Legacy

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

import io
import os
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

Setup Environment

Get RADOLAN Grid and RADOLAN Extended Grid.

# Get coordinates
radolan_grid_xy = wrl.georef.get_radolan_grid(900, 900)
radolan_egrid_xy = wrl.georef.get_radolan_grid(1500, 1400)
radolan_wgrid_xy = wrl.georef.get_radolan_grid(1100, 900)
x = radolan_grid_xy[:, :, 0]
y = radolan_grid_xy[:, :, 1]

xe = radolan_egrid_xy[:, :, 0]
ye = radolan_egrid_xy[:, :, 1]

xw = radolan_wgrid_xy[:, :, 0]
yw = radolan_wgrid_xy[:, :, 1]

Define data reading function and plotting function.

def read_radolan(radfile):
    radfile = os.path.join("../data/showcase/legacy", radfile)
    return wrl.io.read_radolan_composite(radfile)

def plot_radolan(data, attrs, grid, clabel=None):
    fig = plt.figure(figsize=(10, 8))
    ax = fig.add_subplot(111, aspect="equal")
    x = grid[:, :, 0]
    y = grid[:, :, 1]
    pm = ax.pcolormesh(x, y, data, cmap="viridis")
    cb = fig.colorbar(pm, shrink=0.75)
    cb.set_label(clabel)
    plt.xlabel("x [km]")
    plt.ylabel("y [km]")
    plt.title(
        "{0} Product\n{1}".format(attrs["producttype"], attrs["datetime"].isoformat())
    )
    plt.xlim((x[0, 0], x[-1, -1]))
    plt.ylim((y[0, 0], y[-1, -1]))
    plt.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. Specific details on the RADOLAN Format can be retrieved from the DWD RADOLAN/RADVOR-OP Kompositformat.

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.

Also the PG/PC Product with 460 x 460 km grid and runlength-coding is shortly described.

National Composites

ID	INT	avail	Description
RX/WX	5 min	5 min	original radardata in qualitative RVP6-units (1 byte coded)
RZ	5 min	5 min	radardata after correction of PBB converted to rainrate with improved Z-R-relation
RY	5 min	5 min	radardata after correction with Quality-composit (QY)
RH	1 h	5 min	1 h summation of RZ-composit
RB	1 h	hh:50	1 h summation with preadjustment
RW	1 h	hh:50	1 h summation with standard adjustment “best of two”
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”
SQ	6 h	hh:50	6 h summation of RW
SH	12 h	hh:50	12 h summation of RW
SF	24 h	hh:50	24 h summation of RW
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
W4	30 d	05:50	30 d summation of RW

RX Product

Load data from data source.

data, attrs = read_radolan("raa01-rx_10000-1408102050-dwd---bin.gz")

Mask data and apply scale and offset

data = np.ma.masked_equal(data, -9999) / 2 - 32.5

plot_radolan(data, attrs, radolan_grid_xy, clabel="dBZ")

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

RZ Product

Load data from data source

data, attrs = read_radolan("raa01-rz_10000-1408102050-dwd---bin.gz")

Mask data

data = np.ma.masked_equal(data, -9999)

plot_radolan(data, attrs, radolan_grid_xy, clabel="mm * 5min-1")

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

RY Product

Load data from data source

data, attrs = read_radolan("raa01-ry_10000-1408102050-dwd---bin.gz")

Mask data

data = np.ma.masked_equal(data, -9999)

plot_radolan(data, attrs, radolan_grid_xy, clabel="mm * 5min-1")

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

RH Product

Load data from data source

data, attrs = read_radolan("raa01-rh_10000-1408102050-dwd---bin.gz")

Mask data

data = np.ma.masked_equal(data, -9999)

plot_radolan(data, attrs, radolan_grid_xy, clabel="mm * h-1")

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

RB Product

Load data from data source

data, attrs = read_radolan("raa01-rb_10000-1408102050-dwd---bin.gz")

Mask data

data = np.ma.masked_equal(data, -9999)

plot_radolan(data, attrs, radolan_grid_xy, clabel="mm * h-1")

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

RL Product

Load data from data source

data, attrs = read_radolan("raa01-rl_10000-1408102050-dwd---bin.gz")

Mask data

data = np.ma.masked_equal(data, -9999)

plot_radolan(data, attrs, radolan_grid_xy, clabel="mm * h-1")

