Plot CAPPI for a single radar with nearest gate / Or Plot PPI with constrained range

[Chou Tat Hoi]

Dear all,

I try to plot 3km CAPPI rainfall(base on dBZ) with some non overlap data (different elevation angles). How ever the result looks have a strong smooth compared with Sigmet iris system (code shows at below). And it also take a long time to plot it. If I have any mistake in grid it ? And because those are non overlap data, is there any way to plot CAPPI with nearest gate? (Without interpolation will become faster?) If not, is there any way to plot PPI with constrained range? So that I could plot different sweep with different range and pile them up as a filled CAPP. Thank you. 

min_z = 2800

max_z = 3200

def retrieve_filter_radar(filenameA, filenameB, filenameC, min_z, max_z):

radarA = pyart.io.read_sigmet(filenameA)

radarB = pyart.io.read_sigmet(filenameB)

radarC = pyart.io.read_sigmet(filenameC)

grid_filtered = pyart.map.grid_from_radars(

(radarA, radarB, radarC),

grid_shape=(1, 2500, 2500),

grid_limits=((min_z, max_z), (-260000.0, 260000.0), (-260000.0, 260000.0)),

#weighting_function='Cressman',

fields=['reflectivity'],

gridding_algo="map_gates_to_grid",

weighting_function='BARNES',

refl_filter_flag=True,   max_refl=75)

grid_filtered.fields['reflectivity_masked'] = {

'data': np.ma.masked_less(grid_filtered.fields['reflectivity']['data'], 0)}

raw_data = grid_filtered.fields['reflectivity_masked']['data'][:]

rain_rate = (10.0**(raw_data/10.0)/200.0)**(5.0/8.0)

        rain_rate_dic = {

            'data': rain_rate,

            'standard_name': 'rain_rate',

            'long_name': 'Rain rate',

            'units': 'mm',    # or whatever the correct units are for this retrieval

        }

        #insert rain_rate into grid

grid_filtered.add_field('rain_rate',rain_rate_dic)

grid_filtered.fields['rain_rate_masked'] = {

'data': np.ma.masked_less(grid_filtered.fields['rain_rate']['data'], 0.1)}

return grid_filtered

[Chou Tat Hoi]

After modified the interpolation, the smooth effect become lower. And dis_beam looks like is the best roi function in our cast, but I don't understand those parameters. Temporary using dist instead of it.

def retrieve_filter_radar(filenameA, filenameB, filenameC, min_z, max_z):

radarA = pyart.io.read_sigmet(filenameA)

radarB = pyart.io.read_sigmet(filenameB)

radarC = pyart.io.read_sigmet(filenameC)

grid_filtered = pyart.map.grid_from_radars(

(radarA, radarB, radarC),

grid_shape=(1, 500, 500),

grid_limits=((min_z, max_z), (-260000.0, 260000.0), (-260000.0, 260000.0)),

#weighting_function='Cressman',

fields=['reflectivity'],

gridding_algo="map_gates_to_grid",

weighting_function='BARNES',

#weighting_function='Cressman',

#roi_func='constant', constant_roi=4540.0,

roi_func='dist', constant_roi=250.0, z_factor=0.00776, xy_factor=0.00776,

#roi_func='dist', constant_roi=250.0, z_factor=0.0082, xy_factor=0.0082,

refl_filter_flag=True,   max_refl=75)

grid_filtered.fields['reflectivity_masked'] = {

'data': np.ma.masked_less(grid_filtered.fields['reflectivity']['data'], 7)}

raw_data = grid_filtered.fields['reflectivity_masked']['data'][:]

rain_rate = (10.0**(raw_data/10.0)/200.0)**(5.0/8.0)

        rain_rate_dic = {

            'data': rain_rate,

            'standard_name': 'rain_rate',

            'long_name': 'Rain rate',

            'units': 'mm',    # or whatever the correct units are for this retrieval

        }

        #insert rain_rate into grid

grid_filtered.add_field('rain_rate',rain_rate_dic)

grid_filtered.fields['rain_rate_masked'] = {

'data': np.ma.masked_less(grid_filtered.fields['rain_rate']['data'], 0.15)}

return grid_filtered

  

[Jonathan Helmus]

The gridding algorithm in Py-ART searches for all radar gates within a given radius of influence and interpolates the grid point by weighting all values within this radius according to their distance from the grid pixel center.  Various parameters influence how the radius of influence is determined as outlines in the map_to_grid documentation [1].  These parameters can be a bit confusing to understand and adjust.  It is possible to visualize the ROI by setting the map_roi parameter to True and then plotting the 'ROI' field which can be helpful for finding good values for the ROI values.

    Gridding is currently the only method for creating a CAPPI in Py-ART.  Unfortunately creating this grid and plotting it on a geographic map can take some time.  Py-ART does not yet have the ability to do a nearest neighbor mapping although such a feature would be a welcome addition to the package.  Using a set of  gatefilters it may be possible to plot select range gates from a series of sweeps which would approximate a CAPPI but I have not tried this.

Cheers,

    - Jonathan Helmus



[1] http://arm-doe.github.io/pyart-docs-travis/user_reference/generated/pyart.map.map_to_grid.html

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[Ali Alshaqah]

Hi Jon,

Could you please provide us with some examples of generating CAPPI from single and multiple Radar with some explanation .