Atmosphere-Land Coupled Data Assimilation by Using Satellite Microwave Radiometers

INTRODUCTION Soil moisture, snow and precipitation are key parameters in numerous environmental studies, including hydrology, meteorology, and agriculture. They play important roles in the interactions between the land surface and the atmosphere, as well as the partitioning of precipitation into runoff and ground water storage and absorbed solar energy into sensible and latent heat flues at the land surface. Recent studies with the general circulation models (GCMs) using active land surface parameterization have shown that strong feedbacks existed between the soil moisture, snow, and precipitation anomalies and climate. The current techniques or algorithms need to be further improved or developed in order to obtain the required accuracy for atmospheric and hydrologic modeling. The key elements of the radiative transfer models, for example, vertical profiles of soil moisture, snow grain size and water vapor and air temperature, can not be obtained easily only by satellite remote sensing. To solve these issues, it is effective to assimilate remote sensing data with numerical simulation schemes, Land Surface Schemes, Snow Physical Models, and Cloud Microphysics Models, which are embedded in GCMs or regional models.