Abstract
Increasing tornado and severe storm warning lead time (lead time is defined here as the elapsed time between the issuance of a watch or warning and the time at which the anticipated weather event first impacts the specified region) through the use of radar observations has long been a challenge for researchers and operational forecasters. To improve lead time and the probability of detecting tornadoes while decreasing the false alarm ratio, a greater understanding, obtained in part by more complete observations, is needed about the region of storms within which tornadoes form and persist. Driven in large part by this need, but also by the goal of using numerical models to explicitly predict intense local weather such as thunderstorms, the National Science Foundation established, in fall 2003, the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA). CASA is developing a revolutionary new paradigm of using a network of small, closely spaced, inexpensive, low-power dual-polarization Doppler weather radars to overcome the inability of widely spaced, high-power radars to sample large regions of the lower atmosphere owing to the curvature of earth given that zero or negative beam elevation angles are not allowed. Also, current radar technology operates mostly independently of the weather and end-user needs, thus producing valuable information on storms as a whole but not focused on any specific phenomenon or need. Conversely, CASA utilizes a dynamically adaptive sensing paradigm to identify, and optimally sample, multiple targets based upon their observed characteristics in order to meet a variety of often competing end-user needs.
The goal of this study is to evaluate a variety of adaptive sampling strategies for CASA radars to assess their effectiveness in identifying intense low-altitude vortices. Such identification, for the purposes of this study, is defined as achieving a best fit of simulated observations to an analytic model of a tornado or mesocyclone. Several parameters are varied in this study including the size of the vortex, azimuthal sampling interval, distance of the vortex from the radar, and radar beamwidth.
Results show that, in the case of small vortices, adaptively decreasing the azimuthal sampling interval (i.e., overlapping beams) is beneficial in comparison to conventional azimuthal sampling that is approximately equal to the beamwidth. However, the benefit is limited to factors of 2 in overlapping. When simulating the performance of a CASA radar in comparison to that of a Weather Surveillance Radar-1988 Doppler (WSR-88D) at close range, with both operating in the conventional nonoverlapping mode, the WSR-88D (with a beamwidth about half that of a CASA radar) performs better. However, when overlapping is applied to the CASA radar, for which little additional processing time is required, the results are comparable. In effect, the sampling resolution of a radar can be increased simply by decreasing the azimuthal sampling interval as opposed to installing a larger antenna.
Joint Adaptive Sampling Interval and Power Allocation for Maneuvering Target Tracking in a Multiple Opportunistic Array Radar System
