TCR-GAN: Predicting tropical cyclone passive microwave rainfall using infrared imagery via generative adversarial networks

TL;DR

This paper introduces TCR-GAN, a novel generative adversarial network that predicts tropical cyclone rainfall from infrared satellite images, offering high-resolution, real-time rainfall estimation crucial for disaster management.

Contribution

The study develops a new GAN-based model and establishes a benchmark dataset for IR-to-rainfall prediction in tropical cyclones, advancing AI applications in meteorology.

Findings

Effective extraction of key features from IR images.

Potential for global real-time tropical cyclone rainfall prediction.

Establishment of a new benchmark dataset TCIRRP.

Abstract

Tropical cyclones (TC) generally carry large amounts of water vapor and can cause large-scale extreme rainfall. Passive microwave rainfall (PMR) estimation of TC with high spatial and temporal resolution is crucial for disaster warning of TC, but remains a challenging problem due to the low temporal resolution of microwave sensors. This study attempts to solve this problem by directly forecasting PMR from satellite infrared (IR) images of TC. We develop a generative adversarial network (GAN) to convert IR images into PMR, and establish the mapping relationship between TC cloud-top bright temperature and PMR, the algorithm is named TCR-GAN. Meanwhile, a new dataset that is available as a benchmark, Dataset of Tropical Cyclone IR-to-Rainfall Prediction (TCIRRP) was established, which is expected to advance the development of artificial intelligence in this direction. Experimental results show that the algorithm can effectively extract key features from IR. The end-to-end deep learning approach shows potential as a technique that can be applied globally and provides a new perspective tropical cyclone precipitation prediction via satellite, which is expected to provide important insights for real-time visualization of TC rainfall globally in operations.