ADAPTIVE SENSING ( DCAS ) FOR IMPROVED DETECTION , UNDERSTANDING , AND PREDICTING OF ATMOSPHERIC HAZARDS

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 midto uppertroposphere. 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. Distr ibuted 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