Feasibility Study of Microwave Radiometer Neural Network Modeling Method Based on Reanalysis Data

To address the challenge of microwave radiometer modeling in regions lacking radiosonde data, this study proposes a neural network retrieval method based on high-resolution the Final Reanalysis (FNL) reanalysis data and validates its feasibility. A microwave radiometer brightness temperature–profiles retrieval model was developed by the Back Propagation (BP) neural network, based on FNL reanalysis data from Qingdao, China. The model’s accuracy was evaluated by comparing retrieval results with synchronous radiosonde data, with an analysis of seasonal variations. Results indicate that the Root Mean Square Error (RMSE) of temperature profiles are 1.15 °C in the near-surface layer (0–2 km) and 2.05 °C in the mid-to-upper layers (>2 km). The comprehensive RMSE for relative humidity, water vapor density, and Integrated Water Vaper (IWV) are 17.27%, 0.96 g/m3, and 1.37 mm, respectively. Overall, the errors are relatively small, and the retrieval results exhibit strong spatiotemporal consistency with radiosonde data. The error increases most rapidly within the lower atmosphere (<2 km), with distinct seasonal differences observed. Temperature and relative humidity retrieval accuracies peak in summer, whereas water vapor density and IWV retrievals perform best in winter and worst in summer. This study confirms that reanalysis data–based modeling effectively addresses the issue of limited radiosonde coverage. This method is applicable to atmospheric remote sensing in regions lacking radiosonde data, such as oceans and plateaus. It provides a feasible solution to the regional limitations of microwave radiometer applications and expands the potential uses of reanalysis data.