Using a K-Means clustering and watershed segmentation algorithm to automatically classify convective storm types
Angelyn G. Kolodziej, CIMMS/Univ. of Oklahoma and NOAA/NSSL, Norman, OK; and V. Lakshmanan
An automated approach to classifying convective storms is based upon both environmental and radar data using a K-Means clustering and watershed segmentation algorithm. Composite reflectivity was tracked and clustered according to three scales: 20 km, 200 km, and 2000 km. For each scale, certain storm types were chosen: short-lived convective cells, supercells, ordinary cells, and convective lines. Using data from five events between May 2008 and May 2009, storms were hand-classified into types. Initial analysis shows that these storms are distinct and can be automatically classified using a decision tree. Ultimate goals of this study are to predict hazards for each storm type using this automated decision tree.
Poster Session , Applications of Artificial Intelligence Methods to Problems in Environmental Science
Wednesday, 20 January 2010, 2:30 PM-4:00 PM, Exhibit Hall B2
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