Microburst Detection Using Agent Networks

Abstract

Automatic detection of microbursts with Doppler radar data is an interesting challenge. Traditionally, manual detection is performed by trained meteorologists who scan through the volumetric radar data for appropriate signatures, bringing to bear human powers of pattern detection and analysis. More recently, automatic systems have been devised to perform this detection using computer techniques together with fuzzy logic. Here a system that attempts to emulate human detection using the technology of agent networks (i.e., networks of cooperating asynchronous software entities) is presented. In this approach, agents detect reflectivity cores and high divergent shear zones. Their output is integrated by higher-level agents to detect microbursts and to track microbursts through time. The system is implemented in the Java language and is successfully detecting microbursts in data from radars near Sydney and Darwin in Australia.