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

RW Product

Load data from data source

data, attrs = read_radolan("raa01-rw_10000-1408102050-dwd---bin.gz")

Mask data

data = np.ma.masked_equal(data, -9999)

plot_radolan(data, attrs, radolan_grid_xy, clabel="mm * h-1")

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

RU Product

Load data from data source

data, attrs = read_radolan("raa01-ru_10000-1408102050-dwd---bin.gz")

Mask data

data = np.ma.masked_equal(data, -9999)

plot_radolan(data, attrs, radolan_grid_xy, clabel="mm * h-1")

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

SQ Product

Load data from data source

data, attrs = read_radolan("raa01-sq_10000-1408102050-dwd---bin.gz")

Mask data

data = np.ma.masked_equal(data, -9999)

plot_radolan(data, attrs, radolan_grid_xy, clabel="mm * 6h-1")

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

SH Product

Load data from data source

data, attrs = read_radolan("raa01-sh_10000-1408102050-dwd---bin.gz")

Mask data

data = np.ma.masked_equal(data, -9999)

plot_radolan(data, attrs, radolan_grid_xy, clabel="mm * 12h-1")

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

SF Product

Load data from data source

data, attrs = read_radolan("raa01-sf_10000-1408102050-dwd---bin.gz")

Mask data

data = np.ma.masked_equal(data, -9999)

plot_radolan(data, attrs, radolan_grid_xy, clabel="mm * 24h-1")

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

W1 Product

Load data from data source

data, attrs = read_radolan("raa01-w1_10000-1408110550-dwd---bin.gz")

Mask data

data = np.ma.masked_equal(data, -9999)

plot_radolan(data, attrs, radolan_grid_xy, clabel="mm * 7d-1")

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

W2 Product

Load data from data source

data, attrs = read_radolan("raa01-w2_10000-1408110550-dwd---bin.gz")

Mask data

data = np.ma.masked_equal(data, -9999)

plot_radolan(data, attrs, radolan_grid_xy, clabel="mm * 14d-1")

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

WX Product

data, attrs = read_radolan("raa01-wx_10000-1408102050-dwd---bin.gz")

Mask data

data = np.ma.masked_equal(data, -9999) / 2 - 32.5

plot_radolan(data, attrs, radolan_wgrid_xy, clabel="dBZ")

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

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	Description
EX	5 min	5 min	analogue RX
EZ	5 min	5 min	analogue RZ
EY	5 min	5 min	analogue EY after correction with Quality-composit
EH	1 h	hh:50	analogue RH (no preadjustment) 1 h summation of EY-composite
EB	1 h	hh:50	analogue RB (with preadjustment) 1 h summation
EW	1 h	hh:50	analogue RW (full adjustment) 1 h summation

EX Product

Load data from data source

data, attrs = read_radolan("raa01-ex_10000-1408102050-dwd---bin.gz")

Mask data and apply scale and offset

data = np.ma.masked_equal(data, -9999) / 2 - 32.5

plot_radolan(data, attrs, radolan_egrid_xy, clabel="dBZ")

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

EZ Product

Load data from data source

data, attrs = read_radolan("raa01-ez_10000-1408102050-dwd---bin.gz")

Mask data

data = np.ma.masked_equal(data, -9999)

plot_radolan(data, attrs, radolan_egrid_xy, clabel="mm * 5min-1")

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

EY Product

Load data from data source

data, attrs = read_radolan("raa01-ey_10000-1408102050-dwd---bin.gz")

Mask data

data = np.ma.masked_equal(data, -9999)

plot_radolan(data, attrs, radolan_egrid_xy, clabel="mm * 5min-1")

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

EH Product

Load data from data source

data, attrs = read_radolan("raa01-eh_10000-1408102050-dwd---bin.gz")

Mask data

data = np.ma.masked_equal(data, -9999)

plot_radolan(data, attrs, radolan_egrid_xy, clabel="mm * h-1")

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

EB Product

Load data from data source

data, attrs = read_radolan("raa01-eb_10000-1408102050-dwd---bin.gz")

Mask data

data = np.ma.masked_equal(data, -9999)

plot_radolan(data, attrs, radolan_egrid_xy, clabel="mm * h-1")

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

PG and PC Product

The PG/PC product is a bit different from the normal RADOLAN formats. The header is actually the same, but the data is runlength encoded. Also, the RADOLAN grid cells have 2km edge length (460x460 cells).

