Author Response to RC1 (Corrected Submission)

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. Concurrently, 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 historical data and integrates platform attitude information. Our approach uses a multi-objective genetic algorithm to construct a small-scale joint prior database. It also incorporates an attitude error correction model, a pressure-altitude model, and a parallel optimization strategy. This fundamentally eliminates dependence on historical data. It also effectively mitigates systematic errors from buoy attitude, 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 2.08 K, and the humidity RMSE of 20.95 %. 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.