Recognizing Low-Altitude Wind Shear Hazards from Doppler Weather Radar: An Artificial Intelligence Approach

View More View Less
  • 1 Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA 02173
© Get Permissions
Full access

Abstract

This paper describes an artificial intelligence-based approach for automated recognition of wind shear hazards. The design of a prototype system for recognizing low-attitude wind shear events from Doppler radar displays is presented. This system, called WX1, consists of a conventional expert system augmented by a specialized capability for processing radar images. The radar image processing component of the system employs numerical and computer vision techniques to extract features from radar data. The expert system carries out symbolic reasoning on these features using a set of heuristic rules expressing meteorological knowledge about wind shear recognition. Results are provided demonstrating the ability of the system to recognize microburst and gust front wind shear events.

Abstract

This paper describes an artificial intelligence-based approach for automated recognition of wind shear hazards. The design of a prototype system for recognizing low-attitude wind shear events from Doppler radar displays is presented. This system, called WX1, consists of a conventional expert system augmented by a specialized capability for processing radar images. The radar image processing component of the system employs numerical and computer vision techniques to extract features from radar data. The expert system carries out symbolic reasoning on these features using a set of heuristic rules expressing meteorological knowledge about wind shear recognition. Results are provided demonstrating the ability of the system to recognize microburst and gust front wind shear events.

Save