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.
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© 2004 Springer-Verlag Berlin Heidelberg
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Brotzge, J. et al. (2004). Distributed Collaborative Adaptive Sensing for Hazardous Weather Detection, Tracking, and Predicting. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science - ICCS 2004. ICCS 2004. Lecture Notes in Computer Science, vol 3038. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24688-6_87
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DOI: https://doi.org/10.1007/978-3-540-24688-6_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22116-6
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