12B.3 Probabilistic evaluation of MCV dynamics and predictability through ensemble forecasting

Thursday, 4 August 2005: 11:00 AM
Ambassador Ballroom (Omni Shoreham Hotel Washington D.C.)
Dan Hawblitzel, Texas A&M University, College Station, TX; and F. Zhang

This study examines the dynamics and predictability of the mesoscale convective vortex (MCV) event of 10-13 June 2003 (BAMEX IOP 8) through ensemble forecasting. The MCV of interest formed from a preexisting upper-level disturbance over the southwest United States on 10 June and matured as it traveled northeastward. The BAMEX field campaign provided a relatively dense collection of upper air observations on 11 June during the mature stage of this vortex. This event is of particular interest to the study of the MCV dynamics and predictability given the anomalously strong and long-lived nature of the circulation and the dense data set.

An ensemble of 20 forecasts using the mesoscale model MM5 with horizontal grid increments of 10 km are employed to evaluate probabilistically the dynamics and predictability of the MCV through the examination of the ensemble mean spread as well as the correlations between different forecast variables at different forecast times. It is shown that a small-amplitude large-scale balanced initial perturbation may result in very large ensemble spread, ranging from a very strong MCV to no MCV at all. Despite similar synoptic-scale conditions, the ensemble MCV forecasts vary greatly depending on intensity and coverage of simulated convection, illustrating the critical role of convection in the development and evolution of an MCV. It is also found that convection near the center of an MCV is an important factor in determining the eventual growth of a surface vortex, and that surface cyclogenesis is favored directly underneath convection and not necessarily the MCV center itself. These findings support the conclusions drawn from the evaluation of the observational analysis and deterministic forecasts. They also illustrate the extreme uncertainty associated with predicting an MCV due to the extreme sensitivity to convection, and demonstrate the advantage of employing ensemble forecasts for MCV prediction.

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