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

1Center for Analysis and Prediction of Storms, University of Oklahoma, 100 East Boyd Norman, OK 73019-1012
jbrotzge@ou.edu
kkd@ou.edu

2Dept. Electrical & Computer Engineering Colorado State University, Fort Collins, CO 80523-1373
chandra@engr.colostate.edu

3Dept. Computer Science, University Massachusetts, Amherst MA 01003
kurose@cs.umass.edu

4Dept. Electrical and Computer Engineering, University Massachusetts, Amherst MA 01003
mclaughlin@ecs.umass.edu
bphilips@ecs.umass.edu
mpreston@ecs.umass.edu
sekelsky@ecs.umass.edu

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 today’s 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 system’s 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.

Last modified: