Example on retrieving and plotting winds on a distributed clusterΒΆ

This is a simple example for how to retrieve winds using the nested grid features of PyDDA.

Author: Robert C. Jackson

  • PyDDA retreived winds @4.05 km
  • PyDDA retreived winds @20.0 km south of origin.
  • PyDDA retreived winds @20.0 km west of origin.

Out:

/blues/gpfs/home/rjackson/pyart/pyart/io/cfradial.py:365: UserWarning: WARNING: valid_min not used since it
cannot be safely cast to variable data type
  data = self.ncvar[:]
/blues/gpfs/home/rjackson/pyart/pyart/io/cfradial.py:365: UserWarning: WARNING: valid_max not used since it
cannot be safely cast to variable data type
  data = self.ncvar[:]
LocalCluster('tcp://127.0.0.1:34918', workers=2, threads=36, memory=135.13 GB)
<Client: 'tcp://127.0.0.1:34918' processes=2 threads=36, memory=135.13 GB>
/home/rjackson/anaconda3/envs/pyart-2020/lib/python3.7/site-packages/pydda/retrieval/angles.py:24: RuntimeWarning: invalid value encountered in arccos
  elev = np.arccos((Re**2 + slantrsq - rh**2)/(2 * Re * slantr))
Calculating weights for radars 0 and 1
/home/rjackson/anaconda3/envs/pyart-2020/lib/python3.7/site-packages/pydda/retrieval/wind_retrieve.py:653: RuntimeWarning: invalid value encountered in arccos
  theta_2 = np.arccos((x-rad2[1])/b)
Calculating weights for models...
Starting solver
rmsVR = 6.763780749907824
Total points: 23059
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|   7.3871|  31.7975|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  11.4149
Norm of gradient: 0.06526775209383506
Iterations before filter: 10
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|   0.7160|  14.2379|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  25.1676
Norm of gradient: 0.022656970042356808
Iterations before filter: 20
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|   0.5321|   9.8464|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  47.6630
Norm of gradient: 0.019575025191868944
Iterations before filter: 30
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|   0.1613|   7.4489|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  54.7406
Norm of gradient: 0.024242219942816612
Iterations before filter: 40
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|   0.0950|   6.6185|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  55.4003
Norm of gradient: 0.006852171865775456
Iterations before filter: 50
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|   0.0558|   5.5634|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  48.4985
Norm of gradient: 0.007062978135599922
Iterations before filter: 60
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|   0.0635|   5.1455|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  47.1794
Norm of gradient: 0.007387512143371025
Iterations before filter: 70
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|   0.0743|   4.7404|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  41.4687
Norm of gradient: 0.012528253916469778
Iterations before filter: 80
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|   0.0963|   4.4825|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  38.0718
Norm of gradient: 0.007383190140180377
Iterations before filter: 90
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|   0.0248|   4.2487|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  35.5239
Norm of gradient: 0.0063945697721171195
Iterations before filter: 100
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|   0.0282|   4.2277|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  35.1107
Norm of gradient: 0.006355652235950298
Iterations before filter: 110
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|   0.0161|   4.2245|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  35.0654
Norm of gradient: 0.006053121760374165
Iterations before filter: 120
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|   0.0212|   4.1906|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  34.8727
Norm of gradient: 0.011452701382835957
Iterations before filter: 130
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|   0.0146|   4.1923|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  34.8769
Norm of gradient: 0.0030135879499404877
Iterations before filter: 140
Applying low pass filter to wind field...
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|3746.0950|   2.8641|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  13.5970
Norm of gradient: 0.998609813914212
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|3378.9225|   2.9509|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  13.5978
Norm of gradient: 0.9547921379803676
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
| 221.0979|  23.0871|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  13.6217
Norm of gradient: 0.23792226768098468
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
| 114.3039|  24.1503|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  13.8287
Norm of gradient: 0.15061385334713479
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|  21.2646|  23.0345|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  13.6886
Norm of gradient: 0.06481576024485435
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|  24.7439|  19.7879|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  13.7359
Norm of gradient: 0.140284224757774
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|  10.5934|  21.2488|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  13.7121
Norm of gradient: 0.045492655928316694
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|   6.0063|  19.7451|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  13.7295
Norm of gradient: 0.03183975927355813
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|   1.9115|  16.4765|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  13.7880
Norm of gradient: 0.017344264451652755
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|   1.3409|  14.3912|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  13.8497
Norm of gradient: 0.02718263559465569
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|   1.4336|  12.4225|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  13.9390
Norm of gradient: 0.015722180537225953
| Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w
|   0.8802|  11.7888|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  13.9765
Norm of gradient: 0.010982739882723191
Iterations after filter: 1
Iterations after filter: 2
Done! Time = 138.8
Waiting for nested grid to be retrieved...
/home/rjackson/anaconda3/envs/pyart-2020/lib/python3.7/site-packages/pydda/vis/barb_plot.py:175: UserWarning: linewidths is ignored by contourf
  alpha=contour_alpha)
/home/rjackson/anaconda3/envs/pyart-2020/lib/python3.7/site-packages/pydda/retrieval/wind_retrieve.py:653: RuntimeWarning: invalid value encountered in arccos
  theta_2 = np.arccos((x-rad2[1])/b)
/home/rjackson/anaconda3/envs/pyart-2020/lib/python3.7/site-packages/pydda/vis/barb_plot.py:214: UserWarning: The following kwargs were not used by contour: 'color'
  levels=[bca_min, bca_max], color='k')
/home/rjackson/anaconda3/envs/pyart-2020/lib/python3.7/site-packages/pydda/retrieval/wind_retrieve.py:653: RuntimeWarning: invalid value encountered in arccos
  theta_2 = np.arccos((x-rad2[1])/b)
/home/rjackson/anaconda3/envs/pyart-2020/lib/python3.7/site-packages/pydda/vis/barb_plot.py:214: UserWarning: The following kwargs were not used by contour: 'color'
  levels=[bca_min, bca_max], color='k')
/home/rjackson/anaconda3/envs/pyart-2020/lib/python3.7/site-packages/pydda/vis/barb_plot.py:637: UserWarning: linewidths is ignored by contourf
  alpha=contour_alpha)
/home/rjackson/anaconda3/envs/pyart-2020/lib/python3.7/site-packages/pydda/vis/barb_plot.py:825: UserWarning: linewidths is ignored by contourf
  alpha=contour_alpha)

