Research progress on atmospheric temperature and humidity profiles observation based on ground-based microwave radiometer

Objective This study investigates the technical principles and application advancements of ground-based microwave radiometers in atmospheric temperature and humidity profile observation, explores critical pathways to enhance measurement accuracy and stability, and provides theoretical support for meteorological monitoring and climate research. Data and Methods Based on physical foundations including thermal radiation theory, radiative transfer equations, and brightness temperature, this work systematically reviews microwave radiation measurement principles, calibration technology evolution, and data quality control methods. By comparing performance parameters of typical domestic and international devices, it focuses on analyzing optimization directions for inversion algorithms and the potential of integrating machine learning approaches. Results Ground-based microwave radiometers enable continuous detection of atmospheric temperature and humidity profiles within 0–10 km through multi-frequency observations (22–59 GHz). Liquid nitrogen cold calibration and tilt-curve calibration techniques improve brightness temperature accuracy to 0.2–0.5 K. Neural network algorithms reduce the root mean square error (RMSE) of temperature inversion to 1.48°C, while physically constrained models decrease the mean absolute error (MAE) of high-altitude (>8 km) temperature inversion by 0.19°C. However, challenges persist, including radio frequency interference in complex weather (10% error in L-band), insufficient long-term calibration stability (annual drift >0.2 K), and increased humidity inversion errors under cloudy/rainy conditions (RMSE up to 25.21%). Conclusion Ground-based microwave radiometers, enhanced by anti-interference hardware design, dynamic real-time calibration, and machine learning-physical model fusion algorithms, significantly improve atmospheric parameter inversion accuracy. This research lays theoretical and technical foundations for their widespread application in meteorological monitoring and climate studies. Future efforts should prioritize breakthroughs in multi-frequency collaborative observation, nonlinear error correction, and extreme weather adaptability. Through in-depth exploration of environmental noise suppression, calibration error tracing, and inversion robustness enhancement, this work provides critical support for advancing atmospheric detection technologies under global climate change, facilitating the development of high-precision, all-weather atmospheric profile observation systems.Key words Ground-based microwave radiometer,temperature and humidity profiles, atmospheric detection, inversion algorithm, machine learn