Objective Classification of Tornadic and Nontornadic Severe Weather Outbreaks
- 1 December 2009
- journal article
- Published by American Meteorological Society in Monthly Weather Review
- Vol. 137 (12), 4355-4368
- https://doi.org/10.1175/2009mwr2897.1
Abstract
Tornadoes often strike as isolated events, but many occur as part of a major outbreak of tornadoes. Nontornadic outbreaks of severe convective storms are more common across the United States but pose different threats than do those associated with a tornado outbreak. The main goal of this work is to distinguish between significant instances of these outbreak types objectively by using statistical modeling techniques on numerical weather prediction output initialized with synoptic-scale data. The synoptic-scale structure contains information that can be utilized to discriminate between the two types of severe weather outbreaks through statistical methods. The Weather Research and Forecast model (WRF) is initialized with synoptic-scale input data (the NCEP–NCAR reanalysis dataset) on a set of 50 significant tornado outbreaks and 50 nontornadic severe weather outbreaks. Output from the WRF at 18-km grid spacing is used in the objective classification. Individual severe weather parameters forecast by the model near the time of the outbreak are analyzed from simulations initialized at 24, 48, and 72 h prior to the outbreak. An initial candidate set of 15 variables expected to be related to severe storms is reduced to a set of 6 or 7, depending on lead time, that possess the greatest classification capability through permutation testing. These variables serve as inputs into two statistical methods, support vector machines and logistic regression, to classify outbreak type. Each technique is assessed based on bootstrap confidence limits of contingency statistics. An additional backward selection of the reduced variable set is conducted to determine which variable combination provides the optimal contingency statistics. Results for the contingency statistics regarding the verification of discrimination capability are best at 24 h; at 48 h, modest degradation is present. By 72 h, the contingency statistics decline by up to 15%. Overall, results are encouraging, with probability of detection values often exceeding 0.8 and Heidke skill scores in excess of 0.7 at 24-h lead time.Keywords
This publication has 41 references indexed in Scilit:
- Evaluation of WRF Forecasts of Tornadic and Nontornadic Outbreaks When Initialized with Synoptic-Scale InputMonthly Weather Review, 2009
- Statistical Modeling of Downslope Windstorms in Boulder, ColoradoWeather and Forecasting, 2008
- A Simple and Flexible Method for Ranking Severe Weather EventsWeather and Forecasting, 2006
- A generalized approach to parameterizing convection combining ensemble and data assimilation techniquesGeophysical Research Letters, 2002
- Use of Regression Techniques to Predict Hail Size and the Probability of Large HailWeather and Forecasting, 1997
- The NCEP/NCAR 40-Year Reanalysis ProjectBulletin of the American Meteorological Society, 1996
- On Summary Measures of Skill in Rare Event Forecasting Based on Contingency TablesWeather and Forecasting, 1990
- Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional ModelJournal of the Atmospheric Sciences, 1989
- The Structure and Classification of Numerically Simulated Convective Stormsin Directionally Varying Wind ShearsMonthly Weather Review, 1984
- Statistical Relations between Summer Thunderstorm Patterns and Continental Midtropospheric HeightsMonthly Weather Review, 1984