This study investigates technical principles and advancements in the application of ground-based microwave radiometers for the observation of atmospheric temperature and humidity profiles, explores critical pathways that can enhance measurement accuracy and stability, and provides theoretical support for meteorological monitoring and climate research. Based on physical foundations including the thermal radiation theory, radiative transfer equations, and brightness temperature, this work systematically reviews microwave radiation measurement principles and calibration technology evolution as well as data quality control methods. By comparing performance parameters of typical domestic and international devices, this study focuses on analyzing the direction of retrieval algorithms optimization and the potential of integrating machine learning approaches. Ground-based microwave radiometers enable continuous detection of atmospheric temperature and humidity profiles within 0—10 km through multi-frequency (22—59 GHz) observations. Liquid nitrogen cold calibration and tilt-curve calibration techniques improve brightness temperature accuracy up to 0.2—0.5 K. Neural network algorithms reduce the Root Mean Square Error (RMSE) of temperature inversion to 1.48℃, while physically constrained models decrease the Mean Absolute Error (MAE) of high-altitude (＞8 km) temperature inversion by 0.19℃. However, challenges persist, including radio frequency interference in complex weather (10% error in K-band), insufficient long-term calibration stability (annual drift＞0.2 K), and increased humidity inversion error under cloudy/rainy conditions (RMSE up to 25.21%). Ground-based microwave radiometers, enhanced by anti-interference hardware design and dynamic real-time calibration and machine learning-physical model fusion algorithms, can significantly improve the retrieval accuracy of atmospheric parameters. 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 observations, nonlinear error correction, and extreme weather adaptability. Through in-depth exploration of environmental noise suppression, calibration error tracking, and retrieval robustness enhancement, this work provides a critical support for advancing atmospheric detection technologies under global climate change, facilitating the development of high-precision, all-weather atmospheric profile observation systems.

In the context of global climate change and intensified human activities, the Qingzang plateau, known as the "Asian Water Tower" and the "Third Pole of the Earth", plays an increasingly significant role in regulating regional and global climate systems through its land-atmosphere interactions. Numerical simulations, as an effective tool for understanding the complex climatic processes on the Qingzang plateau, have played an irreplaceable role in exploring the physical mechanisms behind the land-atmosphere systems on the Qingzang plateau and their weather and climate effects. In this paper systematically reviews the research progresses of numerical simulations on four major land-atmosphere interaction processes on the Qingzang plateau, including land-surface-atmospheric processes (boundary layer processes), cloud precipitation physics processes, regional water cycle processes, and tropospheric processes, and focuses on discussing how these processes manifest at different temporal and spatial scales and their impacts on regional weather systems, monsoon circulations, and global climate. Finally, this paper outlines future research directions by proposing a need to enhance model accuracy, optimize parameterization schemes and integrate multiple observational data sources to further reveal the unique role of the Qingzang plateau in global climate system dynamics and provide scientific basis for addressing challenges posed by climate change.