96 Further Analysis and Verification of Storm Prediction Center 1300 UTC Tornado Outlooks

Wednesday, 9 November 2016
Broadway Rooms (Hilton Portland )
Alex Erwin, University of Illinois, Urbana, IL; and J. W. Frame and G. Marion

Handout (436.7 kB)

Storm Prediction Center (SPC) Convective Outlooks provide severe weather researchers, storm observers, spotters, and the public with reliable severe weather forecasts on a daily basis. These Outlooks include geographical areas likely to be impacted by tornadoes, large hail, and damaging winds, as well as a general categorical severe weather risk. All 1300 UTC Outlooks that included a forecast of a 10% or greater chance of tornadoes within 25 miles of a point, issued between 3 March 2006 and 3 March 2016, were selected for further examination in this study. An Outlook was deemed a successful forecast if the percentage of the area forecast to be at the highest risk of tornadoes was greater than or approximately equal to the highest tornado probability in the Outlook. If this was not the case, then the Outlook did not verify. The use of a quantitative threshold in this work builds upon previous work in which the verification was accomplished only qualitatively.

Outlooks that did not verify were further examined via radar and satellite imagery, surface observations, and upper-air data to determine why. Statistics on various modes of failure, including lack of convective initiation, development of only subsevere convection, and issues related to convective mode will be presented. Additional analyses of common variables used in severe weather and tornado forecasting, such as shear vector orientation relative to initiating boundaries, storm-relative helicity, and lifting condensation level height, will be performed on both successful and unsuccessful outlooks. Preliminary analyses indicate that cold fronts present the greatest forecast challenges along with the prediction of tornadoes within quasi-linear convective systems. Statistics will be compiled and data from successful forecasts will be contrasted with data from unsuccessful forecasts in hopes of guiding additional research that can be used to improve future forecasts.

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