Example on integrating radar and HRRR data#

This is an example of how to retrieve winds in Hurricane Florence. In this example, we use data from 2 NEXRAD radars as well as from the HRRR to retrieve the winds

This example has been updated to use _Herbie to retrieve the HRRR data. In addition, _pooch is used to retrieve the gridded data for the example. Herbie is not required to run PyDDA, but must be installed to run this example.

Author: Robert C. Jackson

import pydda
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np

from herbie import Herbie

H = Herbie("2018-09-14 06:00", model="hrrr", product="prs", fxx=0)
H.download()

grid_mhx_path = pydda.tests.get_sample_file("grid_mhx.nc")
grid_ltx_path = pydda.tests.get_sample_file("grid_ltx.nc")

grid_mhx = pydda.io.read_grid(grid_mhx_path)
grid_ltx = pydda.io.read_grid(grid_ltx_path)

grid_mhx = pydda.constraints.add_hrrr_constraint_to_grid(grid_mhx, H.grib)
grid_mhx = pydda.initialization.make_constant_wind_field(grid_mhx, (0.0, 0.0, 0.0))
out_grids, _ = pydda.retrieval.get_dd_wind_field(
    [grid_mhx, grid_ltx],
    Co=1e-2,
    Cm=128.0,
    Cmod=1e-4,
    Cx=1e-4,
    Cy=1e-4,
    Cz=1e-4,
    max_iterations=100,
    mask_outside_opt=True,
    vel_name="corrected_velocity",
    engine="scipy",
    model_fields=["hrrr"],
)

fig = plt.figure(figsize=(25, 15))
ax = plt.axes(projection=ccrs.PlateCarree())
ax = pydda.vis.plot_horiz_xsection_barbs_map(
    out_grids,
    ax=ax,
    bg_grid_no=-1,
    level=3,
    barb_spacing_x_km=20.0,
    barb_spacing_y_km=20.0,
    cmap="ChaseSpectral",
)
ax.set_xticks(np.arange(-80, -75, 0.5))
ax.set_yticks(np.arange(33.0, 35.5, 0.5))
plt.title(out_grids[0].time.attrs["units"][13:] + " winds at 0.5 km")
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'

✅ Found ┊ model=hrrr ┊ product=prs ┊ 2018-Sep-14 06:00 UTC F00 ┊ GRIB2 @ local ┊ IDX @ aws

False

Calculating weights for radars 0 and 1

Calculating weights for radars 1 and 0

Exception ignored in: <bound method IPythonKernel._clean_thread_parent_frames of <ipykernel.ipkernel.IPythonKernel object at 0x7f184f560980>>
Traceback (most recent call last):
  File "/usr/share/miniconda/envs/pydda-docs/lib/python3.12/site-packages/ipykernel/ipkernel.py", line 781, in _clean_thread_parent_frames
    def _clean_thread_parent_frames(

KeyboardInterrupt: 

Calculating weights for models...
Starting solver 
rmsVR = 21.58818045293143
Total points: 1284618
The max of w_init is 0.0

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