ADD: Extra examples

examples/hurricane_florence.py

@@ -4,7 +4,12 @@
 
 This is an example of how to retrieve winds in Hurricane Florence.
 In this example, we use data from 2 NEXRAD radars as well as from
-the HRRR to retrieve the winds.
+the HRRR to retrieve the winds
+
+Grided netCDF files are downloadable from:
+https://drive.google.com/drive/u/1/folders/1pcQxWRJV78xuJePTZnlXPPpMe1qut0ie
+
+.. image:: ../../hrrr_figure.png
 
 Author: Robert C. Jackson
 """
@@ -14,6 +19,7 @@
 import pydda
 import matplotlib.pyplot as plt
 import cartopy.crs as ccrs
+import numpy as np
 
 hrrr_url = ('https://pando-rgw01.chpc.utah.edu/hrrr/prs/20180914/' +
             'hrrr.t06z.wrfprsf00.grib2')
@@ -27,15 +33,16 @@
 u_init, v_init, w_init = pydda.initialization.make_constant_wind_field(
     grid_mhx, (0.0, 0.0, 0.0))
 out_grids = pydda.retrieval.get_dd_wind_field(
-    [grid_mhx, grid_ltx], u_init, v_init, w_init, Co=1.0, Cm=1000.0, Cmod=1e-3,
+    [grid_mhx, grid_ltx], u_init, v_init, w_init, Co=0.1, Cm=1000.0, Cmod=1e-3,
     mask_outside_opt=True, vel_name='corrected_velocity',
     model_fields=["hrrr"])
 
-fig = plt.figure(figsize=(15, 10))
+fig = plt.figure(figsize=(25, 15))
 ax = plt.axes(projection=ccrs.PlateCarree())
 ax = pydda.vis.plot_horiz_xsection_barbs_map(
     out_grids, ax=ax, bg_grid_no=-1, level=1, barb_spacing_x_km=20.0,
-    barb_spacing_y_km=20.0)
-
+    barb_spacing_y_km=20.0, cmap='pyart_HomeyerRainbow')
+ax.set_xticks(np.arange(-80, -75, 0.5))
+ax.set_yticks(np.arange(33., 35.5, 0.5))
 plt.title(out_grids[0].time['units'][13:] + ' winds at 0.5 km')
 plt.show()

examples/oklahoma_example.py

@@ -0,0 +1,48 @@
+"""
+
+Example of a wind retrieval from KVNX and 2 XSAPRs in Oklahoma
+--------------------------------------------------------------
+
+This shows an example of how to combine data from 3 radars and HRRR in
+the vicinity of the SGP site in North Central Oklahoma.
+
+Grided netCDF files are downloadable from:
+https://drive.google.com/drive/u/1/folders/1pcQxWRJV78xuJePTZnlXPPpMe1qut0ie
+
+.. image:: ../../arm_figure.png
+
+"""
+
+import pyart
+import pydda
+import matplotlib.pyplot as plt
+import numpy as np
+import urllib
+
+hrrr_url = ('https://pando-rgw01.chpc.utah.edu/hrrr/prs/20181004/' +
+            'hrrr.t10z.wrfprsf00.grib2')
+grid0 = pyart.io.read_grid('grid0.20171004.095021.nc')
+grid1 = pyart.io.read_grid('grid1.20171004.095021.nc')
+grid2 = pyart.io.read_grid('grid2.20171004.095021.nc')
+
+urllib.request.urlretrieve(hrrr_url, 'test.grib2')
+grid_mhx = pydda.constraints.add_hrrr_constraint_to_grid(grid0,
+                                                         'test.grib2')
+
+# Set initialization and do retrieval
+u_init, v_init, w_init = pydda.initialization.make_constant_wind_field(grid1)
+new_grids = pydda.retrieval.get_dd_wind_field([grid0, grid1, grid2],
+                                              u_init, v_init, w_init, Co=0.1, Cm=100.0,
+                                              model_fields=["hrrr"],
+                                              mask_outside_opt=True)
+# Make a neat plot
+fig = plt.figure(figsize=(10,10))
+ax = pydda.vis.plot_horiz_xsection_quiver(new_grids, background_field='reflectivity', level=3,
+                                          show_lobes=False, bg_grid_no=0, vmin=0, vmax=60,
+                                          quiverkey_len=10.0,
+                                          quiver_spacing_x_km=2.0, quiver_spacing_y_km=2.0,
+                                          quiverkey_loc='top', colorbar_contour_flag=True,
+                                          cmap='pyart_HomeyerRainbow')
+ax.set_xlim([-20, 40])
+ax.set_ylim([-20, 40])
+plt.show(ax)

examples/plot_examples.py

@@ -28,7 +28,7 @@
 # Start the wind retrieval. This example only uses the mass continuity
 # and data weighting constraints.
 Grids = pydda.retrieval.get_dd_wind_field([berr_grid, cpol_grid], u_init,
-                                          v_init, w_init, Co=10.0, Cm=1500.0,
+                                          v_init, w_init, Co=1.0, Cm=1500.0,
                                           Cz=0, 
                                           frz=5000.0, filt_iterations=2,
                                           mask_outside_opt=True, upper_bc=1)

examples/plot_examples_nested.py

@@ -38,7 +38,7 @@
     # Start the wind retrieval. This example only uses the mass continuity
     # and data weighting constraints.
     Grids = pydda.retrieval.get_dd_wind_field_nested(
-        [berr_grid, cpol_grid], u_init,  v_init, w_init, client, Co=10.0,
+        [berr_grid, cpol_grid], u_init,  v_init, w_init, client, Co=1.0,
         Cm=1500.0, Cz=0, frz=5000.0,
         filt_iterations=2, mask_outside_opt=True, upper_bc=1)
 

examples/plot_fun_with_constraints.py

@@ -40,7 +40,7 @@
 new_grids = pydda.retrieval.get_dd_wind_field([cpol_grid, berr_grid],
                                     u_init, v_init, w_init,
                                     Co=1.0, Cm=1500.0, frz=5000.0,
-                                    mask_outside_opt=True)
+                                    mask_outside_opt=False)
 
 fig = plt.figure(figsize=(7, 7))
 ax = plt.axes(projection=ccrs.PlateCarree())
@@ -52,8 +52,8 @@
 # Or, let's make the radar data more important!
 new_grids = pydda.retrieval.get_dd_wind_field([cpol_grid, berr_grid],
                                     u_init, v_init, w_init,
-                                    Co=10.0, Cm=1500.0, frz=5000.0,
-                                    mask_outside_opt=True)
+                                    Co=1.0, Cm=1500.0, frz=5000.0,
+                                    mask_outside_opt=False)
 fig = plt.figure(figsize=(7, 7))
 ax = plt.axes(projection=ccrs.PlateCarree())
 

examples/sydney_tornado.py

@@ -0,0 +1,43 @@
+"""
+
+Example of a wind retrieval in a tornado over Sydney
+----------------------------------------------------
+
+This shows an example of how to retrieve winds from 4 radars over Sydney.
+
+Grided netCDF files are downloadable from:
+https://drive.google.com/drive/u/1/folders/1pcQxWRJV78xuJePTZnlXPPpMe1qut0ie
+
+.. image:: ../../sydney_tornado.png
+
+"""
+
+import pyart
+import pydda
+import matplotlib.pyplot as plt
+import numpy as np
+
+grid1 = pyart.io.read_grid('grid1_sydney.nc')
+grid2 = pyart.io.read_grid('grid2_sydney.nc')
+grid3 = pyart.io.read_grid('grid3_sydney.nc')
+grid4 = pyart.io.read_grid('grid4_sydney.nc')
+
+# Set initialization and do retrieval
+u_init, v_init, w_init = pydda.initialization.make_constant_wind_field(grid1, vel_field='VRADH_corr')
+new_grids = pydda.retrieval.get_dd_wind_field([grid1, grid2, grid3, grid4],
+                                              u_init, v_init, w_init,
+                                              vel_name='VRADH_corr', refl_field='DBZH',
+                                              mask_outside_opt=True)
+# Make a neat plot
+fig = plt.figure(figsize=(10,7))
+ax = pydda.vis.plot_horiz_xsection_quiver_map(new_grids, background_field='DBZH', level=3,
+                                              show_lobes=False, bg_grid_no=3, vmin=0, vmax=60,
+                                              quiverkey_len=40.0,
+                                              quiver_spacing_x_km=2.0, quiver_spacing_y_km=2.0,
+                                              quiverkey_loc='top', colorbar_contour_flag=True,
+                                              cmap='pyart_HomeyerRainbow')
+ax.set_xticks(np.arange(150.5, 153, 0.1))
+ax.set_yticks(np.arange(-36, -32.0, 0.1))
+ax.set_xlim([151.0, 151.35])
+ax.set_ylim([-34.15, -33.9])
+plt.show(ax)