Automated gust front detection using knowledge-based signal processing

Abstract

The latest airport surveillance radar, enhanced with a wind shear processor (ASR-9 WSP), is being developed as a less expensive alternative weather radar. Although gust fronts are visible to human observers in ASR-9 WSP imagery, the lower sensitivity and less reliable Doppler measurements of this radar make automated gust front detection a much more challenging problem. Using machine intelligence and knowledge-based signal processing techniques developed in the context of automatic target recognition, a machine intelligent gust front algorithm (MIGFA) is constructed that is radically different from previous algorithms. Developed initially for use with ASR-9 WSP data, MIGFA substantially outperforms a state-of-the-art gust from detection algorithm based on earlier approaches. These results also indirectly suggest that MIGFA performance may be nearly as good as human performance. Preliminary results of an operational test period (two months, approximately 15000 scans processed) are presented. >