Load ODIM_H5 Volume data from German Weather Service#

In this example, we obtain and read the latest 30 minutes of available volumetric radar data from German Weather Service available at opendata.dwd.de. Finally we do some plotting.

This retrieves 6 timesteps of the 10 sweeps (moments DBZH and VRADH) of the DWD volume scan of a distinct radar. This amounts to 120 data files which are combined into one volumetric Cf/Radial2 like xarray powered structure.

Exports to single file Odim_H5 and Cf/Radial2 format are shown at the end of this tutorial.

Note

The following code is based on xarray, xarray-datatree and xradar. It claims multiple data files and presents them in a DataTree.

[1]:

import wradlib as wrl
import warnings

warnings.filterwarnings("ignore")
import matplotlib.pyplot as pl
import numpy as np
import xarray as xr

try:
    get_ipython().run_line_magic("matplotlib inline")
except:
    pl.ion()

[2]:

import urllib3
import os
import io
import glob
import shutil
import datetime

Download radar volumes of latest 30 minutes from server using `wetterdienst`#

wetterdienst is a neat package for easy retrieval of data primarily from DWD. For further information have a look at their documentation.

[3]:

from wetterdienst.provider.dwd.radar import (
    DwdRadarDataFormat,
    DwdRadarDataSubset,
    DwdRadarParameter,
    DwdRadarValues,
)
from wetterdienst.provider.dwd.radar.sites import DwdRadarSite

[4]:

elevations = range(10)

end_date = datetime.datetime.utcnow()
start_date = end_date - datetime.timedelta(minutes=30)

results_velocity = []
results_reflectivity = []

for el in elevations:
    # Horizontal Doppler Velocity
    request_velocity = DwdRadarValues(
        parameter=DwdRadarParameter.SWEEP_VOL_VELOCITY_H,
        start_date=start_date,
        end_date=end_date,
        site=DwdRadarSite.ESS,
        elevation=el,
        fmt=DwdRadarDataFormat.HDF5,
        subset=DwdRadarDataSubset.POLARIMETRIC,
    )

    # Horizontal Reflectivity
    request_reflectivity = DwdRadarValues(
        parameter=DwdRadarParameter.SWEEP_VOL_REFLECTIVITY_H,
        start_date=start_date,
        end_date=end_date,
        elevation=el,
        site=DwdRadarSite.ESS,
        fmt=DwdRadarDataFormat.HDF5,
        subset=DwdRadarDataSubset.POLARIMETRIC,
    )

    # Submit requests.
    results_velocity.append(request_velocity.query())
    results_reflectivity.append(request_reflectivity.query())

[5]:

import wetterdienst

wetterdienst.__version__

[5]:

'0.20.3'

Acquire data as memory buffer#

[6]:

%%time
volume_velocity = []
for item1 in results_velocity:
    files = []
    for item2 in item1:
        files.append(item2.data)
    volume_velocity.append(files)

  2%|▎         | 6/240 [00:08<05:40,  1.45s/it]
  2%|▎         | 6/240 [00:04<02:53,  1.35it/s]
  2%|▎         | 6/240 [00:05<03:15,  1.20it/s]
  2%|▎         | 6/240 [00:04<03:05,  1.26it/s]
  2%|▎         | 6/240 [00:05<03:16,  1.19it/s]
  2%|▎         | 6/240 [00:04<03:07,  1.25it/s]
  2%|▎         | 6/240 [00:04<03:02,  1.28it/s]
  2%|▎         | 6/240 [00:04<02:59,  1.30it/s]
  2%|▎         | 6/240 [00:04<02:57,  1.32it/s]
  2%|▎         | 6/240 [00:04<02:54,  1.34it/s]

CPU times: user 10.5 s, sys: 217 ms, total: 10.7 s
Wall time: 51.2 s

[7]:

volume_velocity = [v[-6:] for v in volume_velocity]
volume_velocity = np.array(volume_velocity).T.tolist()

[8]:

%%time
volume_reflectivity = []
for item1 in results_reflectivity:
    files = []
    for item2 in item1:
        files.append(item2.data)
    volume_reflectivity.append(files)

  3%|▎         | 7/240 [00:06<03:33,  1.09it/s]
  2%|▎         | 6/240 [00:04<02:46,  1.40it/s]
  2%|▎         | 6/240 [00:03<02:35,  1.50it/s]
  2%|▎         | 6/240 [00:03<02:35,  1.50it/s]
  2%|▎         | 6/240 [00:04<02:48,  1.39it/s]
  2%|▎         | 6/240 [00:04<03:13,  1.21it/s]
  2%|▎         | 6/240 [00:06<04:00,  1.03s/it]
  2%|▎         | 6/240 [00:03<02:25,  1.60it/s]
  2%|▎         | 6/240 [00:04<02:36,  1.50it/s]
  2%|▎         | 6/240 [00:04<02:54,  1.34it/s]

CPU times: user 10.7 s, sys: 153 ms, total: 10.8 s
Wall time: 46.4 s

[9]:

volume_reflectivity = [v[-6:] for v in volume_reflectivity]
volume_reflectivity = np.array(volume_reflectivity).T.tolist()

Export to Cf/Radial2#

This exports the radar volume at given timestep including all moments into one Cf/Radial2 compliant data file.

[30]:

xradar.io.to_cfradial2(vol0, "dwd_cfradial2.nc")

Import again and check equality#

[31]:

vol1 = xradar.io.open_odim_datatree("dwd_odim.h5")
vol2 = open_datatree("dwd_cfradial2.nc")

[32]:

xr.testing.assert_equal(vol1.root.ds, vol2.root.ds)
for grp in vol1.groups[1:]:
    xr.testing.assert_equal(
        vol1[grp]
        .to_dataset()
        .reset_coords(["latitude", "longitude", "altitude"], drop=True),
        vol2[grp].to_dataset().swap_dims(time="azimuth").sortby("azimuth"),
    )

Load ODIM_H5 Volume data from German Weather Service

Contents

Load ODIM_H5 Volume data from German Weather Service#

Download radar volumes of latest 30 minutes from server using `wetterdienst`#

Acquire data as memory buffer#

Read the data into xarray powered structure#

Inspect structure#

Root Group#

Sweep Groups#

plot sweeps#

DBZH#

VRADH#

Plot single sweep using cartopy#

Inspect radar moments#

Plot Quasi Vertical Profile#

Export to OdimH5#

Export to Cf/Radial2#

Import again and check equality#

Load ODIM_H5 Volume data from German Weather Service

Contents

Load ODIM_H5 Volume data from German Weather Service#

Download radar volumes of latest 30 minutes from server using wetterdienst#

Acquire data as memory buffer#

Read the data into xarray powered structure#

Inspect structure#

Root Group#

Sweep Groups#

plot sweeps#

DBZH#

VRADH#

Plot single sweep using cartopy#

Inspect radar moments#

Plot Quasi Vertical Profile#

Export to OdimH5#

Export to Cf/Radial2#

Import again and check equality#

Download radar volumes of latest 30 minutes from server using `wetterdienst`#