete(rain_intensity,clutter_indices) # spatial interpolation of rain intensity for the whole radar scan based on the data without clutter filled_R = intpol_nearest(values_list).reshape(len(attrs[’az’]), len(attrs[’r’])) # calculating nearest neighbour interpolation if requested and filling possible boundary gaps filled_R_linear = intpol_linear(values_list).reshape(len(attrs[’az’]),len(attrs[’r’])) filled_R = np.where(np.isnan(filled_R_linear), filled_R, filled_R_linear) rain_intensity = rain_intensity.reshape(len(attrs[’az’]), len(attrs[’r’])) wrl.vis.polar_plot(rain_intensity, title = ’Rain intensity’, unit = ’mm/h’, R = attrs[’max_range’] / 1000., colormap = ’spectral’, vmax = 140.) wrl.vis.polar_plot(filled_R, title = ’Clutter corrected rain intensity’, unit = ’mm/h’, R = attrs[’max_range’] / 1000., colormap = ’spectral’, vmax = 140.) As the result you will see the original rain intensity (left) and the image with clutter correction (right) .. image:: images/cluttercorrection.gif 2.7 Attenuation correction ... to be continued ... 2.7. Attenuation correction 33
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