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 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 2.1.0
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...
TensorFlow-probability detected. TensorFlow engine enabled!
False
Calculating weights for radars 0 and 1
Calculating weights for radars 1 and 0
Calculating weights for models...
Starting solver
rmsVR = 16.21367982576569
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
62 tree["/nest_1/"].attrs = kwargs_dict
64 """
65 Perform the retrieval
66 """
---> 68 grid_tree = pydda.retrieval.get_dd_wind_field_nested(tree)
70 """
71 Plot the coarse grid output and finer grid output
72 """
74 fig, ax = plt.subplots(1, 2, figsize=(10, 5))
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: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/miniconda3/envs/pydda-docs/lib/python3.10/site-packages/scipy/optimize/_lbfgsb_py.py:237, in fmin_l_bfgs_b(func, x0, fprime, args, approx_grad, bounds, m, factr, pgtol, epsilon, iprint, maxfun, maxiter, disp, callback, maxls)
225 callback = _wrap_callback(callback)
226 opts = {'disp': disp,
227 'iprint': iprint,
228 'maxcor': m,
(...)
234 'callback': callback,
235 'maxls': maxls}
--> 237 res = _minimize_lbfgsb(fun, x0, args=args, jac=jac, bounds=bounds,
238 **opts)
239 d = {'grad': res['jac'],
240 'task': res['message'],
241 'funcalls': res['nfev'],
242 'nit': res['nit'],
243 'warnflag': res['status']}
244 f = res['fun']
File /usr/share/miniconda3/envs/pydda-docs/lib/python3.10/site-packages/scipy/optimize/_lbfgsb_py.py:369, in _minimize_lbfgsb(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, finite_diff_rel_step, **unknown_options)
367 low_bnd[i] = l
368 l = 1
--> 369 if not np.isinf(u):
370 upper_bnd[i] = u
371 u = 1
KeyboardInterrupt: