PrecipGAN: Merging Microwave and Infrared Data for Satellite Precipitation Estimation Using Generative Adversarial Network

Abstract Global satellite precipitation estimation at high spatiotemporal resolutions is crucial for hydrological and meteorological applications but is still a challenging task. One major challenge is that the microwave data are discontinuous in space and time. We present a novel approach to merge incomplete passive microwave (PMW) precipitation estimates using the conditional information provided by complete infrared (IR) precipitation estimates based on the generative adversarial network (GAN), and name the algorithm PrecipGAN. PrecipGAN decomposes the precipitation system into content and evolution subspaces to propagate PMW estimates to regions outside the orbit coverage of PMW sensors. PrecipGAN can skillfully simulate the spatiotemporal changes of precipitation events, and produce precipitation estimates with overall better statistical performance than the baseline product Integrated MultisatellitE Retrievals for GPM (IMERG) Uncalibrated over the Continental US. PrecipGAN provides an alternative of accurate and computationally efficient algorithm that can be implemented globally to produce satellite‐based precipitation estimates.