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Increasing the Usefulness of a Mesocyclone Climatology

5.4. Increasing the Usefulness of a Mesocyclone Climatology. 21 St Severe Local Storms Conference San Antonio, TX. Kevin M. McGrath  , Thomas A. Jones, and John T. Snow University of Oklahoma Norman, OK.  Corresponding Author. 8/13/02. Objectives.

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Increasing the Usefulness of a Mesocyclone Climatology

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  1. 5.4 Increasing the Usefulness of a Mesocyclone Climatology 21St Severe Local Storms Conference San Antonio, TX Kevin M. McGrath, Thomas A. Jones, and John T. Snow University of Oklahoma Norman, OK Corresponding Author 8/13/02

  2. Objectives • Produce a climatology and resulting statistics of algorithm-detected mesocyclones in the Southern Great Plains • Process a large amount of WSR-88D data using a realization of the NSSL Mesocyclone Detection Algorithm (MDA) • Improve the quality of the data set by developing methods to identify and automatically remove spurious detections

  3. Radar Data • Level II data have been acquired from multiple Southern Plains radars under the auspices of the Collaborative Radar Acquisition Field Test (CRAFT) project • Convective cases from 2000 and 2001 from the initial set of six radars (KAMA, KFWS, KINX, KLBB, KSRX, and KTLX) have been processed using the MDA • Approximately 2500 hours of data have been processed from each radar

  4. Challenges • The relatively high number of “weak” detections tend to obscure the stronger and more significant detections • Spurious detections caused, in part, by: • Ground clutter • Anomalous propagation • Incorrectly dealiased velocity data • Difficultly in associating mesocyclone detections with mesocyclones in nature

  5. Filtering Techniques • Remove MDA detections that meet any of the following criteria: • Located within 5 km of the radar • Located at the maximum unambiguous velocity range • Weak in intensity (Meso. Strength Rank = 0) • Detected in clear air mode (VCP 31 or 32) • Not associable with a SCIT defined storm cell at time of detection Initial Filtering “SCIT” Filtering

  6. Example of SCIT Filtering (KAMA, 20010502 15Z – 20010504 0Z) MDA detections, post-initial filtering. Note region of high ranking, false detections. Mesocyclone track KAMA KAMA MDA detections remaining after passage through the SCIT filter (10 km circular window). Meso track now much more noticeable.

  7. Unfiltered 2000 and 2001 KTLX Detections N = 256,345 Mesocyclone Detections Density of Mesocyclone Detections

  8. “False” 2000 and 2001 KTLX Detections N = 8,067 SCIT Filtered Mesocyclones Density of SCIT Filtered Detections  Using a circular window of 10 km.

  9. “Real” 2000 and 2001 KTLX Detections N = 18,788 SCIT Filtered Mesocyclones Density of SCIT Filtered Detections  Using a circular window of 10 km.

  10. “Real” 2000 and 2001 KTLX Detections Equal Area Range Bins Histogram Azimuth Histogram

  11. Number of MDA Detections  Removed detections with range  5 km, range equal to maximum unambiguous velocity range, MSr = 0, or those detected in VCP = 31 or 32.

  12. “Real” Detections Using a 10 km search window KAMA KFWS KAMA KINX KTLX KLBB KFWS KINX

  13. “Real” Detections Using a 10 km search window KAMA KTLX KINX KINX KSRX KFWS

  14. Density of “Real” Detections Using a 10 km search window KAMA KAMA KTLX KFWS KLBB KLBB KFWS

  15. Density of “Real” Detections Using a 10 km search window KAMA KTLX KINX KFWS KFWS KFWS KINX KSRX

  16. Conclusions and Future Work • A large percentage of MDA detections are spurious • The quality of a mesocyclone detection data set can be significantly improved using rather simple filtering techniques • Processing of 2002 data continues… • Expand the study to include KDDC, KFDR, KICT, and KVNX • Develop filtering techniques that require less human interaction. Specifically, the concentration of detections at so-called “first trip rings”.

  17. Acknowledgements • Don Burgess, NSSL • Kelvin Droegemeier, CAPS • Thomas Jones, OU • Jason Levit, CAPS • John Snow, OU • Greg Stumpf, NSSL • Andy White, School of Meteorology,OU • Oklahoma NASA Space Grant Consortium • NOAA Warning Decision Training Branch

  18. Contact Information • Kevin M. McGrath kmcgrath@ou.edu School of Meteorology,University of Oklahoma 100 E. Boyd, Room 1310 Norman, OK 73019 • Project URL: http://mesocyclone.ou.edu • Data plots of processed cases available

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  21. Determining Filter Radius Correlation of Mesocyclone Low-level Rot. Vel. And SCIT Derived Storm-cell VIL as a Function of Separation Distance Between Centroids % of KTLX Mesocyclone Detections Retained as a Function of SCIT Filter Search Radii

  22. May 5 – 6, 2002 Mesocyclones

  23. May 5 – 6, 2002 Mesoanticyclones

  24. “False” Detections Using a 10 km search window KAMA KTLX KFWS KLBB

  25. “False” Detections Using a 10 km search window KTLX KINX KFWS KSRX

  26. Density of “False” Detections Using a 10 km search window KAMA KTLX KFWS KAMA KFWS KLBB

  27. Density of “False” Detections Using a 10 km search window KTLX KINX KFWS KAMA KSRX KFWS KINX

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