Monday, 8 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
An approach to improve satellite data preprocessing and assimilation, is being developed at the National Oceanic and Atmospheric Administration’s (NOAA) Center for Satellite Applications and Research (STAR). This approach leverages NOAA’s current data assimilation (DA) systems and incorporates satellite remote sensing algorithms to make better use of the full information content of satellite observations in order to create an observation-driven global analysis. While this approach has been demonstrated for use in nowcasting and situational awareness, its impact on medium to long range weather forecasting has yet to be explored. To be presented here are a general background of the approach, which includes a variational preprocessing component that allows for the all-sky assimilation of satellite radiances using a unified satellite data quality control procedure, and a general background of the integration of the full preprocessing system and unified satellite data quality control with the NOAA Global Data Assimilation System / Global Forecast System (GDAS/GFS). Also to be discussed are the implications and impacts of performing numerical weather prediction on global and regional scales when the initial state of the forecast has been informed by the preprocessing system and accordingly adjusted to fit satellite observations and correct meteorological feature displacements. Additionally to be explored is the use of the preprocessing system with simulated observations, and the impacts in an Observation System Simulation Experiment (OSSE) context.
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