Distributed Collaborative Adaptive Sensing for Hazardous Weather Detection, Tracking, and Predicting*
J. Brotzge1, V. Chandresakar2, K. Droegemeier1, J. Kurose3, D. McLaughlin4, B. Philips4, M. Preston4, and S. Sekelsky4
2Dept. Electrical & Computer Engineering Colorado State
University, Fort Collins, CO 80523-1373
3Dept. Computer Science, University Massachusetts, Amherst MA 01003
Abstract. A new data-driven approach to atmospheric sensing and detecting/predicting hazardous atmospheric phenomena is presented. Dense networks of small high-resolution radars are deployed with sufficient density to spatially resolve tornadoes and other dangerous storm events and overcome the earth curvature-induced blockage that limits todayís ground-radar networks. A distributed computation infrastructure manages both the scanning of the radar beams and the flow of data processing by dynamically optimizing system resources in response to multiple, conflicting end-user needs. In this paper, we provide a high-level overview of a system architecture embodying this new approach towards sensing, detection and prediction. We describe the systemís data rates, and overview various modes in which the system can operate.
*This work was supported by a grant from the Engineering Research Centers program of the National Science Foundation under cooperative agreement EEC-0313747. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
LNCS 3038, pp. 670-679.