1422 Land Surface Emissivity in the GSI: Evaluation of the First Guess and Control Variable

Wednesday, 25 January 2017
4E (Washington State Convention Center )
Biljana Orescanin, RTi, College Park, MD; and A. Collard, B. Johnson, T. Auligne, and J. C. Derber

Handout (20.9 MB)

A lack of emissivity observation data on the global scale is one of many difficulties for emissivity retrievals. Most of the field campaigns for land emissivity studies are short-lived and of small scale, and generally are not carried out in coordination with any specific satellite-based instruments or overpasses. This study focuses on testing and implementing the emissivity to the GSI as a control variable using MW channels over land surfaces. Land surface emissivities for global scales are currently mostly derived from satellite-based observations though radiative transfer calculations. Over land surfaces the emissivity is especially important for simulating surface-sensitive channels due to its high spatial and temporal variability. Estimating the atmospheric contribution from cloudy or rainy atmosphere, as well as the strong atmospheric scattering and absorption of land surface signals under such conditions, is still seen as a challenge, especially at higher frequencies. The estimation of satellite radiance is a key component of assimilating satellite data into numerical weather prediction (NWP) models. In addition, surface emissivity is crucial for estimating surface temperature from satellite measurements, retrieval of atmospheric moisture and temperature profiles from satellites, and studies of the Earth's surface-atmosphere system such as surface energy balance and climate modeling. This study is separated into: a) testing the brightness temperature sensitivity to emissivity changes in GSI, and b) adding the emissivity to the GSI as a control variable. Sensitivity experiments are expected to provide good understanding on any potential emissivity related issues, such as dimensionality, and channel- and sensor-specifics properties. At the same time, sensitivity experiments serve as a good insight into system’s linearity.
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