P11.3 DISTRIBUTED COLLABORATIVE ADAPTIVE SENSING (DCAS) FOR IMPROVED DETECTION, UNDERSTANDING, AND PREDICTING OF ATMOSPHERIC HAZARDS David J. McLaughlin 1* , V. Chandrasekar 2 , Kelvin Droegemeier 3, , Stephen Frasier 1 , Jim Kurose 4 , Francesc Junyent 1 , Brenda Philips 1 , Sandra Cruz-Pol 5 , and Jose Colom 5 1 Dept. of Electrical and Computer Engineering, University of Massachusetts 2 Dept. of Electrical and Computer Engineering, Colorado State University 3 School of Meteorology, University of Oklahoma 4 Dept. of Computer Science, University of Massachusetts 5 Dept. of Electrical and Computer Engineering, University of Puerto Rico, Mayaguez 1. INTRODUCTION Today’s weather observing radars are designed for long-range (up to hundreds of km) coverage with single-beam antennas designed to comprehensively map winds, precipitation, and other phenomena over large volumes of the mid- to upper- troposphere. Data collected by these sensors serve as critical inputs to weather-related decision making in many public and private sector functions (such as hazardous weather warning, transportation, agriculture, energy, among others) and new uses continue to develop as data dissemination [1] and computational capabilities improve. Although the coverage of these radars is adequate for many situations, coverage at low altitudes far away from the radars is insufficient for many applications due to Earth’s curvature and terrain-induced blockage [2]. For example, the WSR-88D (NEXRAD) system is unable to view ~ 80% of the troposphere’s volume below 3 km altitude. This prevents the system from detecting the full vertical rotation of most tornadoes and can limit the accuracy of precipitation estimates near the surface. Long-range radars operate at wavelengths in the 5-10 cm range in order to minimize attenuation due to precipitation, and this necessitates the use of physically large antennas to achieve high spatial resolution. Despite the use of large antennas, spatial broadening of the radar beam with increasing range prevents these systems from resolving structures on the sub-km scale over most of the coverage volume. Distributed Collaborative Adaptive Sensing (DCAS) is a new approach to radar sensing of the atmosphere being investigated to overcome the coverage limitations inherent in long-range radar networks. Distributed refers to the use of large numbers of small solid-state radars, spaced appropriately to overcome blockage due to the Earth’s curvature and improve resolution degradation caused * Corresponding author address: David J. McLaughlin, Director, Center for Collaborative Adaptive Sensing of the Atmosphere (CASA), College of Engineering, University of Massachusetts, Amherst, MA 01003, mclaughlin@ecs.umass.edu by beam spreading. In addition to providing the potential for high-resolution sampling throughout the entire troposphere, this distributed concept lends itself to the efficient utilization of low-power solid-state radars. These radars (once they are developed) are highly reliable, inexpensive, adaptive, and can operate collaboratively, via rapid coordinated targeting of multiple radar beams, based on atmospheric and hydrologic analysis tools (detection, tracking, and predicting algorithms) that diagnose weather conditions in real-time and re-steer radar beams onto atmospheric regions where threats exist. Collaborative operation is envisioned as a means to achieve greater sensitivity, precision, and resolution than is possible with a single beam by coordinating the beam-positions of multiple radars and by processing echoes from multiple beams viewing the same scattering region. Adaptive refers to the ability of these radars and their associated computing and communications infrastructure to rapidly reconfigure in response to changing conditions in a manner that optimizes the systems’ ability to respond to competing end-user demands. For example, a DCAS radar network might pinpoint tornado locations with extremely high spatial resolution for public warning while simultaneously mapping the horizontal wind field associated with the parent thunderstorm and providing quantitative precipitation estimates for input to distributed hydrological models. The system accomplishes this by continually adjusting both radar parameters (such as pulse-repetition frequency, scan rate/dwell time) and computational functions (such as calculating spectral moments versus calculating full Doppler spectra) during the volume scans of multiple coordinated radars, all in response to changing weather. The U.S. National Science Foundation recently established an Engineering Research Center titled the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA), formed by a consortium of four universities, namely (listed alphabetically), Colorado State University, University of Massachusetts (lead University), University of Oklahoma, and University of Puerto Rico at Mayaquez, and a partnership with industry and government labs to create the underlying knowledge, technology, and systems-engineering trade-off space for realizing future DCAS systems and to conduct