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Tornado Detection Algorithm (TDA)

Tornado Detection Algorithm (TDA). By: Jeffrey Curtis and Jessica McLaughlin. Build 9. Typically triggered after a tornado event occurred Linked to the Mesocyclone Detection Algorithm Smaller rotations missed by algorithm. Build 10.

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Tornado Detection Algorithm (TDA)

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  1. Tornado Detection Algorithm (TDA) By: Jeffrey Curtis and Jessica McLaughlin

  2. Build 9 • Typically triggered after a tornado event occurred • Linked to the Mesocyclone Detection Algorithm • Smaller rotations missed by algorithm

  3. Build 10 • Aimed to address low probability of detection by the Build 9 • TDA separated from the Mesocyclone Detection Algorithm • Built to detect significant regions of shear • Higher probability of detection • Distinguishes between tornadic and non-tornadic shear

  4. The Algorithm • 1-D pattern vectors identified • Gate to gate shear – velocity difference between two adjacent range bins • Minimum shear value • Detects only cyclonic rotation

  5. The Algorithm (cont.) • 2-D features created by the combination of three or more 1-D pattern vectors • Classifies features in order of shear values 35, 30, 25, 20, 15 and 11 m s-1 • Sorts 2-D features by increasing height

  6. The Algorithm (cont.) • Checks vertical continuity of 2-D features • Strongest circulation declared base • 3-D features composed of a minimum of three 2-D features • Ideal case of no gaps within elevation sweeps • No more than one elevation scan gap

  7. Fig. 1. A schematic of a 3D vortex formed by three 2D vortices. (Mitchell et al, 1998)

  8. Tornado Vortex Signature (TVS) • 3 dimensional circulation • Base extends to the 0.5 radar elevation height or has a base below 2000ft. (600m) above radar level • Shown by a red triangle and is coded red in the table

  9. Minimum velocity difference required is 25 ms-1 Circulation depth of at least 1.5 km TVS (cont.)

  10. Elevated Tornado Vortex Signature (ETVS) • 3 dimensional circulation • Base does not extend to the 0.5 radar elevation height and has a base above 2000ft. (600m) above radar level • Shown by a yellow triangle and is coded yellow in the table

  11. Minimum velocity difference required is 36 ms-1 Circulation depth of at least 1.5 km ETVS (cont.)

  12. Positives of TDA • Uses gate to gate instead of only strong shear values • gate to gate is more closely related to tornadic circulation • Mesocyclone does not need to be present to search for strong velocities • Searches all velocity pairs

  13. Positives (cont.) • More information given to the observer • Can determine shear type (TVS or ETVS) • Can determine base or depth of the circulation • Parameters can be changed to allow for better performance • Can allow for a higher probability of detecting significant regions of shear

  14. Negative of TDA • Doesn’t detect areas of anti-cyclonic rotation • High FAR (False Alarm Rate) • Can cause too many warnings to be made to the public • Build 9 had lower FAR

  15. Negatives (cont.) • Relationship between tornadoes and ETVS is not fully researched • Must fully complete radar scan before TVS/ETVS is resolved

  16. Using the Tornado Detection Algorithm • Important features to look for: • Position of the algorithm in relationship to the storm • Length of time that the TVS has been present • Distance from radar • Environmental winds

  17. References • Mitchell, E.D., 1998: The National Severe Storms Laboratory Tornado Detection Algorithm. Wea. Forecasting, 9,352-366. • The National Severe Storms Laboratory Tornado Detection Algorithm: Documentation. http://www.nssl.noaa.gov/wrd/swat/mitchell/nssl_tda.html • The NSSL Tornado Detection Algorithm (TDA), and Its Use for the 1996 Warning Decision Support System (WDSS) Proof-of-Concept (PoC) Tests. http://www.nssl.noaa.gov/wrd/swat/mitchell/tdawdss96user2.html

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