Pena (2025) Dataset of Extreme Rainfall Quantiles over Italy from Six Satellite and Reanalysis Products Using GEV and MEVD

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Identification

• Journal: Open MIND
• Year: 2025
• Date: 2025-10-07
• Authors: Cesar Arturo Sánchez Pena
• DOI: 10.5281/zenodo.18607063

Research Groups

University of Udine (INTENSE project)

Short Summary

This dataset provides spatial maps of extreme daily precipitation quantiles over Italy, derived from six Remote Sensing and Reanalysis products using GEV and MEVD statistical approaches, including a novel downscaling method to estimate point-scale extremes.

Objective

• To estimate and map extreme daily precipitation quantiles over Italy using various Remote Sensing and Reanalysis products and advanced statistical methods, including a downscaling approach to bridge the gap between spatially averaged and point-scale estimates.

Study Configuration

• Spatial Scale: Italy, at native spatial resolutions of the Remote Sensing and Reanalysis products, and downscaled to point scale.
• Temporal Scale: January 2002 to December 2023 (22 years).

Methodology and Data

• Models used: Generalized Extreme Value (GEV) distribution, Metastatistical Extreme Value Distribution (MEVD), and a stochastic downscaling method for extreme-value statistics grounded in random field theory.
• Data sources: Remote Sensing products (IMERG, CMORPH, MSWEP, GSMaP, CHIRPS) and Reanalysis product (ERA5).

Main Results

• A dataset comprising 72 georeferenced raster maps (.tiff format) of extreme daily precipitation quantiles (in mm/day) over Italy.
• Quantiles are estimated for four return periods (10, 50, 100, and 200 years).
• Maps are derived using GEV at native resolution (24 maps), MEVD at native resolution (24 maps), and MEVD at point scale via downscaling (24 maps) from six different Remote Sensing and Reanalysis products.

Contributions

• Provides a comprehensive and spatially explicit dataset of extreme daily precipitation quantiles for Italy, crucial for hydrological and risk assessment studies.
• Applies and demonstrates a stochastic downscaling method for extreme-value statistics, effectively bridging the gap between spatially averaged satellite/reanalysis estimates and point-scale rainfall statistics.

Funding

• "raINfall exTremEs and their impacts: from the local to the National ScalE" (INTENSE) project, funded by the European Union – Next Generation EU within the framework of the PRIN (Progetti di ricerca di Rilevante Interesse Nazionale) programme (grant 2022ZC2522).

Citation

@article{Pena2025Dataset,
  author = {Pena, Cesar Arturo Sánchez},
  title = {Dataset of Extreme Rainfall Quantiles over Italy from Six Satellite and Reanalysis Products Using GEV and MEVD},
  journal = {Open MIND},
  year = {2025},
  doi = {10.5281/zenodo.18607063},
  url = {https://doi.org/10.5281/zenodo.18607063}
}