15B.5 Assessment of assimilating Cloud Water Path into the NSSL Experimental Warn-on-Forecast System for Ensemble (NEWS-e) during the 2016 Hazardous Weather Testbed

Thursday, 26 January 2017: 4:30 PM
Conference Center: Tahoma 4 (Washington State Convention Center )
Thomas A. Jones, CIMMS, Norman, OK; and D. M. Wheatley, K. H. Knopfmeier, P. S. Skinner, D. C. Dowell, T. T. Ladwig, C. R. Alexander, P. Minnis, and R. Palikonda

The goal of the Warn-on-Forecast project is to provide probabilistic short-term (0-3 h) forecast guidance for high impact weather events such as tornadoes, hail, high winds, and flash flooding. The prototype system known as the NSSL Experimental Warn-on-Forecast System for ensembles (NEWS-e) uses the WRF-ARW model and employs an ensemble adjustment Kalman filter (EaKF) data assimilation technique on a convection-permitting (3 km) grid. Initial conditions are provided by an experimental High Resolution Rapid Refresh ensemble (HRRR-e). WSR-88D reflectivity and radial velocity and geostationary satellite cloud water path (CWP) retrievals are assimilated over the regional domain at 15 minute intervals between 1800 UTC and 0300 UTC the following day when severe weather was expected to occur.  

            This research describes the impact of assimilating CWP into the NEWS-e during realtime operations conducted during the spring 2016 Hazardous Weather Testbed (HWT) in Norman, OK. Between April and June 2016, forecasts were generated for over a dozen severe weather events that encompass multiple environments and storm modes. This research assesses the overall positive and negative impacts of assimilating CWP during the HWT compared to a parallel set of experiments run that only assimilate radar reflectivity and radial velocity. Initial results indicate an improvement in the analysis of cloud properties which leads into improvements in the analysis and forecasts of thermodynamic parameters such as surface temperature and incoming solar radiation. Assimilating CWP also led to quicker convective initiation (CI) within the model in several instances. Improvements in the near-storm environment coupled with quicker CI led to better forecasts of rotating convection during the early phases of several severe weather events.

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