Real-Time Retrieval of All-Weather Weighted Mean Temperature From FengYun-4A Observations

Atmospheric weighted mean temperature (Tm) is a crucial parameter that links precipitable water vapor (PWV) and zenith wet delay (ZWD). To address the challenge of balancing the quality and timeliness of Tm data, this study introduced infrared remote-sensing technology based on meteorological satellites for the first time to retrieve Tm. We developed separate Tm estimation models for FengYun-4A (FY4A) observations under both clear and cloudy conditions and combined them to enable real-time retrieval of all-weather Tm. This combined model, called the all-weather Tm estimation model, is based on the linear relationship between Tm and surface temperature as well as remote sensing retrieval theories related to surface temperature and cloud-top properties. This grouping modeling approach allows continuous spatiotemporal Tm data to be estimated at minute intervals, even under cloudy conditions. Radiosonde-derived and ERA5-derived Tm data from 2022 were used to assess the accuracy of FY4A-derived Tm for Australia. Compared to radiosonde-derived Tm, the root mean square error (RMSE)/bias values for FY4A-derived Tm were 1.37/0.05, 1.45/0.06, and 1.38/0.06 K for all-time, daytime, and nighttime, respectively. Compared to the ERA5-derived Tm, the RMSE/bias values for FY4A-derived Tm were 1.26/0.01, 1.33/0.01, and 1.37/0.03 K under all-weather, clear, and cloudy conditions, respectively. The validation results indicated that the satellite-based Tm retrieval model possesses the advantages of real-time monitoring, all-weather capability, high accuracy, and high spatiotemporal resolution. Thus, it has tremendous potential for deepening interdisciplinary collaboration between the meteorology and navigation fields.