Research on Heavy Rainfall Inversion Algorithm Based on CD-Pix2Pix Model

Abstract

With the intensification of climate change, frequent short-duration heavy rainfall events exert significant impacts on human society and natural environment. Traditional rainfall recognition methods show limitations, including poor timeliness, inadequate handling of imbalanced data, and low accuracy when dealing with these events. This paper proposes a method based on CD-Pix2Pix model for inverting short-duration heavy rainfall events, aiming to improve the accuracy of inversion. The method integrates the attention mechanism network CSM-Net and the Dropblock module with a Bayesian optimized loss function to improve imbalanced data processing and enhance overall performance. This study utilizes multisource heterogeneous data, including radar composite reflectivity, FY-4B satellite data, and ground automatic station rainfall observations data, with China Meteorological Administration Land Data Assimilation System (CLDAS) data as the target labels fror the inversion task. Experimental results show that the enhanced method outperforms conventional rainfall inversion methods across multiple evaluation metrics, particularly demonstrating superior performance in Threat Score (TS, 0.495), Probability of Detection (POD, 0.857), and False Alarm Ratio (FAR, 0.143).