DMI - Radar reflectivity over Denmark

This notebook describes how to access and use the zarr-version of the DMI radar composite dataset. The dataset includes ten-minute reflectivity over Denmark, which is obtained by the five radars owned by the Danish Meteorologiske Institute (DMI). The v0.1.1 zarr version of thre dataset covers 2016 to the end of 2024.

The conversion of the radar composite was done by the code hosted on mlcast-community/mlcast-dataset-DMI

import matplotlib.pyplot as plt
import cartopy.crs as ccrs

import mlcast_datasets

cat = mlcast_datasets.open_catalog()
list(cat.precipitation)

['radklim_hourly',
 'radklim_5_minutes',
 'dmi_10_minutes',
 'it_dpc_sri_5min',
 'uk_metoffice_5min',
 'be_rmi_radclim_mfb_5min']

DMI 10-min reflectivity

Mlcast catalog contains multiple datasets, hence to call specifically for DMI radar data:

ds = cat.precipitation.dmi_10_minutes.to_dask()
ds

<xarray.Dataset> Size: 14TB
Dimensions:  (time: 517536, y: 1728, x: 1984)
Coordinates:
  * time     (time) datetime64[ns] 4MB 2016-02-29 ... 2025-12-31T23:50:00
  * y        (y) float64 14kB -3.941e+05 -3.936e+05 ... 4.689e+05 4.694e+05
  * x        (x) float64 16kB -4.221e+05 -4.216e+05 ... 5.689e+05 5.694e+05
    lat      (y, x) float64 27MB dask.array<chunksize=(1728, 1984), meta=np.ndarray>
    lon      (y, x) float64 27MB dask.array<chunksize=(1728, 1984), meta=np.ndarray>
Data variables:
    crs      float64 8B ...
    dbz      (time, y, x) float64 14TB dask.array<chunksize=(1, 1728, 1984), meta=np.ndarray>
Attributes:
    Author:                     Thomas Bøvith (tbh@dmi.dk)
    Conventions:                CF-1.6
    consistent_timestep_start:  2016-02-29T00:00:00.000000000
    institution:                Danmarks Meteorologiske Institute (DMI)
    license:                    CC-BY-4.0
    mlcast_created_by:          Ricardo Jara <arjj@dmi.dk>
    mlcast_created_on:          2026-02-24T09:00:17.521323
    mlcast_created_with:        https://github.com/mlcast-community/mlcast-da...
    mlcast_dataset_identifier:  DK-DMI-precipitation
    mlcast_dataset_version:     0.1.1
    title:                      radar-based precipitation

xarray.Dataset

• Dimensions:
  • time: 517536
  • y: 1728
  • x: 1984
• Coordinates: (5)
  • time
    (time)
    datetime64[ns]
    2016-02-29 ... 2025-12-31T23:50:00

    long_name :

    Time

    standard_name :

    time

    array(['2016-02-29T00:00:00.000000000', '2016-02-29T00:10:00.000000000',
           '2016-02-29T00:20:00.000000000', ..., '2025-12-31T23:30:00.000000000',
           '2025-12-31T23:40:00.000000000', '2025-12-31T23:50:00.000000000'],
          shape=(517536,), dtype='datetime64[ns]')

  • y
    (y)
    float64
    -3.941e+05 -3.936e+05 ... 4.694e+05

    units :

    m

    array([-394118.987724, -393618.987724, -393118.987723, ...,  468381.012955,
            468881.012956,  469381.012956], shape=(1728,))

  • x
    (x)
    float64
    -4.221e+05 -4.216e+05 ... 5.694e+05

    units :

    m

    array([-422114.801239, -421614.801239, -421114.801239, ...,  568385.19853 ,
            568885.19853 ,  569385.19853 ], shape=(1984,))

  • lat
    (y, x)
    float64
    dask.array<chunksize=(1728, 1984), meta=np.ndarray>

    long_name :

    Latitude

    standard_name :

    latitude

    units :

    degrees_north

    Array Chunk Bytes 26.16 MiB 26.16 MiB Shape (1728, 1984) (1728, 1984) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lon
    (y, x)
    float64
    dask.array<chunksize=(1728, 1984), meta=np.ndarray>

    long_name :

    Longitude

    standard_name :

    longitude

    units :

    degrees_east

    Array Chunk Bytes 26.16 MiB 26.16 MiB Shape (1728, 1984) (1728, 1984) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• Data variables: (2)
  • crs
    ()
    float64
    ...

