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Toward A Radar-Based Climatology Of Mesocyclones. 2 nd Conference on Severe Storms in Europe Prague, CR. John T. Snow, Kevin M. McGrath, and Thomas A. Jones University of Oklahoma Norman, OK. Objectives. Long-term: Produce a climatology of mesocyclones in the southern Great Plains

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toward a radar based climatology of mesocyclones

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


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

  • 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
radar data
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
algorithm output
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

  • 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
filtering techniques
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

determining scit filter radius
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

example of scit filtering
Example of SCIT Filtering

KAMA, 20010502 15Z – 20010504 0Z

MDA detections, post-initial filtering. Note region of high ranking, false detections.

Mesocyclone track



MDA detections remaining after passage through the SCIT filter (10 km circular window). Meso track now much clearer.

unfiltered 2000 and 2001 ktlx detections
Unfiltered 2000 and 2001 KTLX Detections

N = 256,345

Mesocyclone Detections

Density of Mesocyclone Detections

true 2000 and 2001 ktlx detections
“True” 2000 and 2001 KTLX Detections

N = 18,788

SCIT-Filtered Mesocyclones

Density of SCIT-Filtered Detections

 Using a circular window of 10 km.

true 2000 and 2001 ktlx detections1
“True” 2000 and 2001 KTLX Detections

Equal Area Range Bins Histogram

Azimuth Histogram

number of mda detections
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.

mesocyclones cyclonic and anticyclonic

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



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



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

may 5 6 2002 meso cyclones
May 5 – 6, 2002 Mesocyclones


Detections after initial filtering: 4601 after SCIT filtering (10km window): 3139

may 5 6 2002 meso anticyclones
May 5 – 6, 2002 Mesoanticyclones


Detections after initial filtering: 4055 after SCIT filtering (10km window): 2617

associating detections with tornadoes

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

2000 tornadic detections
2000 Tornadic Detections







2001 tornadic detections
2001 Tornadic Detections







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
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!)
  • 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

contact information
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: [email protected]

Project URL:

false 2000 and 2001 ktlx detections
“False” 2000 and 2001 KTLX Detections

N = 8,067

SCIT Filtered Mesocyclones

Density of SCIT Filtered Detections

 Using a circular window of 10 km.