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

Gorde:
Argitaratua izan da:Journal of Tropical Meteorology vol. 31, no. 5 (Oct 2025), p. 556-565
Egile nagusia: Zhang, Yu-Hao
Beste egile batzuk: Lu, Zhen-Yu, Zhang, Xiao-Wen, Lu, Bing-Jian
Argitaratua:
Guangzhou Institute of Tropical & Marine Meteorology
Gaiak:
China
Climate change
Satellite data
Accuracy
Data processing
Deep learning
Agricultural production
Rainfall
Rainfall-climatic change relationships
Data assimilation
Data analysis
Heavy rainfall
Machine learning
Meteorological satellites
Remote sensing
Bayesian analysis
Precipitation
False alarms
Reflectance
Climate models
Data collection
Natural environment
Radar data
Probability theory
Rain
Environmental
Sarrera elektronikoa:Citation/Abstract
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Laburpena: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).
ISSN:1006-8775
DOI:10.3724/j.1006-8775.2025.012
Baliabidea:East & South Asia Database