8B.2 Development of a High-Resolution Rapid Refresh Ensemble (HRRRE) for Severe Weather Forecasting

Tuesday, 8 November 2016: 4:45 PM
Pavilion Ballroom West (Hilton Portland )
David C. Dowell, NOAA/ESRL/GSD, Boulder, CO; and C. R. Alexander, J. Beck, S. G. Benjamin, M. Hu, T. Ladwig, K. H. Knopfmeier, P. S. Skinner, and D. M. Wheatley

We are developing a prototype High-Resolution Rapid Refresh Ensemble (HRRRE) to provide formal ensemble analyses and forecasts of severe weather and other storm-scale phenomena.  Like the deterministic HRRR and time-lagged HRRR ensemble, the HRRRE is an hourly-updated 3-km WRF-ARW system.  For the ensemble Kalman filter data assimilation, the background-error covariances estimated from the 40-member HRRRE include explicit convection.  At selected times, we advance a subset of the ensemble members to forecast lead times of 12 – 18 h.  Owing to computational constraints, we are conducting initial tests on a sub-CONUS domain.

Severe weather cases in the southeast US, of interest to the VORTEX-SE project, have been the focus of retrospective HRRRE testing.  We tested real-time capabilities of the HRRRE prototype in spring 2016 through collaboration with the Warn-on-Forecast project.  From the hourly-cycled ensemble, we initialized HRRRE forecasts at 0000, 0300, 1200, 1500, and 1800 UTC, emphasizing particularly reflectivity and updraft helicity forecasts over regions where severe weather was anticipated.  The 1500 UTC HRRRE also provided initial and boundary conditions for the NSSL Experimental Warn-on-Forecast System for ensembles (NEWS-e), which included sub-hourly radar- and satellite-data assimilation and focused on short-term (0 – 3 h) ensemble forecasts.

At the conference, we will provide examples of the retrospective and real-time HRRRE forecasts, describe overall strengths and weaknesses of the initial prototype, and report on ongoing efforts to improve the system.  We will demonstrate the HRRRE in real time in spring 2017 for the VORTEX-SE field campaign and for the NOAA Hazardous Weather Testbed Spring Experiment.  The experience gained in 2016 and 2017 will benefit our collaboration with the NCEP Environmental Modeling Center to develop a national storm-scale data-assimilation and forecast ensemble system.

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