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
Failed to import TF-Keras. Please note that TF-Keras is not installed by default when you install TensorFlow Probability. This is so that JAX-only users do not have to install TensorFlow or TF-Keras. To use TensorFlow Probability with TensorFlow, please install the tf-keras or tf-keras-nightly package.
This can be be done through installing the tensorflow-probability[tf] extra.


Welcome to PyDDA 2.1.0
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...
TensorFlow detected. Checking for tensorflow-probability...


Failed to import TF-Keras. Please note that TF-Keras is not installed by default when you install TensorFlow Probability. This is so that JAX-only users do not have to install TensorFlow or TF-Keras. To use TensorFlow Probability with TensorFlow, please install the tf-keras or tf-keras-nightly package.
This can be be done through installing the tensorflow-probability[tf] extra.


Unable to load both TensorFlow and tensorflow-probability. TensorFlow engine disabled.
No module named 'tf_keras'
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 = 7.242250167336594
Total points: 281276
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|2864.7900|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000
The gradient of the cost functions is 0.12289888187724508
Nfeval | Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w  
     10| 117.2266|  23.2756|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|  18.4482
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
Cell In[1], line 17
     15 # Set initialization and do retrieval
     16 grid1 = pydda.initialization.make_constant_wind_field(grid1, vel_field="VRADH_corr")
---> 17 new_grids, _ = pydda.retrieval.get_dd_wind_field(
     18     [grid1, grid2, grid3, grid4],
     19     Co=1e-2,
     20     Cm=256.0,
     21     Cx=10,
     22     Cy=10,
     23     Cz=10,
     24     vel_name="VRADH_corr",
     25     refl_field="DBZH",
     26     mask_outside_opt=True,
     27     wind_tol=0.5,
     28     max_iterations=200,
     29     engine="scipy",
     30 )
     31 # Make a neat plot
     32 fig = plt.figure(figsize=(10, 7))

File ~/work/PyDDA/PyDDA/pydda/retrieval/wind_retrieve.py:1474, in get_dd_wind_field(Grids, u_init, v_init, w_init, engine, **kwargs)
   1467     w_init = new_grids[0]["w"].values.squeeze()
   1469 if (
   1470     engine.lower() == "scipy"
   1471     or engine.lower() == "jax"
   1472     or engine.lower() == "auglag"
   1473 ):
-> 1474     return _get_dd_wind_field_scipy(
   1475         new_grids, u_init, v_init, w_init, engine, **kwargs
   1476     )
   1477 elif engine.lower() == "tensorflow":
   1478     return _get_dd_wind_field_tensorflow(
   1479         new_grids, u_init, v_init, w_init, **kwargs
   1480     )

File ~/work/PyDDA/PyDDA/pydda/retrieval/wind_retrieve.py:602, 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)
    600 parameters.print_out = False
    601 if engine.lower() == "scipy":
--> 602     winds = fmin_l_bfgs_b(
    603         J_function,
    604         winds,
    605         args=(parameters,),
    606         maxiter=max_iterations,
    607         pgtol=tolerance,
    608         bounds=bounds,
    609         fprime=grad_J,
    610         disp=0,
    611         iprint=-1,
    612         callback=_vert_velocity_callback,
    613     )
    614 else:
    616     def loss_and_gradient(x):

File /usr/share/miniconda/envs/pydda-docs/lib/python3.12/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)
    269 callback = _wrap_callback(callback)
    270 opts = {'disp': disp,
    271         'iprint': iprint,
    272         'maxcor': m,
   (...)    278         'callback': callback,
    279         'maxls': maxls}
--> 281 res = _minimize_lbfgsb(fun, x0, args=args, jac=jac, bounds=bounds,
    282                        **opts)
    283 d = {'grad': res['jac'],
    284      'task': res['message'],
    285      'funcalls': res['nfev'],
    286      'nit': res['nit'],
    287      'warnflag': res['status']}
    288 f = res['fun']

File /usr/share/miniconda/envs/pydda-docs/lib/python3.12/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)
    459 g = g.astype(np.float64)
    460 # 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,
    462                iwa, task, lsave, isave, dsave, maxls, ln_task)
    464 if task[0] == 3:
    465     # The minimization routine wants f and g at the current x.
    466     # Note that interruptions due to maxfun are postponed
    467     # until the completion of the current minimization iteration.
    468     # Overwrite f and g:
    469     f, g = func_and_grad(x)

KeyboardInterrupt: