@inbook{5fbc3967601642aa9312df752c09a1d0,
title = "Distributed collaborative adaptive sensing for hazardous weather detection, tracking, and predicting",
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.",
author = "J. Brotzge and V. Chandresakar and K. Droegemeier and J. Kurose and D. McLaughlin and B. Philips and M. Preston and S. Sekelsky",
year = "2004",
doi = "10.1007/978-3-540-24688-6_87",
language = "English (US)",
isbn = "3540221166",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "670--677",
editor = "Marian Bubak and {van Albada}, {Geert Dick} and Sloot, {Peter M. A.} and Dongarra, {Jack J.}",
booktitle = "Computational Science — ICCS 2004",
address = "Germany",
}