Automated detection of frontal systems from numerical model-generated data

  • Authors:

  • Xiang Li;Rahul Ramachandran;Sara Graves;Sunil Movva;Bilahari Akkiraju;David Emmitt;Steven Greco;Robert Atlas;Joseph Terry;Juan-Carlos Jusem

  • Affiliations:

  • University of Alabama in Huntsville;University of Alabama in Huntsville;University of Alabama in Huntsville;University of Alabama in Huntsville;University of Alabama in Huntsville;Simpson Weather Associates;Simpson Weather Associates;Goddard Space Flight Center, NASA;Science Applications International Corporation;Science Applications International Corporation

  • Venue:
  • Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
  • Year:
  • 2005

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Abstract

Fronts are significant meteorological phenomena of interest. The extraction of frontal systems from observations and model data can greatly benefit many kinds of research and applications in atmospheric sciences. Due to the huge amount of observational and model data available nowadays, automated extraction of front systems is necessary. This paper presents an automated method to detect frontal systems from numerical model-generated data. In this method, a frontal system is characterized by a vector of features, comprised of parameters derived from the model wind field. K-means clustering is applied to the generated sample set of the feature vectors to partition the feature space and to identify clusters representing the fronts. The probability that a model grid belongs to a front is estimated based on its feature vector. The probability image is generated corresponding to the model grids. A hierarchical thresholding technique is applied to the probability image to identify the frontal systems and a Gaussian Bayes classifier is trained to determine the proper threshold value. This is followed by post processing to filter out false signatures. Experiment results from this method are in good agreement with the ones identified by the domain experts.