Guide and example on how to use nested grids with DataTrees#
This is an example on how to use PyDDA’s ability to handle nested grids using xarray DataTrees. In this example, we load radars with two pre-generated Cf/Radial grid. The fine grids are higher resolution grids that are contained within the coarser grid.
The DataTree structure that PyDDA follows is:
::
root
|---nest_0/radar_1
|---nest_0/radar_2
|---nest_0/radar_n
|---nest_1/radar_1
|---nest_1/radar_2
|---nest_1/radar_m
Each member of this tree is a DataTree itself. PyDDA will know if the
DataTree contains data from a radar when the name of the node begins
with radar_. The root node of each grid level, in this example,
root and inner_nest will contain the keyword arguments that are
inputs to :code:pydda.retrieval.get_dd_wind_field as attributes for the
tree. PyDDA will use the attributes at each level as the arguments for the
retrieval, allowing the user to vary the coefficients by grid level.
Using :code:pydda.retrieval.get_dd_wind_field_nested will allow PyDDA
to perform the retrieval on the 0th grid first. It will then
perform on the subsequent grid levels, using the previous nest as both the
horizontal boundary conditions and initialization for the retrieval in the next
nest. Finally, PyDDA will update the winds in the first grid by nearest-
neighbor interpolation of the latter grid into the overlapping portion between
the inner and outer grid level.
PyDDA will then return the retrieved wind fields as the “u”, “v”, and “w” DataArrays inside each of the root nodes for each level, in this case root and inner_nest.
## Do imports
import pydda
import matplotlib.pyplot as plt
import warnings
from xarray import DataTree
warnings.filterwarnings("ignore")
"""
We will load pregenerated grids for this case.
"""
test_coarse0 = pydda.io.read_grid(pydda.tests.get_sample_file("test_coarse0.nc"))
test_coarse1 = pydda.io.read_grid(pydda.tests.get_sample_file("test_coarse1.nc"))
test_fine0 = pydda.io.read_grid(pydda.tests.get_sample_file("test_fine0.nc"))
test_fine1 = pydda.io.read_grid(pydda.tests.get_sample_file("test_fine1.nc"))
"""
Initalize with a zero wind field. We have HRRR data already generated for this case inside
the example data files to provide a model constraint.
"""
test_coarse0 = pydda.initialization.make_constant_wind_field(
test_coarse0, (0.0, 0.0, 0.0)
)
"""
Specify the retrieval parameters at each level
"""
kwargs_dict = dict(
Cm=256.0,
Co=1e-2,
Cx=50.0,
Cy=50.0,
Cz=50.0,
Cmod=1e-5,
model_fields=["hrrr"],
refl_field="DBZ",
wind_tol=0.5,
max_iterations=150,
engine="scipy",
)
"""
Enforce equal times for each grid. This is required for the DataTree structure since time is an
inherited dimension.
"""
test_coarse1["time"] = test_coarse0["time"]
test_fine0["time"] = test_coarse0["time"]
test_fine1["time"] = test_coarse1["time"]
"""
Provide the overlying grid structure as specified above.
"""
tree_dict = {
"/nest_0/radar_ktlx": test_coarse0,
"/nest_0/radar_kict": test_coarse1,
"/nest_1/radar_ktlx": test_fine0,
"/nest_1/radar_kict": test_fine1,
}
tree = DataTree.from_dict(tree_dict)
tree["/nest_0/"].attrs = kwargs_dict
tree["/nest_1/"].attrs = kwargs_dict
"""
Perform the retrieval
"""
grid_tree = pydda.retrieval.get_dd_wind_field_nested(tree)
"""
Plot the coarse grid output and finer grid output
"""
fig, ax = plt.subplots(1, 2, figsize=(10, 5))
pydda.vis.plot_horiz_xsection_quiver(
grid_tree["nest_0"],
ax=ax[0],
level=5,
cmap="ChaseSpectral",
vmin=-10,
vmax=80,
quiverkey_len=10.0,
background_field="DBZ",
bg_grid_no=1,
w_vel_contours=[1, 2, 5, 10],
quiver_spacing_x_km=50.0,
quiver_spacing_y_km=50.0,
quiverkey_loc="bottom_right",
)
pydda.vis.plot_horiz_xsection_quiver(
grid_tree["nest_1"],
ax=ax[1],
level=5,
cmap="ChaseSpectral",
vmin=-10,
vmax=80,
quiverkey_len=10.0,
background_field="DBZ",
bg_grid_no=1,
w_vel_contours=[1, 2, 5, 10],
quiver_spacing_x_km=50.0,
quiver_spacing_y_km=50.0,
quiverkey_loc="bottom_right",
)
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 = 16.213679825765688
Total points: 412470
The max of w_init is 0.0
Total number of model points: 2342081
Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w
0|4115.2916| 0.0000| 0.0000| 0.0000| 0.0000|11243.5276| 0.0000| 0.0000
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
Cell In[1], line 68
64 """
65 Perform the retrieval
66 """
