IMERG-Like Precipitation Retrieval From Geo-Kompsat-2A Observations Using Conditional Generative Adversarial Networks

This study proposes an infrared-to-rain (IR2Rain) model to enhance the accuracy of the geostationary (GEO) weather satellite Geo-Kompsat-2A (GK-2A) rain rate (RR) product. The IR2Rain model is built upon a conditional generative adversarial network, taking GK-2A brightness temperatures as inputs and Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) Final RRs as target values. To address the distinct physical characteristics and ranges of the input and target datasets, IR2Rain employs preprocessing for normalization and postprocessing for denormalization. The IR2Rain model is developed and validated using the paired input and output datasets collected between September 2019 and December 2022, encompassing a broad region across Asia and Oceania. This study compares the performance of IR2Rain-derived RRs against IMERG RR, GK-2A RR, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network dynamic infrared (IR) rain rate-now products. The results demonstrated a probability of detection of 0.607, a critical success index of 0.482, a root-mean-square error of 0.759 mm/h, and a correlation coefficient of 0.671. By combining the high temporal resolution of GEO satellite observations with the reliability of IMERG Final data, the IR2Rain model produces a robust near-real-time IMERG-like precipitation product. Despite smoothing effects and the tendency to underestimate intense rainfall, IR2Rain improves the performance relative to RR products based on the same GK-2A IR data, mitigates the latency encountered in IMERG data generation, and provides timely and accurate precipitation information on intensity and distribution. These products are particularly valuable for operational weather forecasting and public end users in Asia and Oceania, supporting disaster preparedness and hydrological applications.