IT-DPC - Surface Rainfall Intensity over Italy

This notebook describes how to access and use the zarr-version of the Italian DPC SRI dataset. The dataset includes 5-minute Surface Rainfall Intensity (SRI) measurements in mm/h at 1 km x 1 km spatial resolution over Italy, derived from the national radar network (23 radars) operated by the Italian Department of Civil Protection (Dipartimento Protezione Civile). The v0.1.0 zarr version of the dataset covers 2010 to the end of 2025.

The conversion of the radar data was done by the code hosted on mlcast-community/mlcast-dataset-IT-DPC-SRI.

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']

IT-DPC 5-min Surface Rainfall Intensity

The Italian DPC SRI dataset is available in the intake catalog as it_dpc_sri_5min.

ds = cat.precipitation.it_dpc_sri_5min.to_dask()
ds

<xarray.Dataset> Size: 7TB
Dimensions:        (time: 1039785, y: 1400, x: 1200, missing_times: 15530)
Coordinates:
  * time           (time) datetime64[ns] 8MB 2010-01-01 ... 2025-12-31T23:55:00
  * y              (y) float64 11kB 6.495e+05 6.485e+05 ... -7.495e+05
  * x              (x) float64 10kB -5.995e+05 -5.985e+05 ... 5.995e+05
    lat            (y, x) float64 13MB dask.array<chunksize=(1400, 1200), meta=np.ndarray>
    lon            (y, x) float64 13MB dask.array<chunksize=(1400, 1200), meta=np.ndarray>
  * missing_times  (missing_times) datetime64[ns] 124kB 2010-01-03T00:30:00 ....
Data variables:
    RR             (time, y, x) float32 7TB dask.array<chunksize=(1, 1400, 1200), meta=np.ndarray>
    crs            float32 4B ...
Attributes: (12/13)
    Author:                     Gabriele Franch
    Copyright:                  Dipartimento Protezione Civile (DPC) Nazional...
    Processed by:               Fondazione Bruno Kessler
    base_frequencies:           15min:2010-01-01T00:00/2014-06-25T09:00;10min...
    consistent_timestep_start:  2020-06-30T00:00
    history:                    Created at 2026-02-11T18:15:05+01:00
    ...                         ...
    mlcast_created_by:          Gabriele Franch <franch@fbk.eu>
    mlcast_created_on:          2026-02-11T18:15:05+01:00
    mlcast_created_with:        https://github.com/mlcast-community/mlcast-da...
    mlcast_dataset_identifier:  IT-DPC-SRI
    mlcast_dataset_version:     0.1.0
    title:                      Italian Radar DPC Surface Rainfall Intensity ...

xarray.Dataset

• Dimensions:
  • time: 1039785
  • y: 1400
  • x: 1200
  • missing_times: 15530
• Coordinates: (6)
  • time
    (time)
    datetime64[ns]
    2010-01-01 ... 2025-12-31T23:55:00

    long_name :

    Time

    standard_name :

    time

    array(['2010-01-01T00:00:00.000000000', '2010-01-01T00:15:00.000000000',
           '2010-01-01T00:30:00.000000000', ..., '2025-12-31T23:45:00.000000000',
           '2025-12-31T23:50:00.000000000', '2025-12-31T23:55:00.000000000'],
          shape=(1039785,), dtype='datetime64[ns]')

  • y
    (y)
    float64
    6.495e+05 6.485e+05 ... -7.495e+05

    units :

    m

    array([ 649500.,  648500.,  647500., ..., -747500., -748500., -749500.],
          shape=(1400,))

  • x
    (x)
    float64
    -5.995e+05 -5.985e+05 ... 5.995e+05

    units :

    m

    array([-599500., -598500., -597500., ...,  597500.,  598500.,  599500.],
          shape=(1200,))

  • lat
    (y, x)
    float64
    dask.array<chunksize=(1400, 1200), meta=np.ndarray>

    grid_mapping :

    crs

    long_name :

    Latitude

    standard_name :

    latitude

    units :

    degrees_north

    Array Chunk Bytes 12.82 MiB 12.82 MiB Shape (1400, 1200) (1400, 1200) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lon
    (y, x)
    float64
    dask.array<chunksize=(1400, 1200), meta=np.ndarray>

    grid_mapping :

    crs

    long_name :

    Longitude

    standard_name :

    longitude

    units :

    degrees_east

    Array Chunk Bytes 12.82 MiB 12.82 MiB Shape (1400, 1200) (1400, 1200) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • missing_times
    (missing_times)
    datetime64[ns]
    2010-01-03T00:30:00 ... 2025-12-...

    array(['2010-01-03T00:30:00.000000000', '2010-01-07T12:45:00.000000000',
           '2010-01-07T13:15:00.000000000', ..., '2025-12-04T08:50:00.000000000',
           '2025-12-04T08:55:00.000000000', '2025-12-04T19:25:00.000000000'],
          shape=(15530,), dtype='datetime64[ns]')

• Data variables: (2)
  • RR
    (time, y, x)
    float32
    dask.array<chunksize=(1, 1400, 1200), meta=np.ndarray>

    grid_mapping :

    crs

    long_name :

    Total precipitation rate

    standard_name :

    rainfall_flux

    units :

    kg m-2 h-1

    Array Chunk Bytes 6.35 TiB 6.41 MiB Shape (1039785, 1400, 1200) (1, 1400, 1200) Dask graph 1039785 chunks in 2 graph layers Data type float32 numpy.ndarray

  • crs
    ()
    float32
    ...

