Retrieval of Surface and Atmospheric Geophysical Variables over Snow-Covered Land from Combined Microwave and Infrared Satellite Observations

Abstract

Surface temperature and emissivities, as well as atmospheric water vapor and cloud liquid water, have been calculated from Special Sensor Microwave Imager observations for snow-covered land areas using a neural network inversion scheme that includes first-guess information. A learning database to train the neural network is derived from a global collection of coincident surface and atmospheric parameters, extracted from the National Centers for Environmental Prediction reanalysis, from the International Satellite Cloud Climatology Project data, and from microwave emissivity atlases previously calculated. Despite the large space and time variability of the snow microwave response, the surface and atmospheric parameters are retrieved. Water vapor is estimated with a theoretical rms error of approximately 30%, verified against radiosonde measurements, that is almost the same as over snow-free land. The theoretical rms error of the surface skin temperature retrieval is 1.5 and 1.9 K, respectively, for clear and cloudy scenes. The surface skin temperatures are compared with the surface air temperatures measured at meteorological stations to verify that the expected differences are found. The space and time variations of the retrieved surface emissivities are evaluated by comparison with surface parameter variations such as surface air temperature, snow depth, and vegetation cover.