Load data from data source

radfile = "../data/misc/raa00-pc_10015-1408030905-dwd---bin.gz"
data, attrs = wrl.io.read_radolan_composite(radfile, missing=255)
radolan_grid_pc = wrl.georef.get_radolan_grid(460, 460)

Mask data

data = np.ma.masked_equal(data, 255)
print(data.shape)

(460, 460)

# plot the images side by side
plt.figure(figsize=(10, 8))
plt.subplot(111, aspect="equal")
X = radolan_grid_pc[:, :, 0]
Y = radolan_grid_pc[:, :, 1]
# color-scheme taken from DWD "legend_radar_products_pc.pdf"
colors = [
    "lightgrey",
    "yellow",
    "lightblue",
    "magenta",
    "green",
    "red",
    "darkblue",
    "darkred",
]
cmap = mpl.colors.ListedColormap(colors, name="DWD-pc-scheme")
bounds = np.arange(len(colors) + 1)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
plt.pcolormesh(X, Y, data, cmap=cmap, norm=norm)
plt.xlim((X[0, 0], X[-1, -1]))
plt.ylim((Y[0, 0], Y[-1, -1]))

# add colorbar and do some magic for proper visualisation
cb = plt.colorbar(shrink=0.75, norm=norm, boundaries=bounds)
loc = bounds + 0.5
cb.set_ticks(loc[:-1])
labels = bounds[:-1]
cb.set_ticklabels(labels)
cl = cb.ax.get_yticklabels()
cl[-1].set_text("9")
cb.ax.set_yticklabels([elem.get_text() for elem in cl])
plt.title("RADOLAN PG Product \n" + attrs["datetime"].isoformat())
plt.grid(color="r")

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

RADVOR

Further new developments are the “RADVOR” products (Radar-based precipitation forecast in the shortest term). An opendata showcase is given below the following table.

ID	INT	avail	Description
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)
RQ	60 min	15 min	Quantified precipitation analysis and prediction, 1 analyse, 2 predictions
RE	60 min	5 min	Analysis and prediction of the aggregate state and hail, 1 analyse, 24 predictions (POLARA)
FS	60 min	15 min	snow depth analysis and forecast, 1 analyse, 2 predictions
FQ	360 min	15 min	snow depth analysis and forecast, 1 analyse, 2 predictions

RV Product

fname = "../data/radvor/DE1200_RV2210180700.tar.bz2"
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 = wrl.io.open_radolan_mfdataset(buffer)
display(ds)

<xarray.Dataset> Size: 132MB
Dimensions:          (prediction_time: 25, y: 1200, x: 1100, time: 1)
Coordinates:
  * prediction_time  (prediction_time) datetime64[us] 200B 2022-10-18T07:00:0...
  * y                (y) float64 10kB -4.822e+06 -4.821e+06 ... -3.623e+06
  * x                (x) float64 9kB -5.432e+05 -5.422e+05 ... 5.558e+05
  * time             (time) datetime64[ns] 8B 2022-10-18T07:00:00
Data variables:
    RV               (prediction_time, y, x) float32 132MB dask.array<chunksize=(1, 1200, 1100), meta=np.ndarray>
    crs              (prediction_time) int64 200B 0 0 0 0 0 0 0 ... 0 0 0 0 0 0
Attributes:
    radarid:         10000
    formatversion:   5
    radolanversion:  P300001H
    radarlocations:  ['deasb', 'deboo', 'dedrs', 'deeis', 'deess', 'defbg', '...
    predictiontime:  0
    moduleflag:      8

xarray.Dataset

• Dimensions:
  • prediction_time: 25
  • y: 1200
  • x: 1100
  • time: 1
• Coordinates: (4)
  • prediction_time
    (prediction_time)
    datetime64[us]
    2022-10-18T07:00:00 ... 2022-10-...

