Wednesday, 7 November 2012
Symphony III and Foyer (Loews Vanderbilt Hotel)
A combined mesoscale and storm-scale ensemble data assimilation and prediction system is developed using the Advanced Research Weather Research and Forecasting (WRF-ARW) model and the ensemble adjustment Kalman filter (EAKF) from the Data Assimilation Research Testbed (DART) software for a very short-range ensemble forecast of the 8 May 2003 Oklahoma City tornadic supercell storm. Traditional atmospheric observations are assimilated into a 45 member mesoscale ensemble over a continental U.S. domain starting three days prior to the event. A one-way nested 45-member storm-scale ensemble is initialized at 2-km horizontal grid spacing centered on the tornadic event at 2100 UTC on the day of the event. Three radar observation assimilation and forecast experiments are conducted at storm-scale using a single moment, a semi-double moment and a true double moment bulk microphysics schemes. Results indicate that the EAKF initializes the supercell storm into the model with good accuracy after 1-hr long radar observation assimilation window. The ensemble forecasts capture the movement of the main supercell storm, as well as the establishment of a hook echo that matches reasonably well with radar observations. The reflectivity structure of the supercell storm using a double moment microphysics scheme appears to compare better to the observations than that using a single moment scheme. In addition, the ensemble system predicts the probability of the strong low level rotation track of the tornadic supercell that correlates well with the observed damage track. The rapid 3-min update cycle of the storm-scale ensemble from the radar observations seems to enhance the skill of the ensemble and confidence of the imminent tornado threat. The encouraging results obtained from this study show promise for a short range probabilistic storm-scale forecast of supercell thunderstorms, which is the main goal of the National Oceanic and Atmospheric Administration's (NOAA's) Warn-on-Forecast initiative.
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