Load ODIM_H5 Volume data from German Weather Service#

In this example, we obtain and read the last 2 hours of available volumetric radar data from German Weather Service available at opendata.dwd.de. Finally we do some plotting.

This retrieves 24 timesteps of the 10 sweeps (moments DBZH and VRADH) of the DWD volume scan of a distinct radar. This amounts to 480 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 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
import xradar as xd

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

[2]:

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

[3]:

from html.parser import HTMLParser


class DWDHTMLParser(HTMLParser):
    def handle_starttag(self, tag, attrs):
        if tag != "a":
            return
        self.links.append(attrs[0][1])


parser = DWDHTMLParser()

Download data from opendata.dwd.de#

[4]:

radar = "ESS"
DBZH = "sweep_vol_z"
VRADH = "sweep_vol_v"

import certifi

opendata_url1 = f"https://opendata.dwd.de/weather/radar/sites/{DBZH}/{radar.lower()}/hdf5/filter_polarimetric/"

http = urllib3.PoolManager(cert_reqs="CERT_REQUIRED", ca_certs=certifi.where())
response = http.request("GET", opendata_url1).data.decode("utf-8")

parser.links = []
parser.feed(response)
filelist1 = parser.links[1:]

filelist1.sort(key=lambda x: x.split("-")[2])
filelist1.reverse()

opendata_url2 = f"https://opendata.dwd.de/weather/radar/sites/{VRADH}/{radar.lower()}/hdf5/filter_polarimetric/"

http = urllib3.PoolManager(cert_reqs="CERT_REQUIRED", ca_certs=certifi.where())
response = http.request("GET", opendata_url2).data.decode("utf-8")

parser.links = []
parser.feed(response)
filelist2 = parser.links[1:]

filelist2.sort(key=lambda x: x.split("-")[2])
filelist2.reverse()

Clean up local folder#

[5]:

flist = glob.glob("ras07*")
for f in flist:
    os.remove(f)

Download latest 24 volumes to current directory#

[6]:

for f in filelist1[: 10 * 25]:
    with http.request(
        "GET", os.path.join(opendata_url1, f), preload_content=False
    ) as r, open(f, "wb") as out:
        shutil.copyfileobj(r, out)

for f in filelist2[: 10 * 25]:
    with http.request(
        "GET", os.path.join(opendata_url2, f), preload_content=False
    ) as r, open(f, "wb") as out:
        shutil.copyfileobj(r, out)

[7]:

volume_reflectivity = glob.glob("ras07*_dbzh_*")
volume_velocity = glob.glob("ras07*_vradh_*")

[8]:

volume_reflectivity = np.array(
    sorted(volume_reflectivity, key=lambda x: x.split("-")[2])
)
volume_velocity = np.array(sorted(volume_velocity, key=lambda x: x.split("-")[2]))

[9]:

volume_reflectivity = volume_reflectivity.reshape(-1, 10).T
volume_velocity = volume_velocity.reshape(-1, 10).T

Export to OdimH5#

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

[31]:

xd.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.

[32]:

xd.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 data from opendata.dwd.de#

Clean up local folder#

Download latest 24 volumes to current directory#

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#