    standard_name :

    time

    units :

    seconds since 1970-01-01T00:00:00Z

    array(['2022-10-18T07:00:00.000000', '2022-10-18T07:05:00.000000',
           '2022-10-18T07:10:00.000000', '2022-10-18T07:15:00.000000',
           '2022-10-18T07:20:00.000000', '2022-10-18T07:25:00.000000',
           '2022-10-18T07:30:00.000000', '2022-10-18T07:35:00.000000',
           '2022-10-18T07:40:00.000000', '2022-10-18T07:45:00.000000',
           '2022-10-18T07:50:00.000000', '2022-10-18T07:55:00.000000',
           '2022-10-18T08:00:00.000000', '2022-10-18T08:05:00.000000',
           '2022-10-18T08:10:00.000000', '2022-10-18T08:15:00.000000',
           '2022-10-18T08:20:00.000000', '2022-10-18T08:25:00.000000',
           '2022-10-18T08:30:00.000000', '2022-10-18T08:35:00.000000',
           '2022-10-18T08:40:00.000000', '2022-10-18T08:45:00.000000',
           '2022-10-18T08:50:00.000000', '2022-10-18T08:55:00.000000',
           '2022-10-18T09:00:00.000000'], dtype='datetime64[us]')

  • y
    (y)
    float64
    -4.822e+06 ... -3.623e+06

    units :

    m

    long_name :

    y coordinate of projection

    standard_name :

    projection_y_coordinate

    array([-4821588.861931, -4820588.861931, -4819588.861931, ..., -3624588.861931,
           -3623588.861931, -3622588.861931], shape=(1200,))

  • x
    (x)
    float64
    -5.432e+05 -5.422e+05 ... 5.558e+05

    units :

    m

    long_name :

    x coordinate of projection

    standard_name :

    projection_x_coordinate

    array([-543196.835218, -542196.835218, -541196.835218, ...,  553803.164782,
            554803.164782,  555803.164782], shape=(1100,))

  • time
    (time)
    datetime64[ns]
    2022-10-18T07:00:00

    standard_name :

    time

    array(['2022-10-18T07:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (2)
  • RV
    (prediction_time, y, x)
    float32
    dask.array<chunksize=(1, 1200, 1100), meta=np.ndarray>

    valid_min :

    0

    valid_max :

    4095

    standard_name :

    rainfall_rate

    long_name :

    RV

    unit :

    mm h-1

    grid_mapping :

    crs

    Array Chunk Bytes 125.89 MiB 5.04 MiB Shape (25, 1200, 1100) (1, 1200, 1100) Dask graph 25 chunks in 76 graph layers Data type float32 numpy.ndarray

  • crs
    (prediction_time)
    int64
    0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0

    crs_wkt :

    PROJCRS["Radolan Projection",BASEGEOGCRS["Radolan Coordinate System",DATUM["Unknown based on WGS 84 ellipsoid",ELLIPSOID["WGS 84",6378137,298.25722356301,LENGTHUNIT["metre",1,ID["EPSG",9001]]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8901]]],CONVERSION["unnamed",METHOD["Polar Stereographic (variant B)",ID["EPSG",9829]],PARAMETER["Latitude of standard parallel",60,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8832]],PARAMETER["Longitude of origin",10,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8833]],PARAMETER["False easting",0,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",0,LENGTHUNIT["metre",1],ID["EPSG",8807]]],CS[Cartesian,2],AXIS["easting",south,ORDER[1],LENGTHUNIT["metre",1,ID["EPSG",9001]]],AXIS["northing",south,ORDER[2],LENGTHUNIT["metre",1,ID["EPSG",9001]]]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.31424518

    inverse_flattening :

    298.25722356301

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    Radolan Coordinate System

    horizontal_datum_name :

    Unknown based on WGS 84 ellipsoid

    projected_crs_name :

    Radolan Projection

    grid_mapping_name :

    polar_stereographic

    standard_parallel :

    60.0

    straight_vertical_longitude_from_pole :

    10.0

    false_easting :

    0.0

    false_northing :

    0.0

    array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
           0, 0, 0])

• Attributes: (6)

  radarid :

  10000

  formatversion :

  5

  radolanversion :

  P300001H

  radarlocations :

  ['deasb', 'deboo', 'dedrs', 'deeis', 'deess', 'defbg', 'defld', 'dehnr', 'deisn', 'demem', 'deneu', 'denhb', 'deoft', 'depro', 'deros', 'detur', 'deumd']

  predictiontime :

  0

  moduleflag :

  8

ds.RV.plot(col="prediction_time", col_wrap=5, vmax=20)

