Example on retrieving and plotting winds#
This is a simple example for how to retrieve and plot winds from 2 radars using PyDDA.
Author: Robert C. Jackson
import pydda
from matplotlib import pyplot as plt
berr_grid = pydda.io.read_grid(pydda.tests.EXAMPLE_RADAR0)
cpol_grid = pydda.io.read_grid(pydda.tests.EXAMPLE_RADAR1)
# Load sounding data and insert as an intialization
berr_grid = pydda.initialization.make_constant_wind_field(
berr_grid, (0.0, 0.0, 0.0), vel_field="corrected_velocity"
)
# 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],
Co=1.0,
Cm=256.0,
Cx=0.0,
Cy=0.0,
Cz=0.0,
Cb=0.0,
frz=5000.0,
filter_window=5,
mask_outside_opt=True,
upper_bc=1,
wind_tol=0.5,
engine="scipy",
parallel=False,
)
# 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=[5, 10, 15],
barb_spacing_x_km=5.0,
barb_spacing_y_km=15.0,
vmin=0,
vmax=70,
)
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=[5, 10, 15],
barb_spacing_x_km=10.0,
barb_spacing_z_km=2.0,
vmin=0,
vmax=70,
)
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,
barb_spacing_y_km=10.0,
barb_spacing_z_km=2.0,
vmin=0,
vmax=70,
)
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 Office of Science as part of
## the Atmospheric Radiation Measurement (ARM) 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 2.5.0
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...
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 1 and 0
Calculating weights for models...
Starting solver
rmsVR = 6.827303971100176
Total points: 81194
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|83859.8222| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000
The gradient of the cost functions is 0.6631357968851933
Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w
10| 1.8782| 41.0864| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 11.3085
Max change in w: 10.561
The gradient of the cost functions is 0.1375063575316335
Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w
20| 0.3524| 18.6065| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 11.8823
Max change in w: 6.008
The gradient of the cost functions is 0.09826186599547497
Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w
30| 0.1953| 10.8042| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 12.4878
Max change in w: 4.002
The gradient of the cost functions is 0.09669298385312876
Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w
40| 0.1234| 7.0816| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 16.4750
Max change in w: 5.363
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
Cell In[1], line 14
10 )
11
12 # Start the wind retrieval. This example only uses the mass continuity
13 # and data weighting constraints.
---> 14 Grids, _ = pydda.retrieval.get_dd_wind_field(
15 [berr_grid, cpol_grid],
16 Co=1.0,
17 Cm=256.0,
File ~/work/PyDDA/PyDDA/pydda/retrieval/wind_retrieve.py:1587, in get_dd_wind_field(Grids, u_init, v_init, w_init, engine, **kwargs)
1580 w_init = new_grids[0]["w"].values.squeeze()
1582 if (
1583 engine.lower() == "scipy"
1584 or engine.lower() == "jax"
1585 or engine.lower() == "auglag"
1586 ):
-> 1587 return _get_dd_wind_field_scipy(
1588 new_grids, u_init, v_init, w_init, engine, **kwargs
1589 )
1590 elif engine.lower() == "tensorflow":
1591 return _get_dd_wind_field_tensorflow(
1592 new_grids, u_init, v_init, w_init, **kwargs
1593 )
File ~/work/PyDDA/PyDDA/pydda/retrieval/wind_retrieve.py:667, 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_type, filter_window, filter_order, leise_nstep, min_bca, max_bca, upper_bc, above, model_fields, output_cost_functions, roi, wind_tol, tolerance, const_boundary_cond, max_wind_mag, parallel)
665 parameters.print_out = False
666 if engine.lower() == "scipy":
--> 667 winds = fmin_l_bfgs_b(
668 J_function,
669 winds,
670 args=(parameters,),
671 maxiter=max_iterations,
672 pgtol=tolerance,
673 bounds=bounds,
674 fprime=grad_J,
675 callback=_vert_velocity_callback,
676 )
677 else:
679 def loss_and_gradient(x):
File /usr/share/miniconda/envs/pydda-docs/lib/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:259, in fmin_l_bfgs_b(func, x0, fprime, args, approx_grad, bounds, m, factr, pgtol, epsilon, maxfun, maxiter, callback, maxls)
249 callback = _wrap_callback(callback)
250 opts = {'maxcor': m,
251 'ftol': factr * np.finfo(float).eps,
252 'gtol': pgtol,
(...) 256 'callback': callback,
257 'maxls': maxls}
--> 259 res = _minimize_lbfgsb(fun, x0, args=args, jac=jac, bounds=bounds,
260 **opts)
261 d = {'grad': res['jac'],
262 'task': res['message'],
263 'funcalls': res['nfev'],
264 'nit': res['nit'],
265 'warnflag': res['status']}
266 f = res['fun']
File /usr/share/miniconda/envs/pydda-docs/lib/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:420, in _minimize_lbfgsb(fun, x0, args, jac, bounds, maxcor, ftol, gtol, eps, maxfun, maxiter, callback, maxls, finite_diff_rel_step, workers, **unknown_options)
412 _lbfgsb.setulb(m, x, low_bnd, upper_bnd, nbd, f, g, factr, pgtol, wa,
413 iwa, task, lsave, isave, dsave, maxls, ln_task)
415 if task[0] == 3:
416 # The minimization routine wants f and g at the current x.