import pyart
import pydda
from matplotlib import pyplot as plt
from distributed import LocalCluster, Client

# Needed so that distributed doesn't run all of your code when the worker
# starts!
if __name__ == '__main__':

    berr_grid = pyart.io.read_grid(pydda.tests.EXAMPLE_RADAR0)
    cpol_grid = pyart.io.read_grid(pydda.tests.EXAMPLE_RADAR1)

    sounding = pyart.io.read_arm_sonde(pydda.tests.SOUNDING_PATH)

    # Load sounding data and insert as an intialization
    u_init, v_init, w_init = pydda.initialization.make_wind_field_from_profile(
        cpol_grid, sounding[1], vel_field='corrected_velocity')

    # Start our dask distributed cluster. You can use any distributed cluster
    # for this...a LocalCluster is used here for the sake of being able to run
    # this example locally.
    cluster = LocalCluster(n_workers=2)
    print(cluster)
    client = Client(cluster)
    print(client)

    # 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=1.0,
        Cm=1500.0, Cz=0, frz=5000.0,
        filt_iterations=2, mask_outside_opt=True, upper_bc=1)

    # Plot a horizontal cross section
    plt.figure(figsize=(9, 9))
    pydda.vis.plot_horiz_xsection_barbs(Grids, background_field='reflectivity',
                                        level=6,
                                        w_vel_contours=[3, 6, 9, 12, 15],
                                        barb_spacing_x_km=5.0,
                                        barb_spacing_y_km=15.0)
    plt.show()

    # Plot a vertical X-Z cross section
    plt.figure(figsize=(9, 9))
    pydda.vis.plot_xz_xsection_barbs(Grids, background_field='reflectivity',
                                     level=40,
                                     w_vel_contours=[3, 6, 9, 12, 15],
                                     barb_spacing_x_km=10.0,
                                     barb_spacing_z_km=2.0)
    plt.show()

    # Plot a vertical Y-Z cross section
    plt.figure(figsize=(9, 9))
    pydda.vis.plot_yz_xsection_barbs(Grids, background_field='reflectivity',
                                     level=40,
                                     w_vel_contours=[3, 6, 9, 12, 15],
                                     barb_spacing_y_km=10.0,
                                     barb_spacing_z_km=2.0)
    plt.show()

Total running time of the script: ( 4 minutes 47.999 seconds)

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