Retrieving tropospheric temperature and humidity profiles over the ocean using buoy-based microwave radiometers

Abstract

Abstract. The acquisition of atmospheric temperature and humidity profiles over the sea is strategically vital for meteorological forecasting, marine monitoring, and national security. Achieving their real-time, stable, and routine retrieval under complex sea conditions is a critical and urgent challenge. Traditional retrieval methods rely heavily on large historical datasets. However, marine sounding stations are sparse, making data acquisition challenging. Ground-based microwave radiometers offer a unique capability for continuous, all-weather remote sensing of atmospheric thermal emission, enabling routine retrieval of temperature and humidity profiles over oceanic regions. Meanwhile, buoy platforms experience wave disturbance, causing real-time variations in zenith angle observations. Without correction, this induces significant random errors in target brightness temperature. To address these issues, this paper proposes a collaborative retrieval method. This method does not rely on large-scale historical datasets for model training and integrates platform attitude information. Our approach uses a multi-objective genetic algorithm to construct a small-scale joint prior database based on a limited amount of local radiosonde data, which serves only as an initial physical constraint for the retrieval process. It also incorporates an attitude error correction model, an empirical pressure-altitude equation, and a parallel optimization strategy. This thereby reduces dependence on extensive historical datasets. It also effectively mitigates attitude-induced brightness-temperature deviation, enhances computational efficiency, and enables real-time, routine retrieval of marine atmospheric profiles. Simulation experiments and field tests in Qingdao's Jiaozhou Bay confirm the results. Under sparse-data conditions, the temperature RMSE is 4.11 K before systematic bias correction and 2.13 K after correction, while the relative-humidity RMSE is 24.09 % before correction and 21.42 % after correction. A single static bias correction profile is proposed for operational deployment, which achieves performance comparable to the cross-validation results. This validates the method's stability and applicability in real marine environments. This research provides a potentially practical pathway for ocean areas with sparse radiosondes for real-time, stable, and routine detection of marine atmospheric parameters.