11B.4
Probabilistic prediction of low-level vortices associated with mesoscale vortex tornadoes using EnKF data assimilation and implications for warn-on-forecast

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Wednesday, 26 January 2011: 4:45 PM
Probabilistic prediction of low-level vortices associated with mesoscale vortex tornadoes using EnKF data assimilation and implications for warn-on-forecast
615-617 (Washington State Convention Center)
Nathan Snook, CAPS/Univ. of Oklahoma, Norman, OK; and M. Xue and Y. Jung

In operational severe weather forecasting, the transition from the current paradigm of “warn-on-observation” to “warn-on-forecast” (Stensrud 2009 BAMS) to increase lead-time for localized severe weather warnings (for, e.g., flash floods, severe thunderstorms, and tornadoes) represents a significant challenge. Successful implementation of a warn-on-forecast system will require accurate short-term (0-3 hour), storm-resolving, probabilistic numerical forecasts which assimilate data from multiple observing platforms, particularly Doppler radar. In this study we examine the feasibility of one such analysis and forecast system, using an ensemble Kalman filter (EnKF) to assimilate radar data from 4 CASA X-band radars and 5 WSR-88D S-band radars for the tornadic mesoscale convective system of 8-9 May 2007 over western and central Oklahoma. During this event, 3 tornadoes occurred just to the north of the CASA IP-1 radar network, 2 of EF-1 intensity, and 1 of EF-0 intensity. Ensemble-based probabilistic forecast products are generated, focusing on the prediction of near-surface radar reflectivity and low-level circulations.

The Advanced Regional Prediction System (ARPS) EnKF data assimilation system is used to assimilate CASA and WSR-88D radar data. The ensemble used contains 40 members, and radar data are assimilated every 5 minutes for 1 hour. Three experiments are conducted, examining the impact of CASA radar data and use of a mixed-microphysics ensemble. Results from these 3 experiments will also be compared to those of ongoing experiments assimilating mesonet, surface, and aircraft observations, and to results obtained using a 3DVAR assimilation method and an identical grid setup.

During the forecast period, the experiment using both CASA radar data and a mixed-microphysics ensemble produces the best forecast of low-level reflectivity structure and mesocyclone location. The ensemble system is successful in predicting with high probability the mesoscale circulation associated with the first of three reported tornadoes; the control experiment indicates a maximum probability of 0.65 of a significant low-level vortex being present, located within 10 km of the reported tornado and its corresponding mesoscale circulation as observed by WSR-88D. The results of these experiments suggest that both the inclusion of additional low-level radar data and the use of a mixture of microphysics schemes within an ensemble can markedly improve the quality of short-term storm-scale numerical forecasts, and that an operational EnKF-based ensemble analysis and probabilistic forecast system to support a convective-scale warn-on-forecast paradigm may soon become plausible, limited only by the availability of sufficient computational resources.