Using a low-order model to characterize tornadoes in multiple-Doppler radar data

This study describes a new multiple-Doppler radar analysis technique for the objective detection and characterization of tornadoes. The model combines a uniform flow, linear shear flow, linear divergence flow, and Rankine vortex (representing the tornado). The latter three fields are allowed to translate with different speeds and directions. A cost-functional J, designed to account for the discrepancy between model and observations, is defined by projecting the model wind in the direction of the radar(s) to obtain the measured radial wind, which is then compared to actual radial wind. This cost-functional is evaluated over both space and time so that observations can be used at the time 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 J.

This model is initially being tested against an analytical observation set, whose error statistics can be systematically varied. We will next use an ARPS (Advanced Regional Predicting System) dataset of a tornado as our observation set. Finally, the model will be tested against real data. The focus of our preliminary tests will be on the impact of number of radars and location relative to the tornado.