We present IT-DPC-SRI, the first publicly available long-term archive of Italian weather radar precipitation estimates, spanning 16 years (2010--2025). The dataset contains Surface Rainfall Intensity (SRI) observations from the Italian Civil Protection Department's national radar mosaic, harmonized into a coherent Analysis-Ready Cloud-Optimized (ARCO) Zarr datacube. The archive comprises over one million timesteps at temporal resolutions from 15 to 5 minutes, covering a 1200×1400 kilometer domain at 1 kilometer spatial resolution, compressed from 7TB to 51GB on disk. We address the historical fragmentation of Italian radar data - previously scattered across heterogeneous formats (OPERA BUFR, HDF5, GeoTIFF) with varying spatial domains and projections - by reprocessing the entire record into a unified store. The dataset is accessible as a static versioned snapshot on Zenodo, via cloud-native access on the ECMWF European Weather Cloud, and as a continuously updated live version on the ArcoDataHub platform. This release fills a significant gap in European radar data availability, as Italy does not participate in the EUMETNET OPERA pan-European radar composite. The dataset is released under a CC BY-SA 4.0 license.
Figures & tables
Parameter
Value
Projection
Transverse Mercator
Latitude of origin
42\SIUnitSymbolDegree N
Central meridian
12.5\SIUnitSymbolDegree E
Scale factor
1.0
False easting
0m
False northing
0m
Table 1: Coordinate Reference System Parameters
Variable
Dimensions
Chunks
RR
(time, y, x)
(1, 1400, 1200)
time
(time,)
(1039785,)
x
(x,)
(1200,)
y
(y,)
(1400,)
lat
(y, x)
(1400, 1200)
lon
(y, x)
(1400, 1200)
Table 2: Zarr Store Structure
Variable
Attribute
Value
RR
long_name
Total precipitation rate
RR
standard_name
rainfall_flux
RR
units
kg m -2 h -1
RR
grid_mapping
crs
RR
_FillValue
NaN
RR
compressor
Blosc (Zstd, level 9)
Table 3: Key Variable Attributes
Figure 1: Spatial domain of the IT-DPC-SRI dataset. The shaded region shows the radar coverage footprint (grid cells with at least one valid observation). Country borders and coastlines from Natural Earth (10 m resolution).
Figure 2: Temporal coverage of the dataset. Top: year-by-month completeness heatmap (fraction of expected timesteps present). Bottom: total timesteps per year, colored by temporal frequency era (15 min in blue, 10 min in orange, 5 min in green).
Figure 3: Fraction of valid (non-NaN) observations per grid cell, computed from 1000 uniformly spaced timesteps. Contour lines are drawn at 0.5 and 0.9 fractional coverage.
Figure 4: Six consecutive precipitation frames from the Emilia-Romagna flood event (May 16–17, 2023). The panels show precipitation rate in mm h -1 at 3-hour intervals, illustrating the evolution and fine spatial structure of intense rainfall.
Figure 5: Spatial maps of precipitation statistics computed from 2000 sampled timesteps. (a) Temporal mean, (b) maximum observed rate, (c) standard deviation. All values in mm h -1 .
Figure 6: Log–log probability density of non-zero precipitation values (mm h -1 ), computed from 2000 sampled timesteps.
Figure 7: Monthly distribution of domain-mean precipitation rate (mm h -1 ), based on 3000 sampled timesteps. Boxes show the interquartile range with median line; whiskers extend to 1.5 × IQR. Colors indicate meteorological seasons: winter (blue), spring (green), summer (orange), autumn (red).
May 9, 2026·Matheus Lima Castro, William Dantas Vichete, Leopoldo Lusquino Filho
São Paulo State University (UNESP), Sorocaba, Brazil. · HairU Serviços e Tecnologia LTDA, Sorocaba, Brazil. · RECOD.ai, University of Campinas (UNICAMP), Campinas, Brazil.