Toward A Radar-Based Climatology Of Mesocyclones

1. Toward A Radar-Based Climatology Of Mesocyclones 2nd Conference on Severe Storms in Europe Prague, CR John T. Snow, Kevin M. McGrath, and Thomas A. Jones University of Oklahoma Norman, OK

2. Objectives Long-term: Produce a climatology of mesocyclones in the southern Great Plains Immediate: Assess feasibility of constructing such a climatology using data from the national network of WSR-88D radars

3. Procedure • Use high-resolution data from WSR-88D network • Process these data using a realization of the Mesocyclone Detection Algorithm (MDA) developed at NOAA National Severe Storms Laboratory  climatology of mesocyclone detectionsderived from this particular algorithm • Improve the quality of the detection data set by identifying and removing spurious detections climatology based on filtered data set • Associate mesocyclone detections with mesocyclones in nature  severe weather reports

4. Radar Data • High-resolution (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; additional radars available for 2002 • Approximately 2500 hours of data have been processed from each radar of the initial set of six radars, 80% from 2001

5. Algorithm Output • Location of center of mesocyclone  analyze, display on Geographic Information System, associate with severe weather reports • Many other parameters indicating strength, size, intensity of shear, etc… of the mesocyclone  basis for filtering detections • Companion algorithms provide additional information about parent thunderstorm Point: Developed to support operational forecasting, not climatological study

6. Challenges • Large amount of data requires an almost fully automated procedure • High number of “weak” detections (Mesocyclone Strength Rank = 0) tends to obscure the stronger and more significant detections • Obvious spurious detections caused radar characteristics and algorithm limitations • Ground clutter • Anomalous propagation • Incorrectly de-aliased velocity data

7. Filtering Techniques • Remove “false” 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

8. Determining SCIT 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

9. 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 clearer.

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

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

12. “True” 2000 and 2001 KTLX Detections Equal Area Range Bins Histogram Azimuth Histogram

13. 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.

14. “True” Detections Using a 10 km search window KAMA KFWS KAMA KINX KTLX KLBB KFWS KINX

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

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

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

18. Mesocyclones:Cyclonic and Anticyclonic Data are processed twice, once to detect cyclonic mesocyclones, a second time to detect anticyclonic mesocyclones; same filtering technique used each to remove “false” detections

19. Cyclonic Detections after initial + SCIT filtering (10km window): 1215

20. Anticyclonic Detections after initial + SCIT filtering (10km window): 851

22. May 5 – 6, 2002 Mesocyclones Cyclonic Detections after initial filtering: 4601 after SCIT filtering (10km window): 3139

24. May 5 – 6, 2002 Mesoanticyclones Anticyclonic Detections after initial filtering: 4055 after SCIT filtering (10km window): 2617

25. Associating Detections With Tornadoes Use GIS system to associate reported tornadoes temporally and spatially with “true” detections of mesocyclones: time  +30, -10 minutes, space  w/i 10 km N.B.: Some tornado reports not associated with a pre-SCIT filtered mesocyclone detection; SCIT filtering of detections resulted in a few additional tornadoes not being associated with a “true” mesocyclone detection

26. 2000 Tornadic Detections KINX KTLX KSRX KAMA KLBB KFWS

27. 2001 Tornadic Detections KINX KSRX KTLX KAMA KLBB KFWS

28. A Few Early Conclusions

29. A large percentage of MDA detections are spurious  probably real shear regions, but not mesocyclones; study provides different perspective on performance of the operational algorithm • The quality of a mesocyclone detection data set can be significantly improved using rather simple filtering techniques • Results for an area dependent on how radar is operated in that region  “national network”, but each radar is under local control • Surprising number of anticyclonic events  algorithm artifact? • Very small percentage of tornadic mesocyclones  long period – 10 years?, 20 years? – required to develop a climatology with high degree of certainty

30. Work Underway

31. Processing of 2002 data continues… • Expanding the study to include KDDC, KFDR, KICT, and KVNX • Developing filtering techniques that require less human interaction. Specifically, filtering of the concentration of detections at so-called “first trip rings” • Exploring use of existing data set for evaluating of skill in tornadic forecast parameters • Exploring how to group multiple individual detections into families which represent single mesocyclones • Reviewing nature of algorithms to identify possibility that some, perhaps many anticyclonic detections are artifacts of a cyclonic detection (works other way, too!)

32. Acknowledgements • Don Burgess, NSSL • Kelvin Droegemeier, CAPS • Jason Levit, CAPS • Greg Stumpf, NSSL • Andy White, School of Meteorology,OU • Oklahoma NASA Space Grant Consortium • NOAA Warning Decision Training Branch Point: True Oklahoma Weather Center project, would not have been possible without assistance, collaboration by many folks in different organizations

33. Contact Information John T. Snow College of Geosciences University of Oklahoma 100 E. Boyd, Suite 710 Norman, OK 73019 USA Telephone: 405-325-3101 FAX: 405-325-3148 E-mail: jsnow@ou.edu Project URL: http://mesocyclone.ou.edu

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

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

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

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

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