WARNING APPLICATIONS RESEARCH

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Aggregate hailstone approximately six inches in diameter. The Warning Applications group works on systems that help forecasters detect, monitor and predict the occurrence of severe weather.

 

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Tornado Detection Algorithm (TDA)

Three tornadic supercells, as observed by MDA.

Three tornadic supercell storms are presented. The storm on the left occurred near Mesa, AZ and is viewed from the Phoenix, AZ WSR-88D, the middle storm is viewed from the Pueblo CO WSR-88D, and the storm on the right is viewed from the Twin Lakes WSR-88D. Note how dramatic the size of each storm varies. Be aware that these storms are at the same zoom factor. This is a stunning example of how supercell storms may vary in size and which may raise serious implications concerning the identification of these types of storms especially their attendant mesocyclones and tornadoes using the WSR-88D. Larger image

Information on this page was last updated May, 2001.

GOAL: To identify the locally intense circulations associated with tornadoes using Doppler radar data, provide useful guidance to warning operations, and provide at least a 10 minute lead time to tornadoes.

PHILOSOPHY: Identify ALL circulations within Doppler radar data and distinguish between those which are tornadic and those which are non-tornadic.

The NSSL Tornado Detection Algorithm (TDA), and Its Use for the 1996 Warning Decision Support System (WDSS) Proof-of-Concept (PoC) Tests

Adaptable Parameter Documentation (last updated 5 June 1997)

How does the NSSL TDA perform compared to the WSR-88D TVS Algorithm?

The NSSL TDA was developed using a dependent dataset consisting of 23 tornadoes and validated using an independent dataset consisting of 31 tornadoes. The data set consisted of WSR-88D Level II data from KLWX (Sterling, VA), KNQA (Memphis, TN), KLSX (St. Louis, MO), KDDC (Dodge City, KS), KAMA (Amarillo, TX), and KOUN (Norman, OK), and KHGX (Houston, TX). The dependent data set was used to determine the optimal values of adaptable parameters which would yield the highest Critical Success Index (CSI). These parameters which were used to distinguish between tornadic and non-tornadic circulations are as follows:

* Maximum gate-to-gate velocity difference at the .5 degree elevation angle = 25 m/s
* Maximum gate-to-gate velocity difference = 36 m/s
* Circulation depth = 1.5 km

The WSR-88D TVS Algorithm (88D TVS) was also tested using both the dependent and independent datasets. A Default version of the 88D TVS Algorithm (Def. 88D TVS) used a TVS Shear threshold value of .02/s and a 5% areal search about a detected mesocyclone. An optimized version of the 88D TVS (Opt. 88D TVS) used a TVS Shear threshold value of .005/s (1/4 the default value), a 10% areal search about a detected mesocyclone, and a lower pattern vector threshold (6) in the WSR-88D Mesocyclone Detection Algorithm (88D MDA).

The performance scores presented here include the Probability of Detection (POD), False Alarm Rate (FAR), Critical Success Index (CSI), and Heidke Skill Score (HSS).

More information: An Analysis of MDA and TDA Data by Caren Marzban