This works presents the results for the first study to ever attempt to analyze the full potential and limitations of incorporating aerosols within a truly physical SST retrieval for operational weather forecasting purposes. This is accomplished through the application of a satellite sea surface temperature (SST) physical retrieval for satellite split-window and hyperspectral infrared (IR) sensors that allows a better representation of the atmospheric state under aerosol-laden conditions. The new algorithm includes 1) accurate specification of the surface emissivity that characterizes the surface leaving radiance and 2) transmittance and physical characterization of the atmosphere by using the Community Radiative transfer model (CRTM). This project includes application of the NEMS-Global Forecasting System Aerosol Component (NGAC) fields, which corresponds to the first global interactive atmosphere-aerosol forecast system ever implemented at NOAA’s National Center for Environmental Prediction (NCEP).
A number of limiting factors were identified by analysing brightness temperatures and SST outputs biases as a function of latitude, zenith angle, wind and moisture for cases in January and November 2013. SST ouputs are validated against a bulk SST (Reynolds SST) and a parameterized SST derived from operational products and partly against observed measurements from the eastern Atlantic Ocean, which is dominated by Saharan dust throughout most of the year and that is also a genesis region for Atlantic tropical cyclones. These observations are obtained from the NOAA Aerosols and Ocean Science Expeditions (AEROSE). The improved physical SST methodology has the potential to allow for improved representation of the geophysical state under dust-laden conditions.
|Advisor:||Joseph, Everette L.|
|Commitee:||Demoz, Belay, Joseph, Everette L., Min, Qiliong, Morris, Vernon R., Nalli, Nicholas R., Venable, Demetrius D.|
|School Location:||United States -- District of Columbia|
|Source:||DAI-B 78/07(E), Dissertation Abstracts International|
|Subjects:||Meteorology, Atmospheric sciences, Remote sensing|
|Keywords:||Aerosols, Atmospheric radiation, Data assimilation, NOAA, Numerical weather prediction, Satellite remote sensing|
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