    crs_wkt :

    PROJCRS["unknown",BASEGEOGCRS["unknown",DATUM["Unknown based on WGS 84 ellipsoid",ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1],ID["EPSG",7030]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8901]]],CONVERSION["unknown",METHOD["Stereographic"],PARAMETER["Latitude of natural origin",56,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8801]],PARAMETER["Longitude of natural origin",10.5666,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8802]],PARAMETER["Scale factor at natural origin",1,SCALEUNIT["unity",1],ID["EPSG",8805]],PARAMETER["False easting",0,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",0,LENGTHUNIT["metre",1],ID["EPSG",8807]]],CS[Cartesian,2],AXIS["(E)",east,ORDER[1],LENGTHUNIT["metre",1,ID["EPSG",9001]]],AXIS["(N)",north,ORDER[2],LENGTHUNIT["metre",1,ID["EPSG",9001]]],REMARK["PROJ CRS string: +proj=stere +ellps=WGS84 +lat_0=56 +lon_0=10.5666 +lat_ts=56"], BBOX[52.15916120112653, 2.999999998212397, 60.21187595130615, 20.735140169999998]]

    proj4 :

    +proj=stere +ellps=WGS84 +lat_0=56 +lon_0=10.5666 +lat_ts=56

    spatial_ref :

    PROJCRS["unknown",BASEGEOGCRS["unknown",DATUM["Unknown based on WGS 84 ellipsoid",ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1],ID["EPSG",7030]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8901]]],CONVERSION["unknown",METHOD["Stereographic"],PARAMETER["Latitude of natural origin",56,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8801]],PARAMETER["Longitude of natural origin",10.5666,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8802]],PARAMETER["Scale factor at natural origin",1,SCALEUNIT["unity",1],ID["EPSG",8805]],PARAMETER["False easting",0,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",0,LENGTHUNIT["metre",1],ID["EPSG",8807]]],CS[Cartesian,2],AXIS["(E)",east,ORDER[1],LENGTHUNIT["metre",1,ID["EPSG",9001]]],AXIS["(N)",north,ORDER[2],LENGTHUNIT["metre",1,ID["EPSG",9001]]],REMARK["PROJ CRS string: +proj=stere +ellps=WGS84 +lat_0=56 +lon_0=10.5666 +lat_ts=56"]]

    [1 values with dtype=float64]

  • dbz
    (time, y, x)
    float64
    dask.array<chunksize=(1, 1728, 1984), meta=np.ndarray>

    grid_mapping :

    crs

    long_name :

    10 min radar reflectivity

    standard_name :

    equivalent_reflectivity_factor

    units :

    dBZ

    Array Chunk Bytes 12.91 TiB 26.16 MiB Shape (517536, 1728, 1984) (1, 1728, 1984) Dask graph 517536 chunks in 2 graph layers Data type float64 numpy.ndarray

• Attributes: (11)

  Author :

  Thomas Bøvith (tbh@dmi.dk)

  Conventions :

  CF-1.6

  consistent_timestep_start :

  2016-02-29T00:00:00.000000000

  institution :

  Danmarks Meteorologiske Institute (DMI)

  license :

  CC-BY-4.0

  mlcast_created_by :

  Ricardo Jara <arjj@dmi.dk>

  mlcast_created_on :

  2026-02-24T09:00:17.521323

  mlcast_created_with :

  https://github.com/mlcast-community/mlcast-dataset-DMI-radar_precipitation@0.1.1

  mlcast_dataset_identifier :

  DK-DMI-precipitation

  mlcast_dataset_version :

  0.1.1

  title :

  radar-based precipitation

var_name = "dbz"

crs_name = ds[var_name].grid_mapping
data_crs = ccrs.Projection(ds[crs_name].crs_wkt)

g = (
    ds[var_name]
    .sel(time="2024-12-31")
    .isel(time=slice(None, 2))
    .plot(
        transform=data_crs,
        cmap="viridis",
        add_colorbar=True,
        col="time",
        robust=True,
        subplot_kws=dict(projection=data_crs),
    )
)

for ax in g.axes.flat:
    ax.coastlines()
    ax.gridlines(draw_labels=["top", "left"])

/tmp/ipykernel_2515/1860960605.py:15: FutureWarning: self.axes is deprecated since 2022.11 in order to align with matplotlibs plt.subplots, use self.axs instead.
  for ax in g.axes.flat: