Generation of Synthetic Advanced Microwave Scanning Radiometer-2 23.8 GHz Dual-Polarization Measurements From Global Precipitation Measurement Microwave Imager Observations

Abstract

This study presents a deep learning (DL)-based data-to-data (D2D) translation framework for generating synthetic Advanced Microwave Scanning Radiometer-2 (AMSR2)-like dual-polarization (Pol) measurements from the Global Precipitation Measurement Microwave Imager (GMI) 23.8 GHz vertical (V)-Pol data. The proposed D2D model was constructed through two D2D translations: AMSR2 V-Pol to AMSR2 horizontal (H)-Pol (main model) and GMI V-Pol to AMSR2 V-Pol (submodel). The D2D method incorporates a normalization preprocess and a denormalization postprocess and utilizes an adversarial learning framework for interdomain conversion of the physical values in brightness temperature ( $T_{B}$ ) data. The datasets from AMSR2 at 23.8 GHz dual-Pol measurements, covering the period from January 2013 to December 2022 and from GMI at 23.8 GHz V-Pol measurements (23.8V GHz), covering the period from January 2015 to December 2022, were employed as the source and target datasets for training and evaluating the D2D model. The D2D-generated $T_{B}$ data were validated against the AMSR2 observation data using statistical metrics, including the correlation coefficient (CC), bias, mean absolute error (MAE), and root mean square error (RMSE). The D2D-generated AMSR2 23.8 GHz H-Pol measurements (23.8H GHz) showed average values of CC =0.989, bias =0.8 K, MAE =4.0 K, and RMSE =5.8 K when validated against target data. Furthermore, this study demonstrated the critical role of H-Pol measurements in cloud liquid water (CLW) estimation experiments. Consequently, the hypothetical AMSR2 23.8 GHz dual-Pol data can provide additional valuable atmospheric information, thereby enhancing weather forecasting accuracy and climate change studies.