7.2
Evaluation of convection-allowing ensemble forecasts of extreme rainfall associated with a mesoscale vortex

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Tuesday, 25 January 2011: 3:45 PM
Evaluation of convection-allowing ensemble forecasts of extreme rainfall associated with a mesoscale vortex
613/614 (Washington State Convention Center)
Russ S. Schumacher, Texas A&M University, College Station, TX; and A. J. Clark, M. Xue, and F. Kong

Recent advances in both computing capacity and numerical weather prediction (NWP) model development have allowed for ensembles of forecasts with explicitly predicted convection to be run in real time. As part of the National Oceanographic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) Spring Experiment in 2010, the Center for the Analysis and Prediction of Storms (CAPS) ran a 26-member ensemble at 4-km grid spacing, referred to as the Storm-Scale Ensemble Forecast (SSEF). The capabilities of such ensembles, and their representation of forecast uncertainty, in different weather scenarios are not entirely known, however.

In this study, SSEF forecasts from successive days of extreme rainfall in the southern United States will be examined. From 9--11 June 2010, a mesoscale vortex moved from Texas northeastward into Arkansas and was associated with several periods of heavy rainfall that led to flash flooding. During the overnight hours, mesoscale convective systems (MCSs) developed that moved slowly over relatively small areas in south central Texas on 9 June, north Texas on 10 June, and western Arkansas on 11 June. The ability of individual members of the SSEF to predict these MCSs will be examined, as will the uncertainty represented in the ensemble. Sensitivities to initial conditions and physics perturbations will also be investigated, with the goal of understanding the atmospheric processes that contribute to, or inhibit the development of, heavy-rain-producing MCSs. The SSEF members will also be compared with forecasts using parameterized convection to determine whether the higher-resolution forecasts provided improvements in the predicted distribution and amount of precipitation.