Extended Abstract (840K)P1.2
Using a low-order model to detect and characterize tornadoes in multiple-Doppler radar data
Corey K. Potvin, University of Oklahoma, Norman, OK; and A. M. Shapiro, T. Y. Yu, M. Xue, and J. Gao
A new multiple-Doppler radar analysis technique is presented for the objective detection and characterization of tornadoes. The technique consists of fitting radial wind data from two or more radars to a simple analytical model of a tornado and its near-environment. The model combines a uniform flow, linear shear flow, linear divergence flow (all of which comprise a broadscale flow), and a modified combined Rankine vortex (representing the tornado), several characteristics of which are allowed to vary with height. The latter three fields are allowed to translate. A cost-function accounting for the discrepancy between the model radial winds (projection of the model wind vectors in the direction of the radars) and the observed radial winds is evaluated over both space and time so that observations can be used at the actual times and locations they were acquired, thus bypassing the need for time interpolation, moving reference frames or other ad-hoc procedures. The parameters in the low order model are determined by minimizing this cost function.
The technique has previously been tested using analytically-simulated observations whose wind field and error characteristics were systematically varied, as well as an ARPS (Advanced Regional Predicting System) high-resolution simulation of a tornado. In this presentation, the technique is applied to real multiple-Doppler observations of tornadoes. The method is tested for a range of sampling strategies and radar-tornado geometrical configurations. The latest results will be presented.
Poster Session 1, Recent Developments in Atmospheric Applications of Radar and lidar
Tuesday, 22 January 2008, 9:45 AM-11:00 AM, Exhibit Hall B
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