Extended Abstract (172K)2.2
An empirical emissivity model for complex surfaces
Janice Bytheway, Colorado State University, Fort Collins, CO; and C. Kummerow
Over the relatively uniform ocean surface, microwave surface emissivity models can be used to characterize the radiative contribution of the ocean beneath clouds and precipitation. However, physically based models fail to fully characterize the behavior of emissivity over more complex land surfaces, where emissivity is dependent on the more spatially and temporally variable properties of soil type, surface roughness, moisture content, and vegetation characteristics. The retrieval of clouds and precipitation is further complicated by a lack of contrast between atmospheric constituents and the radiometrically warm surface. Nonetheless, an emissivity model is useful to ensure consistency between observed and modeled brightness temperatures for various atmospheric conditions. Empirical models offer an alternative method for describing emissivity behavior. Therefore, an empirical emissivity model has been developed using observations from the family of sensors onboard the Aqua satellite. This empirical model relates the surface emissivity of the atmospherically transparent 10.65-GHz horizontal channel of the Advanced Scanning Microwave Radiometer-EOS (AMSR-E) to those of the sensor's higher frequency channels. The model was developed over scenes deemed cloud free by the Moderate Resolution Imaging Spectroradiometer (MODIS) using the surface temperature and atmospheric temperature and water vapor obtained from the Atmospheric Infrared Sounder (AIRS). The model was first developed and validated over the oceans, where emissivity behavior is well understood. The empirical technique was then applied over land, where the results were compared to those obtained from existing emissivity models. Finally, the validated land model was used in an optimal estimation retrieval to separate precipitating from non-precipitating scenes.
Session 2, Atmospheric Observations for Weather and Climate-II
Monday, 21 January 2008, 10:45 AM-11:45 AM, 204
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