Nighttime Ground Radar-Like Rainfall Estimation Using Virtual Visible and Near-Infrared and Real Infrared Bands of Advanced Meteorological Imager

Rainfall rate (RR) retrieval methods utilizing visible (VIS), near-infrared (NIR), and infrared (IR) bands from geostationary (GEO) meteorological satellites often achieve higher accuracy compared to methods relying solely on IR bands. However, these methods face limitations for nighttime applications. This study aims to introduce an artificial intelligence (AI)-based approach for nighttime RR retrieval using actual IR observations and virtual nighttime VIS (0.47, 0.51, and 0.86 μm) and NIR (1.37 and 1.61 μm) bands data generated through an adversarial data-to-data (D2D) translation method. The study focuses on the Korean Peninsula and employs paired observational datasets from the Advanced Meteorological Imager (AMI) on Geo-KOMPSAT-2A (GK2A) and hybrid surface rainfall data from ground weather radars as input and target domains for the D2D model. The study generates virtual D2D-Generated (DG) VIS and NIR band data and evaluates DG-RR using only IR bands during nighttime. Results indicate that DG-RR using virtual AMI VIS, NIR, and real AMI IR bands demonstrates superior quantitative and qualitative statistical scores compared to DG-RR based solely on the AMI IR band data. This improvement is observed over traditional algorithm-based GK2A AMI RR and precipitation estimation from remotely sensed information using artificial neural networks. Thus, this study underscores the potential of utilizing virtual AI-based VIS and NIR band data, resembling actual observations, in RR estimation and various satellite remote sensing applications.