The Development and Evaluation of a Novel Precipitation Product for Extreme Events: A Principal Component Analysis Approach

Precise precipitation data are essential for the effective execution of water resource management and risk reduction initiatives, particularly during periods of extreme precipitation. This study introduces a new precipitation product (NPP) that integrates in situ and ERA5 estimations to enhance the identification and quantification of extreme precipitation events. We evaluated the newly generated dataset using a diverse array of statistical indices, in conjunction with two satellite precipitation products (IMERG and PERSIANN-DIR). We also implemented principal component analysis (PCA) to evaluate the efficacy of precipitation products. The PCA algorithm facilitates the interpretation of the primary factors that influence severe precipitation by reducing the number of dimensions in the dataset. We also evaluated the efficacy of newly produced products and SPPs using 11 extreme indices. We found that the NPP is the only newly integrated precipitation product that can track the heaviest precipitation at the highest elevations (> 3000 m), which is similar to how it leads on most extreme indices for tracking extreme events. The NPP monitors the maximum number of days for R50, while others suggest a significant level of underestimation. The PERSIANN PDIR's performance is unsatisfactory, and the IMERG is only marginally capable of monitoring the new dataset's performance. The results suggest that PCA pattern 4 significantly influences the efficacy of all datasets due to displaying circular wind patterns, which can produce localized precipitation zones and potentially complicate accurate capture by satellite products. This research provides a valuable resource for water resource managers and meteorologists to improve flood predictions and risk reduction strategies. A novel precipitation product, in conjunction with PCA insights, establishes a robust framework for improving the mitigation of severe weather impacts in vulnerable regions.