Mesocyclone Climatology of the Southern Great Plains using the NSSL Mesocyclone Detection Algorithm

1
Mesocyclone Climatology of the Southern Great
Plains using the NSSL Mesocyclone Detection
Algorithm

• Wednesday 26 March 2003

Kevin M. McGrath School of Meteorology,
University of Oklahoma http//mesocyclone.ou.edu
2
Objectives

• Produce a climatology of mesocyclones in the
  Southern Great Plains using the NSSL Mesocyclone
  Detection Algorithm (MDA)
• Improve the quality of the data set by developing
  methods to identify and automatically remove
  spurious detections
• Perform post-processing analysis and generate
  statistics of output
• Geographic variability in mesocyclone occurrences
• Atmospheric features influencing detections

3
Motivation

• Large percentage of mesocyclones are associated
  with severe weather
• JDOP (1976 1978) determined that 50 of the
  observed mesocyclones were tornadic 80 were
  severe and/or tornadic (Burgess et al. 1979)
• Burgess and Lemon (1991) reported that 31 of the
  observed mesocyclones were tornadic 94 were
  severe and/or tornadic
• A recent study indicated as low as 25.5 of
  mesocyclones are tornadic (Trapp et al. 2002)
• Drop in percentage likely due to increase in
  detection of very weak circulations
• Difficultly in constructing a climatology of
  mesocyclones using in situ observations
• Build on Pittsburgh, PA study (Mitchell et al.
  2000)
• Assist in training and guidance

4
Radar Data

• Level II data acquired from multiple Southern
  Plains radars under the auspices of the
  Collaborative Radar Acquisition Field Test
  (CRAFT) project

• Convective cases from March 2000 through June
  2002 from the initial set of six radars (KAMA,
  KFWS, KINX, KLBB, KSRX, and KTLX) have been
  processed using the MDA

• KDDC, KFDR, KICT, and KVNX added to CRAFT feed in
  spring of 2002. Subsequent data were processed
  during this study data sets too small to include
  in results.

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Mesocyclone Detection Algorithm

• Attempts to detect all storm-scale vortices (1-10
  km diameter) and then diagnose them to determine
  if they are significant
• Process
• Find shear segments and construct 2D vortices
• Vertically associate 2D vortices ? 3D features
• Classify and diagnose
• Adaptable parameters used were those suggested by
  the NSSL
• Data processed in cyclonic mode
• Detections defined on a per-volume-scan basis
  no time correlation attempted
• Adapted to process archived data

6
Near Storm Environment Data

• RUC NSE data incorporated into suite of
  algorithms when used in real-time. The velocity
  dealiasing algorithm uses upper-level wind
  information and the first guess storm cell motion
  are estimated for storm cell identification and
  tracking.

• NSE data was not available for this project FSL
  sounding data used instead
• NSSL software utilized to estimate 253K and 273K
  heights
• Tests indicate MDA not particularly sensitive to
  this data

7
Initial Results
(May 29 30, 2001)
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Challenges

• The relatively high number of weak detections
  tend to obscure the stronger detections
• Spurious detections (often of strong rank)
  caused, in part, by
• Incorrectly dealiased velocity data (especially
  near boundaries parallel to beam)
• Beam broadening and cone-of-silence
• Ground clutter and beam blockage
• Sidelobe contamination
• Anomalous propagation

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Filtering Techniques 1 Initial Filter
Raw MDA output often contains large number of
spurious and weak detections ? noise

• Initial Filter Criteria
• Located within 5 km of the radar (3.0)
• Located at the maximum unambiguous velocity range
  (first trip ring, commonly located at 147 km)
  (0.40)
• Weak in intensity (Mesocyclone Strength Rank 0)
  (88.7)
• Detected in clear air mode (VCP 31 or 32) (4.0)
• 8.8 of raw detections retained

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Filtering Techniques 1 Initial Filter
(KAMA, 20010502 15Z 20010504 0Z)
Raw MDA detections (N 7,866)
KAMA
KAMA
MDA detections remaining after application of
initial filter (N 948, 12.1 retained)
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Results Post Initial Filtering
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Storm Cell Identification and Tracking (SCIT)
Algorithm

• Designed to identify, characterize, track, and
  forecast the short-term movement of storm cells
  identified in three dimensions (Johnson et al.
  1998)
• Maximum reflectivity must be ? 30 dBz
• Bases detections solely on reflectivity data
• Thresholds used in this study were those
  suggested by the NSSL and are commonly used in an
  operational setting

