TCR-GAN：通过生成对抗网络使用红外图像预测热带气旋被动微波降雨
TCR-GAN: Predicting tropical cyclone passive microwave rainfall using infrared imagery via generative adversarial networks
摘要：
热带气旋（TC）通常携带大量的水蒸气，并可能导致大规模的极端降雨。高时空分辨率的热带气旋被动微波降水（PMR）估计是热带气旋灾害预警的关键，但由于微波传感器的时间分辨率较低，仍然是一个具有挑战性的问题。本研究试图解决这个问题，直接预测PMR从卫星红外（IR）图像的TC。我们开发了一种生成对抗网络（GAN）将红外图像转换为PMR，并建立了TC云顶亮温度与PMR之间的映射关系，该算法被命名为TCR-GAN。同时，建立了一个新的数据集，可作为基准，热带气旋红外到降雨预测数据集（TCIRRP），这有望推动人工智能在这方面的发展。实验结果表明，该算法能够有效地提取IR中的关键特征，并显示出可在全球范围内推广应用的端到端深度学习方法，为热带气旋降水卫星预报提供了新的视角，有望为业务中的全球热带气旋降水实时可视化提供重要见解。
英文摘要：
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.
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