A Performance Evaluation and Statistical Analysis of IMERG Precipitation Products During Medicane Daniel (September 2023) in the Thessaly Plain, Greece

The precise estimation of precipitation is key to understanding and mitigating the effects of extreme weather conditions, especially in areas susceptible to Mediterranean cyclones. This work assesses the performance of the integrated multi-satellite retrievals for GPM (IMERG) precipitation products during the extreme Mediterranean cyclone “Medicane Daniel” that affected the Thessaly Plain in Central Greece in early September 2023. Three IMERG versions (final run (FR), early run (ER), and late run (LR)) were inter-compared with gauge-based interpolated rainfall estimates using inverse distance weighting (IDW) and ordinary kriging techniques. Pixel-wise and categorical verification metrics, such as the probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and Peirce skill score (PSS), were calculated for rainfall thresholds between 50 mm and 400 mm. It was found that the IMERG final run agreed most with the ground observations, with a correlation coefficient (R) of 0.87, RMSE of 138.8 mm, and CSI up to 0.995 at the 100 mm threshold when the IDW interpolation was used. Kriging produced slightly better spatial accuracy overall, as indicated by a lower RMSE (14.5 mm) and higher correlation (R = 0.99). The results indicate the benefit of combining satellite precipitation data with ground-based observations through spatial interpolation for the enhanced monitoring of extreme weather events over complex terrain. Kriging is suggested when greater spatial reliability is needed, while IMERG-FR is found to be a reliable satellite product for quick response analysis during heavy precipitation events. The study emphasizes the importance of blending satellite precipitation estimates and ground observations via spatial interpolation methods, i.e., kriging and IDW, allowing for a more localized and precise validation of intense weather events.