Assimilation of satellite radiance data in limited-area, rapidly updating weather model/assimilation systems poses unique challenges compared to those for global model systems. Principal among these is the severe data restriction posed by the short data cutoff time. Also, the limited extent of the model domain reduces the spatial extent of satellite data coverage and the lower model top of regional models reduces the spectral usage of radiance data especially for infrared data. These three factors impact the quality of the feedback to the bias correction procedures, making the procedures potentially less effective. Within the National Oceanic and Atmospheric Administration (NOAA) Rapid Refresh (RAP) hourly updating prediction system, satellite radiance data are assimilated using the standard procedures within the Gridpoint Statistical Interpolation (GSI) analysis package. Experiments for optimizing the operational implementation of radiance data into RAP and for improving benefits of radiance data have been performed. The radiance data impact for short-range forecasts has been documented to be consistent and statistically significantly positive in systematic RAP retrospective runs using real-time datasets. The radiance data impact has also been compared with conventional observation datasets within RAP. The configuration for RAP satellite radiance assimilation evaluated here is that implemented at the National Centers for Environmental Prediction (NCEP) in August 2016.
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