  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.
