Our previous studies have demonstrated that the assimilation of satellite radiance observations improves short-range forecast skill for the Rapid Refresh (RAP) model system. The RAP uses the Gridpoint Statistical Interpolation (GSI) hybrid variational/Ensemble Kalman Filter (EnKF) data assimilation system, with ensemble information for the regional assimilation coming from the 80-member global ensemble data assimilation system. The RAP's model top is 10-hPa, which compromises the ability of the RAP data assimilation to make optimal use of satellite radiance data. Because of this low model top, some upper level channels, especially in the infrared range, are removed to prevent aliasing and resultant forecast degradation. In addition, the effectiveness of the radiance variational bias correction is constrained due to the lower model top. In order to maximize the utility of satellite radiance data assimilation within the RAP, we are evaluating the impact of using a higher model top within the RAP. Tests with a model top of 1 or 2 hPa are planned. Along with this, we will examine the impact of increasing the number of model levels to better describe the upper atmosphere. Tests will also examine other aspects of the RAP satellite assimilation, including impact of the model top on the bias correction, assessment of relative impacts from different satellite sensors, and use of direct readout data (more data coverage from reduced data latency). Results from single case studies, as well as retrospective runs will be presented. For the model top impact assessment, comparison will be made for control experiments with and without the satellite data, and experimental runs using the higher model top with and without the satellite data. This will help us determine degree to which the higher model top effects the satellite assimilation vs. other aspects of the assimilation and modeling system.
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