Wednesday, 10 January 2018: 9:00 AM
Salon K (Hilton) (Austin, Texas)
A full understanding of measured satellite radiancies is a key for successful assimilation of satellite data into numerical weather prediction models. Affecting the upwelling energy at its source, the accuracy of surface emissivity estimate plays an important role in this effort. However, a lack of emissivity observation data on the global scale is one of many obstacles when bridging a gap between available and desired information on the surface emissivity. Most of the field campaigns for land emissivity studies are short-lived and of small scale, generally not carried out in coordination with any specific satellite-based instruments or overpasses. At global scales, a solution for land surface emissivity is commonly seen in its derivation from satellite-based observations using radiative transfer calculations. This approach has its own challenges caused by necessity of having a balance between properties such as surface temperature, atmospheric moisture and temperature profiles, all affecting measured radiance but not directly measured. In order to assess land emissivity data quality and improve quantity of assimilated radiances over non-ocean surfaces within the Gridpoint Statistical Interpolation (GSI) system, our previous work on improving the emissivity first guess is extended to include the emissivity as a control variable. The study focuses on testing and implementing the surface emissivity as an analysis variable to the GSI using microwave channels over land surfaces for the ATMS S-NPP sensor. Having the emissivity as a control variable will hopefully bring us closer to observations and produce less rejection of emissivity by current quality control to ultimately improve forecast in the long run.
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