Optimizing Retrieval of Boundary Layer Temperature and Humidity Profiles From Geostationary Hyperspectral Sounder by Integrating Ground Station Observations

Abstract

Geostationary satellite based hyperspectral infrared (IR) sounders enable continuous measuring weather cube of atmospheric temperature and humidity. Nevertheless, the uncertainties of temperature and humidity profiles in the boundary layer from the sounders are relatively large due to the difficulty on distinguishing surface contributions from radiances measured by low-peaking channels, which limits the quantitative applications. Since the dense ground stations provide rich 2-m temperature and moisture observations near continuously, although those observations do not contain vertical profile information directly, integration together with sounder measurements can improve the boundary layer profiling. Using Geostationary Interferometric Infrared Sounder (GIIRS) onboard Fengyun-4B (FY-4B) as a precursor geostationary sounder through theoretical analysis, simulation, and real data experiments using the optimal estimation method, it is found that integration of ground station observations and satellite sounder measurements can substantially reduce errors by 0.3 K and 2% (absolute) for temperature and humidity, respectively, in the boundary layer, highlighting the value of integrating geostationary hyperspectral IR measurements with ground station observations for quantitative applications such as nowcasting the high impact weather events in near real time.