<xarray.plot.facetgrid.FacetGrid at 0x7c7a9c578d70>

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

RE Product

This product isn’t implemented with all features, yet. Use with care!

files = [
    "../data/radvor/RE2210180700_120.gz",
    "../data/radvor/RE2210180700_115.gz",
    "../data/radvor/RE2210180700_110.gz",
    "../data/radvor/RE2210180700_105.gz",
    "../data/radvor/RE2210180700_100.gz",
    "../data/radvor/RE2210180700_095.gz",
    "../data/radvor/RE2210180700_090.gz",
    "../data/radvor/RE2210180700_085.gz",
    "../data/radvor/RE2210180700_080.gz",
    "../data/radvor/RE2210180700_075.gz",
    "../data/radvor/RE2210180700_070.gz",
    "../data/radvor/RE2210180700_065.gz",
    "../data/radvor/RE2210180700_060.gz",
    "../data/radvor/RE2210180700_055.gz",
    "../data/radvor/RE2210180700_050.gz",
    "../data/radvor/RE2210180700_045.gz",
    "../data/radvor/RE2210180700_040.gz",
    "../data/radvor/RE2210180700_035.gz",
    "../data/radvor/RE2210180700_030.gz",
    "../data/radvor/RE2210180700_025.gz",
    "../data/radvor/RE2210180700_020.gz",
    "../data/radvor/RE2210180700_015.gz",
    "../data/radvor/RE2210180700_010.gz",
    "../data/radvor/RE2210180700_005.gz",
    "../data/radvor/RE2210180700_000.gz",
]

ds = wrl.io.open_radolan_mfdataset(files)
display(ds)

<xarray.Dataset> Size: 81MB
Dimensions:          (prediction_time: 25, y: 900, x: 900, time: 1)
Coordinates:
  * prediction_time  (prediction_time) datetime64[us] 200B 2022-10-18T07:00:0...
  * y                (y) float64 7kB -4.672e+06 -4.671e+06 ... -3.773e+06
  * x                (x) float64 7kB -5.232e+05 -5.222e+05 ... 3.758e+05
  * time             (time) datetime64[ns] 8B 2022-10-18T07:00:00
Data variables:
    RE               (prediction_time, y, x) float32 81MB dask.array<chunksize=(1, 900, 900), meta=np.ndarray>
    crs              (prediction_time) int64 200B 0 0 0 0 0 0 0 ... 0 0 0 0 0 0
Attributes:
    radarid:         10000
    formatversion:   5
    radolanversion:  P300001H
    radarlocations:  ['deasb', 'deboo', 'dedrs', 'deeis', 'deess', 'defbg', '...
    predictiontime:  0
    moduleflag:      8
    quantification:  16

xarray.Dataset

• Dimensions:
  • prediction_time: 25
  • y: 900
  • x: 900
  • time: 1
• Coordinates: (4)
  • prediction_time
    (prediction_time)
    datetime64[us]
    2022-10-18T07:00:00 ... 2022-10-...

    standard_name :

    time

    units :

    seconds since 1970-01-01T00:00:00Z

    array(['2022-10-18T07:00:00.000000', '2022-10-18T07:05:00.000000',
           '2022-10-18T07:10:00.000000', '2022-10-18T07:15:00.000000',
           '2022-10-18T07:20:00.000000', '2022-10-18T07:25:00.000000',
           '2022-10-18T07:30:00.000000', '2022-10-18T07:35:00.000000',
           '2022-10-18T07:40:00.000000', '2022-10-18T07:45:00.000000',
           '2022-10-18T07:50:00.000000', '2022-10-18T07:55:00.000000',
           '2022-10-18T08:00:00.000000', '2022-10-18T08:05:00.000000',
           '2022-10-18T08:10:00.000000', '2022-10-18T08:15:00.000000',
           '2022-10-18T08:20:00.000000', '2022-10-18T08:25:00.000000',
           '2022-10-18T08:30:00.000000', '2022-10-18T08:35:00.000000',
           '2022-10-18T08:40:00.000000', '2022-10-18T08:45:00.000000',
           '2022-10-18T08:50:00.000000', '2022-10-18T08:55:00.000000',
           '2022-10-18T09:00:00.000000'], dtype='datetime64[us]')

