A Novel Machine Learning-Based Clustering-Merging Method for Improving Extreme Precipitation Estimation

Satellite Precipitation Products (SPPs) face challenges in detecting Extreme Precipitation Events (EPEs). Hence, the primary objective of this research is to introduce a novel framework termed Machine-Learning Clustering-Merging Algorithms (ML-CMAs) to evaluate EPEs using SPPs and Auxiliary Data (AD). Daily precipitation measurements were utilized for training and evaluating EPE estimates over Iran, which is comprised of arid and semi-arid regions. Statistical analysis and evaluation of five SPPs demonstrated that during EPE occurrences, all products face challenges in precipitation estimation, and using these products individually is not recommended. Among the SPPs, Multi-Source Weighted-Ensemble Precipitation (MSWEP) performed best for heavy (>20 mm d ^–1) and extreme (>40 mm d ^–1) precipitation events, followed by Global Satellite Mapping of Precipitation (GSMaP), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Dynamic Infrared-Rain Rate (PERSIANN-PDIR). The findings indicate that all proposed methods based on ML-CMAs could estimate precipitation rates more accurately than SPPs and improve statistical indices. The seasonal assessment and spatial analysis of statistical metrics of the overall daily precipitation results for all periods and climates revealed that all methods based on ML-CMAs performed well in all seasons and at nearly all measurement stations. Using unsupervised K-means⁺⁺ classification for clustering EPEs and Deep Neural Network (DNN) and Convolutional Neural Network (CNN) methods for merging the ML-CMAs reduced the error rate of SPPs in EPE estimation by approximately 50%. Therefore, incorporating ML-CMAs along with PWV as AD can significantly improve the performance of SPPs in evaluating EPEs over the study region.