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 plt
import numpy as np
import xarray as xr

try:
    get_ipython().run_line_magic("matplotlib inline")
except:
    plt.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]:

'0.49.0'

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)

  3%|▎         | 7/240 [00:04<02:16,  1.70it/s]
  3%|▎         | 7/240 [00:02<01:11,  3.28it/s]
  3%|▎         | 7/240 [00:02<01:12,  3.22it/s]
  3%|▎         | 7/240 [00:02<01:12,  3.21it/s]
  3%|▎         | 7/240 [00:02<01:10,  3.32it/s]
  2%|▎         | 6/240 [00:01<01:10,  3.31it/s]
  2%|▎         | 6/240 [00:01<01:07,  3.49it/s]
  2%|▎         | 6/240 [00:01<01:07,  3.47it/s]
  2%|▎         | 6/240 [00:02<01:25,  2.74it/s]
  2%|▎         | 6/240 [00:01<01:06,  3.53it/s]

CPU times: user 3.62 s, sys: 62.3 ms, total: 3.69 s
Wall time: 21.9 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 [00:01<01:06,  3.52it/s]
  3%|▎         | 7/240 [00:01<01:05,  3.53it/s]
  3%|▎         | 7/240 [00:02<01:07,  3.48it/s]
  3%|▎         | 7/240 [00:02<01:14,  3.11it/s]
  3%|▎         | 7/240 [00:02<01:10,  3.30it/s]
  2%|▎         | 6/240 [00:02<01:42,  2.28it/s]
  2%|▎         | 6/240 [00:01<01:10,  3.34it/s]
  2%|▎         | 6/240 [00:01<01:09,  3.35it/s]
  2%|▎         | 6/240 [00:01<01:10,  3.33it/s]
  2%|▎         | 6/240 [00:01<01:09,  3.38it/s]

CPU times: user 3.57 s, sys: 59.4 ms, total: 3.63 s
Wall time: 20.2 s

[9]:

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

Export to OdimH5#

This exports the radar volume at given timestep including all moments into one ODIM_H5 compliant data file.

[28]:

xradar.io.to_odim(vol0, "dwd_odim.h5", source="RAD:DWD")

Export to Cf/Radial2#

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

[29]:

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

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#

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#

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