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Storm-Scale Data Assimilation and Ensemble Forecasting with the NSSL...

Jones, T. A., K. Knopfmeier, D. Wheatley, G. Creager, P. Minnis, and R. Palikonda (2016), Storm-Scale Data Assimilation and Ensemble Forecasting with the NSSL Experimental Warn-on-Forecast System. Part II: Combined Radar and Satellite Data Experiments, Wea. Forecasting, 297, 297-327, doi:10.1175/WAF-D-15-0107.1.

This research represents the second part of a two-part series describing the development of a prototype ensemble data assimilation system for the Warn-on-Forecast (WoF) project known as the NSSL Experimental WoF System for ensembles (NEWS-e). Part I describes the NEWS-e design and results from radar reflectivity and radial velocity data assimilation for six severe weather events occurring during 2013 and 2014. Part II describes the impact of assimilating satellite liquid and ice water path (LWP and IWP, respectively) retrievals from the GOES Imager along with the radar observations. Assimilating LWP and IWP observations may improve thermodynamic conditions at the surface over the storm-scale domain through better analysis of cloud coverage in the model compared to radar-only experiments. These improvements sometimes corresponded to an improved analysis of supercell storms leading to better forecasts of low-level vorticity. This positive impact was most evident for events where convection is not ongoing at the beginning of the radar and satellite data assimilation period. For more complex cases containing significant amounts of ongoing convection, only assimilating clear-sky satellite retrievals in place of clear-air reflectivity resulted in spurious regions of light precipitation compared to the radar-only experiments. The analyzed tornadic storms in these experiments are sometimes too weak and quickly diminished in intensity in the forecasts. The lessons learned as part of these experiments should lead to improved iterations of the NEWS-e system, building on the modestly successful results described here.

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Modeling Analysis and Prediction Program (MAP)