New to Meteorology ideas in storm Identification and tracking

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Where in the world is Lak? Thanks to Don MacGorman, Will

Where in the world is Lak?

Thanks to Don MacGorman, Will Agent

& Madison Miller for making the Webex possible
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The common approach Objects identified based on a threshold All pixels

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

Problem: threshold is global

Same threshold does not work for initiating vs.

mature storms
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Example of threshold problem

Example of threshold problem

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Problem: Association is final Association takes only two frames into account Bad decisions percolate

Problem: Association is final

Association takes only two frames into account
Bad decisions

percolate
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Example of association problem

Example of association problem

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Premise … Try to avoid hard decisions Use locally adaptive thresholds

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
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Enhanced Watershed Transform Start from local peak Grow till specified size

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
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EWT Example

EWT Example

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Multiple Hypotheses Tracking (MHT) MHT is based on two useful algorithms:

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
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MHT In practice, will lead to combinatorial explosion So, prune to

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
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EWT and MHT in QC of Az-Shear Azimuthal Shear a very

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 …
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Rotation Tracks Cleanup

Rotation Tracks Cleanup

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Summary Can avoid/postpone hard decisions in tracking Use locally adaptive thresholds

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 …