Speeding up a large-scale filter

Wolfson et al. introduced a storm tracking algorithm, called the Growth and Decay Storm Tracker, in which the large-scale features were extracted from radar data fields by using on elliptical tilter. The elliptical filter as introduced was computationally too expensive to be performed in real time. In this paper, it is shown that although the elliptical filter is nonlinear , it can be decomposed into two parts, one of which is, under some simplify ing assumptions, linear and shift-invariant. The linear component can be ac celerated using fast algorithms available ro compute the digital Fourier tr ansform (DFT). Furthermore. it is shown that the nonlinear part can be writ ten as an update equation, thus reducing the amount of computer memory requ ired. With these improvements to the basic large-scale filtering technique, this paper reports that the large-scale filtering can be done 1-2 orders o f magnitude faster. The improvement makes it possible to use the large-scale filtering techniqu e in situations where the computational time and memory requirements have b een prohibitive.