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Filtering Techniques 2 SCIT Filter

• Attempts to correlate mesocyclone detections with
  the associated storm cells as defined by the SCIT
  algorithm
• Filter searches for storm cell centroids within a
  user defined circular window centered on each
  mesocyclone detection centroid during the same
  volume scan

• Detections that do not have a storm cell centroid
  simultaneously within the search window were
  labeled as false
• Similar techniques were employed by Thomas Jones

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Determining Filter Radius
Correlation of Mesocyclone Low-level Rot. Vel.
And SCIT Derived Storm Cell VIL as a Function of
Separation Distance Between Centroids
Percent of Mesocyclone Detections Retained as a
Function of SCIT Filter Search Radius
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Example of SCIT Filtering
(KAMA, 20010502 15Z 20010504 0Z)
MDA detections, post-initial filtering. Note
region of high ranking, false detections. (N
948)
Mesocyclone track
KAMA
KAMA
MDA detections remaining after passage through
the SCIT filter (10 km circular window). Meso
track now much more noticeable. (N 544, 6.9
of original)
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Results Post SCIT Filtering
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True Detections Using a 10 km search window
KINX
KTLX
KAMA
KSRX
KLBB
KFWS
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Density of True Detections Using a 10 km search
window
KINX
KTLX
KAMA
KSRX
KLBB
KFWS
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Density of True Detections Using a 10 km search
window (bin 77 km2)
KAMA
KTLX
KINX
75
90
75
60
90
75
KLBB
KFWS
KSRX
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False Detections Using a SCIT Filter 10 km
search window
KINX
KTLX
KAMA
KSRX
KLBB
KFWS
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Density of False Detections Using a 10 km
search window
KINX
KTLX
KAMA
KSRX
KLBB
KFWS
22
Density of False Detections Using a 10 km
search window (bin 77 km2)
KAMA
KTLX
KINX
90
90
90
75
90
75
KLBB
KFWS
KSRX
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KAMA Beam Blockage
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Concentration of False Detections NNW of KTLX
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Equal Area Range Distribution 0 229 km (bin
5,542 km2)
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Equal Area Range Distribution 0 146 km (bin
2,239 km2)
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Time of Detection
KTLX
KAMA
KLBB
KFWS
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Time of Detection
KTLX
KINX
KFWS
KSRX
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Mesocyclone Attributes
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Mesocyclone Strength as a Function of Time
True detections
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Mesocyclone Strength as a Function of Time
False detections
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Mesocyclone Strength as a Function of Equal Area
Range Real detections
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Mesocyclone Strength as a Function of Equal Area
Range False detections
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Mesocyclone Strength Distribution
Meso Strength Distribution of True SCIT
Filtered Detections
Meso Strength Distribution of False SCIT
Filtered Detections
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Mesocyclone Base vs. Range
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May 5 6, 2002 Mesocyclones
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May 5 6, 2002 Mesoanticyclones
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June 12 14, 2002 Severe Squall Line
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Conclusions

• A large percentage of MDA detections are false
  in the sense that they are not mesocyclones
• The quality of a mesocyclone detection data set
  can be significantly improved using simple
  filtering techniques
• Spatial distribution varies geographically
• Temporal offset between real and false
  detections
• MDA produces more true detections with VCP 11
• Possible radar calibration issues identifiable

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Recommendations

• Develop filtering techniques that require less
  human interaction, i.e., location of first trip
  ring
• Algorithm should process data in cyclonic and
  anticyclonic modes
• Importance of utilizing ground clutter
  suppression, updated quarterly
• Multi-radar algorithms
• Operate in VCP 11
• Combine MDA and TDA (VDDA Vortex Detection and
  Diagnosis Algorithm)
• Examine seasonal variations in mesocyclone
  climatology
• Remove detections w/ range ? 10 km and/or MSR ?
  3/5

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Acknowledgements

• Don Burgess
• Karen Cooper
• Greg Stumpf

Kelvin Droegemeier Jason Levit Kevin Thomas
Courtney Garrison and staff Thomas Jones John
Snow and staff Andy White
Friends and Family NOAA Warning
Decision Training Branch Oklahoma NASA
Space Grant Consortium