Analog-Based Severe Probability Guidance (171)
Alexander Elmore, Saint Louis University, St. Louis, MO
Kyle A. Perez, Saint Louis University
Chad M. Gravelle, NOAA/NWS Operations Proving Ground, CIMSS - University of Oklahoma
Charles E. Graves, Saint Louis University
Previous research has demonstrated the effectiveness of using historical analogs to aid in forecasting impactful weather in both the short- and long-term forecast period. For nearly the past decade, the Cooperative Institute for Precipitation Systems (CIPS) from Saint Louis University has been utilizing historical analogs to provide guidance for forecasters across the United States for a variety of atmospheric related hazards. However, this guidance is not optimized for any specific weather phenomena. Research is ongoing to investigate the usefulness and reliability of severe weather probabilistic guidance for the extended forecast (i.e., 3-8 days). For this new method, the Global Ensemble Forecast System (GEFS) reforecast data set is used to find similar environments to GEFS mean forecasts. Once the best matching analogs are found in the reforecast data set, a logistic regression model is fit using environmental fields from the GEFS (i.e., the predictors) and the severe weather reports (i.e., the predictand). The output of this model represents the probability of severe weather within 110 km of a grid point. Cases and statistical analysis from spring 2017 through present time, as well as ongoing updates and changes, will be presented.