Mining NEXRAD Radar Data: An Investigative Study

نویسندگان

  • Xiang Li
  • Rahul Ramachandran
  • John Rushing
  • Sara Graves
  • Kevin Kelleher
  • Douglas Kennedy
  • Jason Levit
چکیده

A collaborative team of meteorologists and data mining experts conducted a case study to detect and classify mesocyclone signatures in WSR-88D radar data using mining techniques. Radar data for May 6, 1994 and May 11, 1992 from Norman and Tulsa, Oklahoma was used in this case study. Two Mesocyclone Detection Algorithms (MDA) were used in this study. One (NSSL MDA) is a computationally optimized version of the National Severe Storm Laboratory (NSSL) MDA. The second (UAH MDA) was created based upon the original NSSL algorithm, but with some image processing techniques. The primary difference between the two algorithms is in the technique used to segment the two dimensional (2D) mesocyclone signatures. The UAH MDA uses a region growing technique; hence the shape of the feature is no longer a restriction. True or false labels were assigned to the mesocyclone features generated from the MDA by comparing with a truth set derived by an expert using the NSSL algorithm. This labeled feature data set was then used in a series of analysis experiments. The objective of these experiments was to: Evaluate the performance of different classifiers in their ability to distinguish between a true mesocyclone signature versus an artifact or noise; Optimize the classification results by selecting key parameters from the feature data set using different feature reduction techniques; Explore the patterns in the feature data set using various clustering algorithms; and determine the suitability of the algorithm as a data mining tool.

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تاریخ انتشار 2003