论文标题
TCR-GAN：通过生成对抗网络，使用红外图像预测热带气旋微波降雨
TCR-GAN: Predicting tropical cyclone passive microwave rainfall using infrared imagery via generative adversarial networks
论文作者
论文摘要
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。具有高空间和时间分辨率的TC的被动微波降雨（PMR）估计对于TC的灾难警告至关重要，但由于微波传感器的时间分辨率较低，因此仍然是一个具有挑战性的问题。这项研究试图通过直接预测TC的卫星红外（IR）图像来解决此问题。我们开发了一个生成的对抗网络（GAN），以将IR图像转换为PMR，并在TC云顶明亮温度和PMR之间建立映射关系，该算法称为TCR-GAN。同时，建立了一个新的数据集，可作为基准，热带气旋IR-TO-RAINFALL预测（TCIRRP）的数据集，这有望促进在这个方向上发展人工智能的发展。实验结果表明，该算法可以有效地从IR中提取关键特征。端到端的深度学习方法显示了一种可以在全球应用的技术，并通过卫星提供了新的视角热带气旋降水预测，预计该卫星将为TC在操作中的TC降雨实时可视化提供重要的见解。
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.
