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IT-DPC-SRI: A Cloud-Optimized Archive of Italian Radar Precipitation (2010-2025)

Gabriele Franch, Elena Tomasi, Uladzislau Azhel, Giacomo Tomezzoli, Alessandro Camilletti, Virginia Poli, Renata Pelosini, Gianfranco Vulpiani, Gabriella Scipione, Giuseppe Trotta, Matteo Angelinelli, Leif Denby, Irene Livia Kruse, Marco Cristoforetti

Abstract

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 $1200\times1400$ 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.

IT-DPC-SRI: A Cloud-Optimized Archive of Italian Radar Precipitation (2010-2025)

Abstract

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 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.
Paper Structure (32 sections, 7 figures, 4 tables)

This paper contains 32 sections, 7 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Data Source and Original Products
  3. The Italian National Radar Network
  4. The Surface Rainfall Intensity Product
  5. Historical Data Formats and Challenges
  6. Dataset Description
  7. Spatial and Temporal Coverage
  8. Coordinate Reference System
  9. Variables and Units
  10. File Format and Structure
  11. Variable Attributes and Grid Mapping
  12. Data Processing and Harmonization
  13. Source Data Collection
  14. Spatial Harmonization
  15. Temporal Alignment
  16. ...and 17 more sections

Figures (7)

  • 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$^{-1}$.
  • ...and 2 more figures