- 1National Research Council of Italy - Research Institute for Geo-Hydrological Protection (CNR-IRPI), Via Cavour 4/6, 87036 Rende, CS, Italy, tommaso.caloiero@cnr.it; francesco.chiaravalloti@cnr.it; roberto.coscarelli@cnr.it
- 2National Research Council of Italy - Institute for Agriculture and Forestry Systems in the Mediterranean (CNR-ISAFOM), Via Cavour 4/6, 87036 Rende, CS, Italy, gaetano.pellicone@cnr.it
Precipitation is a critical variable for hydrological studies and water resource management. However, while rain gauges generally produce the most reliable observational results, their often-sparse distribution causes them not to be fully representative of some regions, especially large ones. In fact, in regions with a complex orography and scarce human settlements, rain gauges are usually not sufficient to provide data to resolve precipitation processes in simulation studies. Satellite retrievals have thus been used to create regular data grids, in order to fill in on lacking observations and to address the scarcity of stations in ungauged regions.
This study aimed to evaluate the skills of five satellite precipitation products in reproducing precipitation across three temporal scales (daily, seasonal, and annual) over Italy. These are the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Run, the PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now), the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (HSAF H05), and the Soil Moisture to Rain (SM2Rain). To this purpose, precipitation data for the period 2000-2021 have been extracted from the National System for collection and processing of climate data (SCIA) gridded observational rainfall dataset provided by the Italian Environmental Protection Agency (ISPRA). After resampling all the different datasets to a common grid with spatial resolutions of 0.1°, the performance of the satellite products was then assessed using two distinct sets of statistical metrics. In particular, the accuracy of satellite products at a daily temporal resolution has been evaluated using performance metrics such as the Probability of Detection (POD), the False Alarm Ratio (FAR), the Success Ratio (1-FAR), the BIAS, and the Critical Success Index (CSI). Conversely, at annual and seasonal scales, the Root Mean Square Error (RMSE), the coefficient of determination (R²), and the standard deviation (SD), have been applied.
Results showed that GPM-IMERG Final Run satellite data performed better at a daily resolution both in capability (POD) and reliability (SR), except during the summer season, when the HSAF H05 demonstrates a better overall performance. Conversely, the PDIR-Now tends to overestimate rainfall events. As regards the annual and seasonal time scales, HSAF H05, GPM-IMERG, and SM2Rain demonstrate strong correlations with observed data at annual scale, with high R2 values (≥0.88) and generally low errors (SD and RMSE).
The procedure applied in this work is general and easily applicable where gridded data are available and might help scientists and policy makers to select among available datasets those best suited for further applications, even in areas with a complex orography and an inadequate amount of representative stations.
This work was funded by the Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of Innovation Ecosystems', building 'Territorial R&D Leaders' (Directorial Decree n. 2021/3277) - project Tech4You – Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.
How to cite: Caloiero, T., Chiaravalloti, F., Coscarelli, R., and Pellicone, G.: Validation of satellite estimates of precipitation over Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10789, https://doi.org/10.5194/egusphere-egu25-10789, 2025.