Example of a wind retrieval in a tornado over Sydney#

This shows an example of how to retrieve winds from 4 radars over Sydney.

We use smoothing to decrease the magnitude of the updraft in the region of the mesocyclone. The reduction of noise also helps the solution converge much faster since the cost function is smoother and therefore less susecptible to find a local minimum that is in noise.

The observational constraint is reduced to 0.01 from the usual 1 because we are factoring in many more data points as we are using 4 radars instead of the two in the Darwin example.

This example uses pooch to download the data files.

import pydda
import matplotlib.pyplot as plt
import numpy as np


grid1_path = pydda.tests.get_sample_file("grid1_sydney.nc")
grid2_path = pydda.tests.get_sample_file("grid2_sydney.nc")
grid3_path = pydda.tests.get_sample_file("grid3_sydney.nc")
grid4_path = pydda.tests.get_sample_file("grid4_sydney.nc")
grid1 = pydda.io.read_grid(grid1_path)
grid2 = pydda.io.read_grid(grid2_path)
grid3 = pydda.io.read_grid(grid3_path)
grid4 = pydda.io.read_grid(grid4_path)

# Set initialization and do retrieval
grid1 = pydda.initialization.make_constant_wind_field(grid1, vel_field="VRADH_corr")
new_grids, _ = pydda.retrieval.get_dd_wind_field(
    [grid1, grid2, grid3, grid4],
    Co=1e-2,
    Cm=256.0,
    Cx=10,
    Cy=10,
    Cz=10,
    vel_name="VRADH_corr",
    refl_field="DBZH",
    mask_outside_opt=True,
    wind_tol=0.5,
    max_iterations=200,
    engine="scipy",
)
# 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=20.0,
    w_vel_contours=[1.0, 3.0, 5.0, 10.0, 20.0],
    quiver_spacing_x_km=2.0,
    quiver_spacing_y_km=2.0,
    quiverkey_loc="top",
    colorbar_contour_flag=True,
    cmap="ChaseSpectral",
)
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()

## You are using the Python ARM Radar Toolkit (Py-ART), an open source
## library for working with weather radar data. Py-ART is partly
## supported by the U.S. Department of Energy as part of the Atmospheric
## Radiation Measurement (ARM) Climate Research Facility, an Office of
## Science user facility.
##
## If you use this software to prepare a publication, please cite:
##
##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119

Welcome to PyDDA 2.4.1
If you are using PyDDA in your publications, please cite:
Jackson et al. (2020) Journal of Open Research Science
Detecting Jax...
Jax/JaxOpt are not installed on your system, unable to use Jax engine.
Detecting TensorFlow...
Unable to load both TensorFlow and tensorflow-probability. TensorFlow engine disabled.
No module named 'tensorflow'

False

Calculating weights for radars 0 and 1
Calculating weights for radars 0 and 2
Calculating weights for radars 0 and 3

Calculating weights for radars 1 and 0
Calculating weights for radars 1 and 2
Calculating weights for radars 1 and 3

Calculating weights for radars 2 and 0
Calculating weights for radars 2 and 1
Calculating weights for radars 2 and 3

Calculating weights for radars 3 and 0
Calculating weights for radars 3 and 1
Calculating weights for radars 3 and 2

Calculating weights for models...
Starting solver 
rmsVR = 7.359750110231316
Total points: 399749
The max of w_init is 0.0

Total number of model points: 0

Nfeval | Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w  
      0|4080.4252|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000

The gradient of the cost functions is 0.11190916903769695

Nfeval | Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w  
     10|  92.4335|  17.0200|   0.0001|   0.0000|   0.0000|   0.0000|   0.0000|  48.1870

Max change in w:  nan

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
Cell In[1], line 17
grid4 = pydda.io.read_grid(grid4_path)

# Set initialization and do retrieval
grid1 = pydda.initialization.make_constant_wind_field(grid1, vel_field="VRADH_corr")
---> 17 new_grids, _ = pydda.retrieval.get_dd_wind_field(
   [grid1, grid2, grid3, grid4],
   Co=1e-2,
   Cm=256.0,

File ~/work/PyDDA/PyDDA/pydda/retrieval/wind_retrieve.py:1502, in get_dd_wind_field(Grids, u_init, v_init, w_init, engine, **kwargs)
   w_init = new_grids[0]["w"].values.squeeze()
if (
   engine.lower() == "scipy"
   or engine.lower() == "jax"
   or engine.lower() == "auglag"
):
-> 1502     return _get_dd_wind_field_scipy(
       new_grids, u_init, v_init, w_init, engine, **kwargs
   )
elif engine.lower() == "tensorflow":
   return _get_dd_wind_field_tensorflow(
       new_grids, u_init, v_init, w_init, **kwargs
   )

File ~/work/PyDDA/PyDDA/pydda/retrieval/wind_retrieve.py:614, in _get_dd_wind_field_scipy(Grids, u_init, v_init, w_init, engine, points, vel_name, refl_field, u_back, v_back, z_back, frz, Co, Cm, Cx, Cy, Cz, Cb, Cv, Cmod, Cpoint, cvtol, gtol, Jveltol, Ut, Vt, low_pass_filter, mask_outside_opt, weights_obs, weights_model, weights_bg, max_iterations, mask_w_outside_opt, filter_window, filter_order, min_bca, max_bca, upper_bc, model_fields, output_cost_functions, roi, wind_tol, tolerance, const_boundary_cond, max_wind_mag, parallel)
parameters.print_out = False
if engine.lower() == "scipy":
--> 614     winds = fmin_l_bfgs_b(
       J_function,
       winds,
       args=(parameters,),
       maxiter=max_iterations,
       pgtol=tolerance,
       bounds=bounds,
       fprime=grad_J,
       callback=_vert_velocity_callback,
   )
else:
   def loss_and_gradient(x):

File /usr/share/miniconda/envs/pydda-docs/lib/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:281, in fmin_l_bfgs_b(func, x0, fprime, args, approx_grad, bounds, m, factr, pgtol, epsilon, iprint, maxfun, maxiter, disp, callback, maxls)
callback = _wrap_callback(callback)
opts = {'disp': disp,
       'iprint': iprint,
       'maxcor': m,
   (...)    278         'callback': callback,
       'maxls': maxls}
--> 281 res = _minimize_lbfgsb(fun, x0, args=args, jac=jac, bounds=bounds,
                      **opts)
d = {'grad': res['jac'],
    'task': res['message'],
    'funcalls': res['nfev'],
    'nit': res['nit'],
    'warnflag': res['status']}
f = res['fun']

File /usr/share/miniconda/envs/pydda-docs/lib/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:461, in _minimize_lbfgsb(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, finite_diff_rel_step, workers, **unknown_options)
g = g.astype(np.float64)
# x, f, g, wa, iwa, task, csave, lsave, isave, dsave = \
--> 461 _lbfgsb.setulb(m, x, low_bnd, upper_bnd, nbd, f, g, factr, pgtol, wa,
              iwa, task, lsave, isave, dsave, maxls, ln_task)
if task[0] == 3:
   # The minimization routine wants f and g at the current x.
   # Note that interruptions due to maxfun are postponed
   # until the completion of the current minimization iteration.
   # Overwrite f and g:
   f, g = func_and_grad(x)

KeyboardInterrupt: 