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.

This paper conducts a technical study on a method for determining the occurrence threshold of wind shear based on historical sounding data. After analyzing the impact of low-altitude wind shear on aircraft flight safety, a method for determining the occurrence threshold of wind shear based on historical sounding data is proposed. A statistical analysis of the sounding data from the test area over a period of 15 years from 2010 to 2024has been conducted, which includes the occurrence events and probability statistics of 1000m wind shear for all 12 months of the year. Simulation results validate the feasibility and effectiveness of the method for determining the occurrence threshold of wind shear based on historical sounding data in the test area, forming a method that can be extended to all altitude ranges of aircraft flight and all flight regions globally. This statistical method provides a technical foundation for the efficient detection of wind shear at local airports and enhances flight safety at these airports.

This paper conducts a technical study on a method for determining the occurrence threshold of wind shear based on historical sounding data. After analyzing the impact of low-altitude wind shear on aircraft flight safety, a method for determining the occurrence threshold of wind shear based on historical sounding data is proposed. A statistical analysis of the sounding data from the test area over a period of 15 years from 2010 to 2024 has been conducted, which includes the occurrence events and probability statistics of 1000 m wind shear for all 12 months of the year. The simulation results validate the feasibility and effectiveness of the method for determining the occurrence threshold of wind shear based on historical sounding data in the test area, forming a method that can be extended to all altitude ranges of aircraft flight and all flight regions globally. This statistical method provides a technical foundation for the efficient detection of wind shear at local airports and enhances flight safety at these airports.