  • y
    (y)
    float64
    -4.672e+06 ... -3.773e+06

    units :

    m

    long_name :

    y coordinate of projection

    standard_name :

    projection_y_coordinate

    array([-4671588.861931, -4670588.861931, -4669588.861931, ..., -3774588.861931,
           -3773588.861931, -3772588.861931], shape=(900,))

  • x
    (x)
    float64
    -5.232e+05 -5.222e+05 ... 3.758e+05

    units :

    m

    long_name :

    x coordinate of projection

    standard_name :

    projection_x_coordinate

    array([-523196.835218, -522196.835218, -521196.835218, ...,  373803.164782,
            374803.164782,  375803.164782], shape=(900,))

  • time
    (time)
    datetime64[ns]
    2022-10-18T07:00:00

    standard_name :

    time

    array(['2022-10-18T07:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (2)
  • RE
    (prediction_time, y, x)
    float32
    dask.array<chunksize=(1, 900, 900), meta=np.ndarray>

    valid_min :

    0

    valid_max :

    4095

    standard_name :

    solid_fraction_of_precipitation

    long_name :

    RE

    unit :

    1

    grid_mapping :

    crs

    Array Chunk Bytes 77.25 MiB 3.09 MiB Shape (25, 900, 900) (1, 900, 900) Dask graph 25 chunks in 76 graph layers Data type float32 numpy.ndarray

  • crs
    (prediction_time)
    int64
    0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0

    crs_wkt :

    PROJCRS["Radolan Projection",BASEGEOGCRS["Radolan Coordinate System",DATUM["Unknown based on WGS 84 ellipsoid",ELLIPSOID["WGS 84",6378137,298.25722356301,LENGTHUNIT["metre",1,ID["EPSG",9001]]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8901]]],CONVERSION["unnamed",METHOD["Polar Stereographic (variant B)",ID["EPSG",9829]],PARAMETER["Latitude of standard parallel",60,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8832]],PARAMETER["Longitude of origin",10,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8833]],PARAMETER["False easting",0,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",0,LENGTHUNIT["metre",1],ID["EPSG",8807]]],CS[Cartesian,2],AXIS["easting",south,ORDER[1],LENGTHUNIT["metre",1,ID["EPSG",9001]]],AXIS["northing",south,ORDER[2],LENGTHUNIT["metre",1,ID["EPSG",9001]]]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.31424518

    inverse_flattening :

    298.25722356301

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    Radolan Coordinate System

    horizontal_datum_name :

    Unknown based on WGS 84 ellipsoid

    projected_crs_name :

    Radolan Projection

    grid_mapping_name :

    polar_stereographic

    standard_parallel :

    60.0

    straight_vertical_longitude_from_pole :

    10.0

    false_easting :

    0.0

    false_northing :

    0.0

    array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
           0, 0, 0])

• Attributes: (7)

  radarid :

  10000

  formatversion :

  5

  radolanversion :

  P300001H

  radarlocations :

  ['deasb', 'deboo', 'dedrs', 'deeis', 'deess', 'defbg', 'defld', 'dehnr', 'deisn', 'demem', 'deneu', 'denhb', 'deoft', 'depro', 'deros', 'detur', 'deumd']

  predictiontime :

  0

  moduleflag :

  8

  quantification :

  16

ds.RE.plot(col="prediction_time", col_wrap=5, vmax=2)

<xarray.plot.facetgrid.FacetGrid at 0x7c7aa0badf90>

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

RQ Product

files = [
    "../data/radvor/RQ2210180700_120.gz",
    "../data/radvor/RQ2210180700_060.gz",
    "../data/radvor/RQ2210180700_000.gz",
]

ds = wrl.io.open_radolan_mfdataset(files)
display(ds)

<xarray.Dataset> Size: 10MB
Dimensions:          (prediction_time: 3, y: 900, x: 900, time: 1)
Coordinates:
  * prediction_time  (prediction_time) datetime64[us] 24B 2022-10-18T07:00:00...
  * y                (y) float64 7kB -4.672e+06 -4.671e+06 ... -3.773e+06
  * x                (x) float64 7kB -5.232e+05 -5.222e+05 ... 3.758e+05
  * time             (time) datetime64[ns] 8B 2022-10-18T07:00:00
Data variables:
    RQ               (prediction_time, y, x) float32 10MB dask.array<chunksize=(1, 900, 900), meta=np.ndarray>
    crs              (prediction_time) int64 24B 0 0 0
Attributes:
    radarid:         10000
    formatversion:   5
    radolanversion:  2.29.1
    radarlocations:  ['asb', 'boo', 'drs', 'eis', 'ess', 'fbg', 'fld', 'hnr',...
    predictiontime:  0
    moduleflag:      8
    quantification:  0