417 # Note that interruptions due to maxfun are postponed
418 # until the completion of the current minimization iteration.
419 # Overwrite f and g:
--> 420 f, g = func_and_grad(x)
421 elif task[0] == 1:
422 # new iteration
423 n_iterations += 1
File /usr/share/miniconda/envs/pydda-docs/lib/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:413, in ScalarFunction.fun_and_grad(self, x)
411 self._update_x(x)
412 self._update_fun()
--> 413 self._update_grad()
414 return self.f, self.g
File /usr/share/miniconda/envs/pydda-docs/lib/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:375, in ScalarFunction._update_grad(self)
373 if self._orig_grad in FD_METHODS:
374 self._update_fun()
--> 375 self.g = self._wrapped_grad(self.x, f0=self.f)
376 self.g_updated = True
File /usr/share/miniconda/envs/pydda-docs/lib/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:39, in _ScalarGradWrapper.__call__(self, x, f0, **kwds)
35 def __call__(self, x, f0=None, **kwds):
36 # Send a copy because the user may overwrite it.
37 # The user of this class might want `x` to remain unchanged.
38 if callable(self.grad):
---> 39 g = np.atleast_1d(self.grad(np.copy(x), *self.args))
40 elif self.grad in FD_METHODS:
41 g, dct = approx_derivative(
42 self.fun,
43 x,
44 f0=f0,
45 **self.finite_diff_options,
46 )
File ~/work/PyDDA/PyDDA/pydda/cost_functions/cost_functions.py:635, in grad_J(winds, parameters)
633 grad = sum(f.result() for f in futures)
634 else:
--> 635 grad = _cost_functions_numpy.calculate_grad_radial_vel(
636 parameters.vrs,
637 parameters.els,
638 parameters.azs,
639 winds[0],
640 winds[1],
641 winds[2],
642 parameters.wts,
643 parameters.weights,
644 parameters.rmsVr,
645 coeff=parameters.Co,
646 upper_bc=parameters.upper_bc,
647 upper_bc_mask=parameters.upper_bc_mask,
648 )
650 if parameters.Cm > 0:
651 grad += _cost_functions_numpy.calculate_mass_continuity_gradient(
652 winds[0],
653 winds[1],
(...) 661 upper_bc_mask=parameters.upper_bc_mask,
662 )
File ~/work/PyDDA/PyDDA/pydda/cost_functions/_cost_functions_numpy.py:214, in calculate_grad_radial_vel(vrs, els, azs, u, v, w, wts, weights, rmsVr, coeff, upper_bc, upper_bc_mask, parallel)
210 p_z1 = np.zeros(vrs[0].shape)
212 for i in range(len(vrs)):
213 v_ar = (
--> 214 np.cos(els[i]) * np.sin(azs[i]) * u
215 + np.cos(els[i]) * np.cos(azs[i]) * v
216 + np.sin(els[i]) * (w - np.abs(wts[i]))
217 )
219 x_grad = (
220 2 * (v_ar - vrs[i]) * np.cos(els[i]) * np.sin(azs[i]) * weights[i]
221 ) * lambda_o
222 y_grad = (
223 2 * (v_ar - vrs[i]) * np.cos(els[i]) * np.cos(azs[i]) * weights[i]
224 ) * lambda_o
KeyboardInterrupt: