ODIM_H5#

In this notebook an overview over several RADOLAN products in ODIM_H5 format is given.

import io
import os
import gc
import glob
import tarfile
import warnings

warnings.filterwarnings("ignore")

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import wradlib as wrl
import xarray as xr

Setup Environment#

Define data reading function and plotting function.

def read_radolan(radfile):
    radfile = os.path.join("../data/showcase/odim", radfile)
    return xr.open_dataset(radfile, engine="radolan").load()

def plot_radolan(ds, **kwargs):
    fig = plt.figure(figsize=(10, 8))
    ax = fig.add_subplot(111, aspect="equal")
    var = ds[list(ds.data_vars)[0]]
    pm = var.plot(ax=ax, cmap="viridis", vmin=0, **kwargs)
    ax.set_title(
        "{0} Product\n{1}".format(var.attrs["prodname"], ds.time[0].values.astype("M8[s]"))
    )
    ax.grid(color="r")

Composite#

A few products including RW and SF are available free of charge at this DWD OpenData Server. A full list of RADOLAN products can be found in the DWD RADOLAN Produktübersicht. For specific details on the RADOLAN ODIM_H5 Format see ODIM_H5 format.

Currently, most of the RADOLAN composites have a spatial resolution of 1km x 1km, with the National Composites (R-, S- and W-series) being 900 x 900 km grids, and the European Composites 1500 x 1400 km grids. The polar-stereographic projection is described in the chapter RADOLAN Grid.

One difference is the extended National Composite (only WX) with a 1100 x 900 km grid.

National Composites#

ID	INT	avail	Description
HX	5 min	5 min	Deutschland-Reflektivitäts-Komposit
RY	5 min	5 min	radardata after correction with Quality-composit (QY)
RL	1 h	hh:50	1 h summation with adjustment by Merging
RU	1 h	hh:50	1 h summation with standard and merging adjustment “best of three”
RW	1 h	hh:50	1 h summation with standard adjustment “best of two”
SF	24 h	hh:50	24 h summation of RW
SJ	N d	hh:50	summation of RW from Jan 1st to current day of year
SM	N d	hh:50	summation of RW from 1st of current month to current day of month
W1	7 d	05:50	7 d summation of RW
W2	14 d	05:50	14 d summation of RW
W3	21 d	05:50	21 d summation of RW

Extended Composites#

The common central european products with a range of 1500 km by 1400 km are presented in the following table:

ID	INT	avail
EH	1 h	hh:50
EB	1 h	hh:50
EW	1 h	hh:50

RADVOR and Nowcast#

The following products are based on RADOLAN or “RADVOR” products (Radar-based precipitation forecast in the shortest term). An opendata showcase is given below the following table.

ID	INT	avail	Description
WN	5 min	5 min	Analyzed and predicted precipitation, 1 analyse, 24 predictions (POLARA)
RV	5 min	5 min	Analyzed and predicted precipitation, 1 analyse, 24 predictions (POLARA)
RS	60 min	5 min	Analyzed and predicted precipitation, 1 analyse, 24 predictions (POLARA)

WN Product#

fname = "../data/radvor/odim/composite_wn_20251023_1050_.tar.gz"
fp = tarfile.open(fname)
fp.extractall()
names = fp.getnames()
buffer = [io.BytesIO(fp.extractfile(name).read()) for name in names]
for buf, name in zip(buffer, names):
    buf.name = name
fp.close()

ds = xr.open_mfdataset(buffer, engine="radolan", concat_dim="prediction_time", combine="nested")
display(ds)

<xarray.Dataset> Size: 264MB
Dimensions:          (prediction_time: 25, y: 1200, x: 1100, time: 1)
Coordinates:
  * prediction_time  (prediction_time) datetime64[ns] 200B 2025-10-23T10:50:0...
  * y                (y) float64 10kB -3.623e+06 -3.624e+06 ... -4.822e+06
  * x                (x) float64 9kB -5.432e+05 -5.422e+05 ... 5.558e+05
  * time             (time) datetime64[ns] 8B 2025-10-23T10:50:00
    crs_wkt          int64 8B 0
Data variables:
    DBZH             (prediction_time, y, x) float64 264MB dask.array<chunksize=(1, 1200, 1100), meta=np.ndarray>
Attributes:
    Conventions:     ODIM_H5/V2_3
    camethod:        MAXIMUM
    radarlocations:  
    software:        POLARA
    sw_version:      POLARA_hydrus_4.1.003
    source:          ORG:78,CTY:616,CMT:Deutscher Wetterdienst kundenservice@...
    version:         H5rad 2.3

ds.DBZH.plot(col="prediction_time", col_wrap=5, vmin=0, vmax=60)
del buffer
gc.collect()

../_images/684400e71ee606ce3becfd8d1b8cc0ce0322ce66aef65a4a9be121c6877138fa.png

Data: RADOLAN — Deutscher Wetterdienst (DWD). Licensed under CC BY 4.0.

RV Product#

fname = "../data/radvor/odim/composite_rv_20251023_0500_.tar.gz"
fp = tarfile.open(fname)
fp.extractall()
names = fp.getnames()
buffer = [io.BytesIO(fp.extractfile(name).read()) for name in names]
for buf, name in zip(buffer, names):
    buf.name = name
fp.close()

ds = xr.open_mfdataset(buffer, engine="radolan", concat_dim="prediction_time", combine="nested")
display(ds)

<xarray.Dataset> Size: 264MB
Dimensions:          (prediction_time: 25, y: 1200, x: 1100, time: 1)
Coordinates:
  * prediction_time  (prediction_time) datetime64[ns] 200B 2025-10-23T05:00:0...
  * y                (y) float64 10kB -3.623e+06 -3.624e+06 ... -4.822e+06
  * x                (x) float64 9kB -5.432e+05 -5.422e+05 ... 5.558e+05
  * time             (time) datetime64[ns] 8B 2025-10-23T05:00:00
    crs_wkt          int64 8B 0
Data variables:
    ACRR             (prediction_time, y, x) float64 264MB dask.array<chunksize=(1, 1200, 1100), meta=np.ndarray>
Attributes:
    Conventions:     ODIM_H5/V2_3
    camethod:        DOMAIN
    radarlocations:  'deasb','deboo','dedrs','deeis','deess','defbg','defld',...
    software:        POLARA
    sw_version:      POLARA_hydrus_4.1.003
    source:          ORG:78,CTY:616,CMT:Deutscher Wetterdienst kundenservice@...
    version:         H5rad 2.3

ds.ACRR.plot(col="prediction_time", col_wrap=5, vmin=0, vmax=2)
del buffer
gc.collect()

../_images/bea38c7402b1c9235bf1745b4286aa319cac104f8e5cf779e1cee6a1df5e2079.png

Data: RADOLAN — Deutscher Wetterdienst (DWD). Licensed under CC BY 4.0.

RS Product#

fname = "../data/radvor/odim/composite_rs_20251023_1050_.tar.gz"
fp = tarfile.open(fname)
fp.extractall()
names = fp.getnames()
buffer = [io.BytesIO(fp.extractfile(name).read()) for name in names]
for buf, name in zip(buffer, names):
    buf.name = name
fp.close()

ds = xr.open_mfdataset(buffer, engine="radolan", concat_dim="prediction_time", combine="nested")
display(ds)

<xarray.Dataset> Size: 264MB
Dimensions:          (prediction_time: 25, y: 1200, x: 1100, time: 1)
Coordinates:
  * prediction_time  (prediction_time) datetime64[ns] 200B 2025-10-23T10:50:0...
  * y                (y) float64 10kB -3.623e+06 -3.624e+06 ... -4.822e+06
  * x                (x) float64 9kB -5.432e+05 -5.422e+05 ... 5.558e+05
  * time             (time) datetime64[ns] 8B 2025-10-23T10:50:00
    crs_wkt          int64 8B 0
Data variables:
    ACRR             (prediction_time, y, x) float64 264MB dask.array<chunksize=(1, 1200, 1100), meta=np.ndarray>
Attributes:
    Conventions:     ODIM_H5/V2_3
    camethod:        DOMAIN
    radarlocations:  'deasb','deboo','dedrs','deeis','deess','defbg','defld',...
    software:        POLARA
    sw_version:      POLARA_hydrus_4.1.003
    source:          ORG:78,CTY:616,CMT:Deutscher Wetterdienst kundenservice@...
    version:         H5rad 2.3

ds.ACRR.plot(col="prediction_time", col_wrap=5, vmin=0, vmax=10)
del buffer
gc.collect()

../_images/fa715277861f99f9a67c6fd4857e0f876e6c75a1ce50ec0e96cc28c152d9cf9e.png

Data: RADOLAN — Deutscher Wetterdienst (DWD). Licensed under CC BY 4.0.

ODIM_H5

Contents

ODIM_H5#

Setup Environment#

Composite#

National Composites#

HX Product#

RY Product#

RL Product#

RU Product#

RW Product#

RW 2 Product#

SF Product#

SJ Product#

SM Product#

W1 Product#

W2 Product#

W3 Product#

Extended Composites#

EH Product#

EB Product#

EW Product#

VII Composites#

Composite VII Product#

EuCom VII Product#

EuComXL VII Product#

RADVOR and Nowcast#

WN Product#

RV Product#

RS Product#