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New (to Meteorology) ideas in storm Identification and tracking

New (to Meteorology) ideas in storm Identification and tracking. lakshman@ou.edu , madison.burnett@noaa.gov , travis.smith@noaa.gov. Where in the world is Lak ?. Thanks to Don MacGorman , Will Agent & Madison Miller for making the Webex possible. The common approach.

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New (to Meteorology) ideas in storm Identification and tracking

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  1. New (to Meteorology) ideas in storm Identification and tracking lakshman@ou.edu, madison.burnett@noaa.gov, travis.smith@noaa.gov

  2. Where in the world is Lak? Thanks to Don MacGorman, Will Agent & Madison Miller for making the Webex possible

  3. The common approach • Objects identified based on a threshold • All pixels above threshold are part of object • Contiguous pixels form an object • Objects tracked by association between frames • Several strategies to associate objects • Closest centroid, greatest overlap, cost function optimization, etc. • In this talk, will introduce new (to meteorology) ideas in storm tracking • These ideas used in tracking missiles since the 80s

  4. Problem: threshold is global • Same threshold does not work for initiating vs. mature storms

  5. Example of threshold problem

  6. Problem: Association is final • Association takes only two frames into account • Bad decisions percolate t0 t1 t2

  7. Example of association problem

  8. Premise … • Try to avoid hard decisions • Use locally adaptive thresholds to identify storms • Based on size of storm rather than data threshold • Different regions of image subject to different thresholds • Keep around several possible tracks • Finalize the associations after a few frames

  9. Enhanced Watershed Transform • Start from local peak • Grow till specified size is reached • In effect, we are trying every possible data threshold • Within limits, of course

  10. EWT Example

  11. Multiple Hypotheses Tracking (MHT) • MHT is based on two useful algorithms: • Hungarian Method or Munkres algorithm • Optimal way to associate cells at one frame to the cells at the next frame using linear programming • Based on a “cost” for each pair: could be simply distance between centroids or something more complex • Murty’s K-best association • Way to get not just the best way to associate cells, but the next best way, and the next best way, etc. • Ranked set of associations

  12. MHT • In practice, will lead to combinatorial explosion • So, prune to keep around only K total possibilities • “Confirm” cells at frame t-N • N and K depend on the type of data you have t0 t1 t0 t1 t2

  13. EWT and MHT in QC of Az-Shear • Azimuthal Shear a very noisy field • Rotation tracks (accumulation of Az-Shear) even noisier • A problem at even one time step persists for long time • Can use EWT and MHT to QC the Az-shear field • Identify “cells” of Az-Shear • See which cells potentially pan out • The real-time accumulation uses all Az-Shear from current time, but only the “cells” from previous time steps that are associated with one of the K-best associations …

  14. Rotation Tracks Cleanup

  15. Summary • Can avoid/postpone hard decisions in tracking • Use locally adaptive thresholds to identify storms • Paper in J. Tech. 2009 • Keep around several possible tracks to decide later • In situations where strict causality can be avoided • Paper coming …

  16. Questions?

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