Sunday, 22 January 2017
4E (Washington State Convention Center )
The use of numerical weather prediction (NWP) has brought significant improvements to the forecasting of severe weather outbreaks; however, determination of severe weather outbreak mode (in particular tornadic and nontornadic outbreaks) continues to be a challenge. This research examines the effect of microphysics and planetary boundary layer (PBL) physics schemes on tornado outbreak forecast accuracy. WRF-ARW simulations of forty United States tornadic and nontornadic forecasts are generated. Each case is modeled with 15 different combinations of physics parameterizations: 5 microphysics and 3 planetary boundary layer (PBL), all of which were designed to perform well in convective weather situations. A learning machine technique known as a support vector machine (SVM) is used to predict outbreak mode for each run. Parameters determined from past research to be significant for outbreak discrimination are extracted from the WRF-ARW simulations and input to the SVM, which issues a diagnosis of outbreak type (tornadic or nontornadic) for each model run. The results indicate that the combination of WRF double-moment 6-class microphysics and Yonsei University PBL physics generates the highest skill – 0.658 – of all 15 combinations studied, significant at the 95% confidence level. This combination produced the highest probability of detection and false alarm ratio as well, though within the margins of error of other physics combinations. In general, PBL physics schemes proved to be more significant than microphysics in influencing the modeling of the important severe weather covariates.
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