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

Headnote

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).

Key words: short-duration heavy rainfall; inversion; CD-Pix2Pix

CLC number: P409 Document code: A

(ProQuest: ... denotes formulae omited.)

1 INTRODUCTION

With the intensification of global climate change, extreme weather events, particularly short-duration heavy rainfall, are increasing in both frequency and intensity, posing serious challenges to human society and natural environment (Tabari 2020; Myhre et al. 2019). Such rainfall events can trigger natural disasters like floods and landslides, significantly impacting agricultural production, urban infrastructure, and daily life (Kendon et al. 2014; Trenberth 2011). In this context, accurately identifying heavy rainfall events using meteorological data, especially radar and satellite data, is crucial for mitigating the potential hazards of these extreme events (Westra et al. 2014).

Radar and satellite remote sensing technologies are two pillars of meteorological research, providing valuable data resources for inverting these extreme weather events (Hou et al. 2014; Fowler et al. 2007). Radar, by emitting and receiving electromagnetic waves, can provide highresolution real-time information on rainfall intensity and movement (Wei and Hsu 2021). Satellite remote sensing, on the other hand, covers a broader geographical area, offering a variety of useful information such as cloud cover, water vapor content, and precipitation estimates (Habib et al. 2012). However, despite their value, existing methods still face significant challenges in heavy...