    AREA_OR_POINT :

    Area

    Conventions :

    ODIM_H5/V2_1

    GeoTransform :

    -600000.0 1000.0 0.0 650000.0 0.0 -1000.0

    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",9122]]]],CONVERSION["Transverse Mercator",METHOD["Transverse Mercator",ID["EPSG",9807]],PARAMETER["Latitude of natural origin",42,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8801]],PARAMETER["Longitude of natural origin",12.5,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["easting",east,ORDER[1],LENGTHUNIT["metre",1,ID["EPSG",9001]]],AXIS["northing",north,ORDER[2],LENGTHUNIT["metre",1,ID["EPSG",9001]]],BBOX[35.062255859375,4.51987266540527,47.5729560852051,20.4801273345947]]

    dataset1_data1_quality1_what_gain :

    0.393701

    dataset1_data1_quality1_what_nodata :

    0

    dataset1_data1_quality1_what_offset :

    0

    dataset1_data1_quality1_what_quantity :

    QIND

    dataset1_data1_quality1_what_undetect :

    -1

    dataset1_data1_what_nodata :

    -9999

    dataset1_data1_what_quantity :

    RATE

    dataset1_what_nodata :

    -9999

    dataset1_what_product :

    SURF

    dataset1_what_quantity :

    RATE

    false_easting :

    0.0

    false_northing :

    0.0

    geographic_crs_name :

    unknown

    grid_mapping_name :

    transverse_mercator

    horizontal_datum_name :

    Unknown based on WGS 84 ellipsoid

    inverse_flattening :

    298.257223563

    latitude_of_projection_origin :

    42.0

    longitude_of_central_meridian :

    12.5

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    proj4 :

    +proj=tmerc +lat_0=42 +lon_0=12.5 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +units=m +no_defs +type=crs

    projected_crs_name :

    unknown

    reference_ellipsoid_name :

    WGS 84

    scale_factor_at_central_meridian :

    1.0

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    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",9122]]]],CONVERSION["Transverse Mercator",METHOD["Transverse Mercator",ID["EPSG",9807]],PARAMETER["Latitude of natural origin",42,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8801]],PARAMETER["Longitude of natural origin",12.5,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["easting",east,ORDER[1],LENGTHUNIT["metre",1,ID["EPSG",9001]]],AXIS["northing",north,ORDER[2],LENGTHUNIT["metre",1,ID["EPSG",9001]]],BBOX[35.062255859375,4.51987266540527,47.5729560852051,20.4801273345947]]

    where_LL_lat :

    35.062255859375

    where_LL_lon :

    5.92472267150879

    where_LR_lat :

    35.062255859375

    where_LR_lon :

    19.0752773284912

    where_UL_lat :

    47.5729560852051

    where_UL_lon :

    4.51987266540527

    where_UR_lat :

    47.5729560852051

    where_UR_lon :

    20.4801273345947

    where_xscale :

    1000

    where_xsize :

    1200

    where_yscale :

    1000

    where_ysize :

    1400

    [1 values with dtype=float32]

• Attributes: (13)

  Author :

  Gabriele Franch

  Copyright :

  Dipartimento Protezione Civile (DPC) Nazionale rete RADAR

  Processed by :

  Fondazione Bruno Kessler

  base_frequencies :

  15min:2010-01-01T00:00/2014-06-25T09:00;10min:2014-06-25T09:00/2020-06-30T00:00;5min:2020-06-30T00:00/2025-12-31T23:59:00.000000000

  consistent_timestep_start :

  2020-06-30T00:00

  history :

  Created at 2026-02-11T18:15:05+01:00

  license :

  CC-BY-SA-4.0

  mlcast_created_by :

  Gabriele Franch <franch@fbk.eu>

  mlcast_created_on :

  2026-02-11T18:15:05+01:00

  mlcast_created_with :

  https://github.com/mlcast-community/mlcast-dataset-IT-DPC-SRI@0.2.0

  mlcast_dataset_identifier :

  IT-DPC-SRI

  mlcast_dataset_version :

  0.1.0

  title :

  Italian Radar DPC Surface Rainfall Intensity (SRI) Archive

var_name = "RR"

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

g = (
    ds[var_name]
    .sel(time="2023-07-01")
    .isel(time=slice(None, 3))
    .plot(
        transform=data_crs,
        cmap="viridis",
        add_colorbar=True,
        col="time",
        robust=True,
        subplot_kws=dict(projection=data_crs),
        vmin=0,
    )
)

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

/tmp/ipykernel_2649/891365573.py:21: 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: