USING A LOW-ORDER MODEL TO DETECT AND CHARACTERIZE INTENSE VORTICES IN MULTIPLE-DOPPLER RADAR DATA

Abstract

A new multiple-Doppler radar analysis technique is presented for the objective detection and characterization of intense vortices. The technique consists of fitting radial wind data from two or more radars to a simple analytical model of a vortex and its near-environment. The model combines a uniform flow, linear shear flow, linear divergence flow (all of which comprise a broadscale flow), and modified combined Rankine vortex. The vortex and its environment are allowed to translate. A cost-function accounting for the discrepancy between the model and observed radial winds is evaluated over space and time so that observations can be used at the actual times and locations they were acquired. The parameters in the low-order model are determined by minimizing this cost function.The development of the method is initially guided by emulated radial velocity observations of analytical vortices. A high-resolution Advanced Regional Prediction System (ARPS) simulation of a supercellular tornado is then used to generate more realistic pseudo-observations. Finally, the technique is tested using real dual-Doppler tornado and mesocyclone observations from a variety of radar platforms including Weather Surveillance Radar - 1988 Doppler (WSR-88D), Terminal Doppler Weather Radar (TDWR), Shared Mobile Atmospheric Research and Teaching Radar (SMART-R), and Doppler on Wheels (DOW). The technique shows skill in detecting intense vortices and, when the vortex is well-resolved, in retrieving key model parameters including vortex location, translational velocity, radius and maximum tangential wind speed. In cases where the vortex is not well-resolved, additional vortex characteristics computed from the retrieved model parameters and verified against radial velocity observations can still provide useful information about vortex size and strength

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