Machine Intelligent Gust Front Algorithm
Abstract
The Federal Aviation Administration has sponsored research and development of algorithms for automatic gust front detection as part of a suite of hazardous weather detection capabilities for airports. These algorithms are intended for use with Doppler radar systems, specifically the Terminal Doppler Weather Radar (TDWR) and the Airport Surveillance Radar enhanced with a Wind Shear Processor (ASR-9 WSP). Although gust fronts are observable with fairly reliable signatures in TDWR data, existing gust front detection algorithms have achieved only modest levels of detection performance. For smaller airports not slated to receive a dedicated TDWR, the ASR-9 WSP will provide a less expensive wind shear detection capability. Gust front detection in ASR-9 SP data is an even more difficult problem, given the reduced sensitivity and less reliable Doppler measurements of this radar. A Machine Intelligent Gust Front Algorithm (MIGFA) has been constructed at Lincoln Laboratory that is a radical departure from previous design strategies. Incorporating knowledge-based, signal- processing techniques initially developed at Lincoln Laboratory for automatic target recognition, MIGFA uses meteorological knowledge, spatial and temporal context, conditional data fusion, delayed thresholding, and pixel-level fusion of evidence to improve gust front detection performance significantly. In tests comparing MIGFA with an existing state-of-the-art algorithm applied to ASR-9 WSP data, MIGFA has substantially outperformed the older algorithm. Gust front detection, Machine intelligence, MIGFA, Interest images, Wind shear detection, Doppler weather radar, ASR-9, Wind shear processor, Functional template correlation.
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 04, 1993
- Accession Number
- ADA273695
Entities
People
- Richard L. Delanoy
- Seth W. Troxel
Organizations
- Massachusetts Institute of Technology