xarray.Dataset

• Dimensions:
  • prediction_time: 3
  • y: 900
  • x: 900
  • time: 1
• Coordinates: (4)
  • prediction_time
    (prediction_time)
    datetime64[us]
    2022-10-18T07:00:00 ... 2022-10-...

    standard_name :

    time

    units :

    seconds since 1970-01-01T00:00:00Z

    array(['2022-10-18T07:00:00.000000', '2022-10-18T08:00:00.000000',
           '2022-10-18T09:00:00.000000'], dtype='datetime64[us]')

  • y
    (y)
    float64
    -4.672e+06 ... -3.773e+06

    units :

    m

    long_name :

    y coordinate of projection

    standard_name :

    projection_y_coordinate

    array([-4671588.861931, -4670588.861931, -4669588.861931, ..., -3774588.861931,
           -3773588.861931, -3772588.861931], shape=(900,))

  • x
    (x)
    float64
    -5.232e+05 -5.222e+05 ... 3.758e+05

    units :

    m

    long_name :

    x coordinate of projection

    standard_name :

    projection_x_coordinate

    array([-523196.835218, -522196.835218, -521196.835218, ...,  373803.164782,
            374803.164782,  375803.164782], shape=(900,))

  • time
    (time)
    datetime64[ns]
    2022-10-18T07:00:00

    standard_name :

    time

    array(['2022-10-18T07:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (2)
  • RQ
    (prediction_time, y, x)
    float32
    dask.array<chunksize=(1, 900, 900), meta=np.ndarray>

    valid_min :

    0

    valid_max :

    4095

    standard_name :

    rainfall_rate

    long_name :

    RQ

    unit :

    mm h-1

    grid_mapping :

    crs

    Array Chunk Bytes 9.27 MiB 3.09 MiB Shape (3, 900, 900) (1, 900, 900) Dask graph 3 chunks in 10 graph layers Data type float32 numpy.ndarray

  • crs
    (prediction_time)
    int64
    0 0 0

    crs_wkt :

    PROJCRS["Radolan Projection",BASEGEOGCRS["Radolan Coordinate System",DATUM["Unknown based on WGS 84 ellipsoid",ELLIPSOID["WGS 84",6378137,298.25722356301,LENGTHUNIT["metre",1,ID["EPSG",9001]]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8901]]],CONVERSION["unnamed",METHOD["Polar Stereographic (variant B)",ID["EPSG",9829]],PARAMETER["Latitude of standard parallel",60,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8832]],PARAMETER["Longitude of origin",10,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8833]],PARAMETER["False easting",0,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",0,LENGTHUNIT["metre",1],ID["EPSG",8807]]],CS[Cartesian,2],AXIS["easting",south,ORDER[1],LENGTHUNIT["metre",1,ID["EPSG",9001]]],AXIS["northing",south,ORDER[2],LENGTHUNIT["metre",1,ID["EPSG",9001]]]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.31424518

    inverse_flattening :

    298.25722356301

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    Radolan Coordinate System

    horizontal_datum_name :

    Unknown based on WGS 84 ellipsoid

    projected_crs_name :

    Radolan Projection

    grid_mapping_name :

    polar_stereographic

    standard_parallel :

    60.0

    straight_vertical_longitude_from_pole :

    10.0

    false_easting :

    0.0

    false_northing :

    0.0

    array([0, 0, 0])

• Attributes: (7)

  radarid :

  10000

  formatversion :

  5

  radolanversion :

  2.29.1

  radarlocations :

  ['asb', 'boo', 'drs', 'eis', 'ess', 'fbg', 'fld', 'hnr', 'isn', 'mem', 'neu', 'nhb', 'oft', 'pro', 'ros', 'tur', 'umd']

  predictiontime :

  0

  moduleflag :

  8

  quantification :

  0

ds.RQ.plot(col="prediction_time")

<xarray.plot.facetgrid.FacetGrid at 0x7c7a940ff390>

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