67
---> 68 grid_tree = pydda.retrieval.get_dd_wind_field_nested(tree)
69
70 """
71 Plot the coarse grid output and finer grid output
File ~/work/PyDDA/PyDDA/pydda/retrieval/nesting.py:56, in get_dd_wind_field_nested(grid_tree, **kwargs)
54 elif len(grid_list) > 0:
55 my_kwargs = tree_attrs
---> 56 output_grids, output_parameters = get_dd_wind_field(grid_list, **my_kwargs)
57 output_parameters = output_parameters.__dict__
58 if in_parent is True:
File ~/work/PyDDA/PyDDA/pydda/retrieval/wind_retrieve.py:1746, in get_dd_wind_field(Grids, u_init, v_init, w_init, engine, **kwargs)
1739 w_init = new_grids[0]["w"].values.squeeze()
1741 if (
1742 engine.lower() == "scipy"
1743 or engine.lower() == "jax"
1744 or engine.lower() == "auglag"
1745 ):
-> 1746 return _get_dd_wind_field_scipy(
1747 new_grids, u_init, v_init, w_init, engine, **kwargs
1748 )
1749 elif engine.lower() == "tensorflow":
1750 if kwargs.get("Cterrain", 0.0) > 0:
File ~/work/PyDDA/PyDDA/pydda/retrieval/wind_retrieve.py:788, 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, Cterrain, terrain, Cvad, 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, lower_bc, above, model_fields, output_cost_functions, roi, wind_tol, tolerance, const_boundary_cond, max_wind_mag, parallel)
786 parameters.print_out = False
787 if engine.lower() == "scipy":
--> 788 winds = fmin_l_bfgs_b(
789 J_and_grad,
790 winds,
791 args=(parameters,),
792 maxiter=max_iterations,
793 pgtol=tolerance,
794 bounds=bounds,
795 callback=_vert_velocity_callback,
796 )
797 else:
799 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:412, in ScalarFunction.fun_and_grad(self, x)
410 if not np.array_equal(x, 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:362, in ScalarFunction._update_fun(self)
360 def _update_fun(self):
361 if not self.f_updated:
--> 362 fx = self._wrapped_fun(self.x)
363 self._nfev += 1
364 if fx < self._lowest_f:
File /usr/share/miniconda/envs/pydda-docs/lib/python3.14/site-packages/scipy/_lib/_util.py:544, in _ScalarFunctionWrapper.__call__(self, x)
541 def __call__(self, x):
542 # Send a copy because the user may overwrite it.
543 # The user of this class might want `x` to remain unchanged.
--> 544 fx = self.f(np.copy(x), *self.args)
545 self.nfev += 1
547 # Make sure the function returns a true scalar
File /usr/share/miniconda/envs/pydda-docs/lib/python3.14/site-packages/scipy/optimize/_optimize.py:80, in MemoizeJac.__call__(self, x, *args)
78 def __call__(self, x, *args):
79 """ returns the function value """
---> 80 self._compute_if_needed(x, *args)
81 return self._value
File /usr/share/miniconda/envs/pydda-docs/lib/python3.14/site-packages/scipy/optimize/_optimize.py:74, in MemoizeJac._compute_if_needed(self, x, *args)
72 if not np.all(x == self.x) or self._value is None or self.jac is None:
73 self.x = np.asarray(x).copy()
---> 74 fg = self.fun(x, *args)
75 self.jac = fg[1]
76 self._value = fg[0]
File ~/work/PyDDA/PyDDA/pydda/cost_functions/cost_functions.py:1009, in J_and_grad(winds, parameters)
997 return J_function(winds, parameters), grad_J(winds, parameters)
999 shaped_winds = np.reshape(
1000 winds,
1001 (
(...) 1006 ),
1007 )
1008 radial_cost, radial_gradient = (
-> 1009 _cost_functions_numpy.calculate_radial_vel_cost_and_gradient(
1010 parameters.vrs,
1011 parameters.els,
1012 parameters.azs,
1013 shaped_winds[0],
1014 shaped_winds[1],
1015 shaped_winds[2],
1016 parameters.wts,
1017 parameters.weights,
1018 parameters.rmsVr,
1019 coeff=parameters.Co,
1020 upper_bc=parameters.upper_bc,
1021 upper_bc_mask=parameters.upper_bc_mask,
1022 parallel=False,
1023 radar_geometry=parameters.radar_geometry,
1024 )
1025 )
1026 previous_cache = getattr(parameters, "_radial_eval_cache", None)
1027 parameters._radial_eval_cache = {
1028 "source_winds": winds,
1029 "cost": radial_cost,
1030 "gradient": radial_gradient,
1031 }
File ~/work/PyDDA/PyDDA/pydda/cost_functions/_cost_functions_numpy.py:312, in calculate_radial_vel_cost_and_gradient(vrs, els, azs, u, v, w, wts, weights, rmsVr, coeff, upper_bc, upper_bc_mask, parallel, radar_geometry)
310 p_x1 += 2 * residual * x_projection * weight * lambda_o
311 p_y1 += 2 * residual * y_projection * weight * lambda_o
--> 312 p_z1 += 2 * residual * sin_el * weight * lambda_o
314 p_z1[0, :, :] = 0
315 p_z1 = _apply_upper_bc(p_z1, upper_bc, upper_bc_mask)
KeyboardInterrupt: