S137 Comparison of GFS and NAM Weather Numerical Prediction Models in Predicting Tornado Outbreaks

Sunday, 6 January 2019
Hall 4 (Phoenix Convention Center - West and North Buildings)
Michelle Rose Spencer, Metropolitan State University of Denver, Denver, CO

Severe weather and tornado outbreaks affect millions of people across America every year. The Global Forecasting System (GFS) and North American Mesoscale Forecast System (NAM) are both commonly used to predict severe weather outbreaks. The purpose of this research is to compare the two numerical weather prediction models with each other regarding their ability to predict tornado outbreaks. Comparing the two model results may allow meteorologists to better predict severe weather and tornado outbreaks, depending on different parameters. Currently there is no set definition for a tornado outbreak, so for this study a tornado outbreak will be defined as a day where at least eighteen tornadoes were observed/confirmed and at least four of those were rated EF2 or higher. Ten tornado outbreaks are chosen from May 2015 through April 2018 during various times of the year to ensure a wide range of synoptic scale and temporal scenarios. GEMPAK data is gathered for the twenty-four hours before the tornado outbreaks for each model. IDV is then used to interpret the data. A significant tornado parameter (STP) formula, provided by the College of DuPage, is input into IDV to plot the STP from sounding data within the GEMPAK files. The choice of sounding data used for the STP is determined based on where the tornado outbreaks occurred. These plots are then compared to the observed tornado data provided by the severe prediction center. A verification method is then used to judge the skill of the different forecasting models to determine which model performed better. Further analysis can then be made about which model does better at predicting tornado outbreaks during different temporal scenarios and synoptic scale scenarios.
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