Preprints
https://doi.org/10.5194/essd-2025-156
https://doi.org/10.5194/essd-2025-156
09 Apr 2025
 | 09 Apr 2025
Status: this preprint is currently under review for the journal ESSD.

Development of HYPER-P: HYdroclimatic PERformance-enhanced Precipitation at 1 km/daily over the Europe-Mediterranean region from 2007 to 2022

Paolo Filippucci, Luca Brocca, Luca Ciabatta, Hamidreza Mosaffa, Francesco Avanzi, and Christian Massari

Abstract. Accurate precipitation estimates are essential for a wide range of applications, including climate research, water resource management, agriculture, and natural hazard assessment. However, developing high-quality, long-term daily datasets at fine spatial resolutions remains challenging due to the inherent variability and heterogeneity of precipitation patterns. This study introduces the HYdroclimatic PERformance-enhanced Precipitation (HYPER-P) product, covering Europe and part of the Mediterranean basin from 2007 to 2022 at a 1 km daily resolution. HYPER-P is derived by downscaling and merging multiple data sources, including remote sensing products from Top-Down (TD) and Bottom-Up (BU) approaches, reanalysis datasets, and gridded in situ observations. The downscaling leverages on CHELSA climatology data, while the merging is obtained through a weighted average approach informed by Triple Collocation Analysis.

Four merged products were developed based on multiple combinations of satellite products, observation and reanalysis datasets. The evaluation of these products was conducted through high-resolution validation in three Mediterranean regions with dense observational networks and coarse-resolution validation across Europe and a portion of North Africa. Results indicate that the combination of TD and BU satellite approaches enhance precipitation estimates, with merged products outperforming the parent datasets, especially in regions with sparse gauge coverage. The inclusion of ERA5-Land further improves accuracy over areas characterized by complex topography. The merging of satellite products, particularly the one including ERA5-Land, shows overall strong performance, although challenges remain in validating precipitation estimates where ground observations are limited. This work contributes to advancing precipitation monitoring capabilities, offering valuable tools for scientific and operational applications across Europe and beyond.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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Paolo Filippucci, Luca Brocca, Luca Ciabatta, Hamidreza Mosaffa, Francesco Avanzi, and Christian Massari

Status: open (until 16 May 2025)

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Paolo Filippucci, Luca Brocca, Luca Ciabatta, Hamidreza Mosaffa, Francesco Avanzi, and Christian Massari

Data sets

4DMED precipitation product: 1 km merged precipitation from CPC, IMERG-LR for mediterranean basin P. Filippucci et al. https://doi.org/10.5281/zenodo.15025397

4DMED precipitation product: 1 km merged precipitation from CPC, IMERG-LR and SM2RAIN-ASCAT for mediterranean basin P. Filippucci et al. https://doi.org/10.5281/zenodo.10402392

4DHydro precipitation product: 1 km merged precipitation from IMERG-LR and SM2RAIN-ASCAT for Europe P. Filippucci et al. https://doi.org/10.5281/zenodo.15025462

4DHydro precipitation product: 1 km merged precipitation from ERA5-Land, IMERG-LR and SM2RAIN-ASCAT for Europe/HYPER-P: HYdroclimatic PERformance-enhanced Precipitation at 1 km/daily over the Europe region from 2007 to 2022 P. Filippucci et al. https://doi.org/10.5281/zenodo.15025514

Paolo Filippucci, Luca Brocca, Luca Ciabatta, Hamidreza Mosaffa, Francesco Avanzi, and Christian Massari

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Short summary
Accurate rainfall data is essential, yet measuring daily precipitation worldwide is challenging. This research presents HYdroclimatic PERformance-enhanced Precipitation (HYPER-P), a dataset combining satellite, ground, and reanalysis data to estimate precipitation at a 1 km scale from 2000 to 2023. HYPER-P improves accuracy, especially in areas with few rain gauges. This dataset supports scientists and decision-makers in understanding and managing water resources more effectively.
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