Comparison of modeled and retrieved microwave emissivities from AMSU over Northern Africa and the Middle East

Precise knowledge of the microwave land emissivity allows accurate land surface temperature retrievals, increases the sensitivity of profiling retrievals to lower tropospheric temperature, and allows better diagnosis of cloud properties over land surfaces. In this study, observational retrievals of land surface emissivity are performed using numerical model analysis, and an inversion of the radiative transfer equation. The observationally retrieved microwave emissivities are compared to emissivities derived using a Microwave Emissivity Model (MEM) developed by Fuzhong Weng and others of the Office of Research and Applications (ORA) in the National Environmental Satellite, Data, and Information Service (NESDIS). The observational retrieval employs analysis from the Navy Operational Global Atmospheric Prediction System (NOGAPS) as well as data from both the High Resolution Infrared Radiation Sounder Version 3 (HIRS/3) and the Advanced Microwave Sounding Unit (AMSU). Also of note is that the HIRS/3 and AMSU sensors fly aboard the same satellite bus, so there is no need for temporal interpolation of the instrument data. The infrared data from the HIRS/3 instrument is used to retrieve the land surface temperature in the observational retrieval. Minimizing error in the land surface temperature is of importance because the dominant component in the microwave emissivity error budget is the land surface temperature. The comparison of observed and modeled microwave emissivities are delimited into clear-sky, and cloudy cases. An emissivity error budget is presented, which compares the top of the atmosphere radiative variability due to the assumed microwave land emissivity error to the signal of non-precipitating clouds of varying water contents. The comparisons of observed and modeled microwave land emissivities are used to define the precision to which the emissivity can be diagnosed on a regional basis. This will aid data assimilation efforts by providing realistic estimates of error on the land surface temperature, lower tropospheric temperature, and cloud property data derived over land areas using microwave satellite data.