Analysis of the Top Significant Fields in Analog-Based Severe Probability Guidance (23)
Kyle Perez, Saint Louis University, St. Louis, MO
Alexander Elmore, Saint Louis University
Chad Gravelle, NOAA/NWS Operations Proving Ground, CIMSS - University of Oklahoma, Norman, OK
Charles Graves, Saint Louis University
Previous research has demonstrated the effectiveness of using historical analogs to aidin forecasting impactful weather in both the short- and long-term forecast period. Fornearly the past decade, the Cooperative Institute for Precipitation Systems (CIPS) fromSaint Louis University has been utilizing historical analogs to provide guidance forforecasters. Today, current research is focusing on investigating the usefulness andreliability of severe weather probabilistic guidance for the extended forecast (i.e., 3-8days). For this new method, the Global Ensemble Forecast System (GEFS) reforecastdata set is used to find similar environments to GEFS mean forecasts. Once the bestmatching analogs are found in the reforecast data set, a logistic regression model is fitusing environmental fields from the GEFS (i.e., the predictors) and the severe weatherreports (i.e., the predictand). The output of this model represents the probability ofsevere weather within 110 km of a grid point when applied to the current forecast. Thispresentation will focus on the analysis of the top significant fields found by the logisticregression to discriminate between a severe from a non-severe environments usingvarious past severe weather events. The events used were based on collaborative workwith various National Weather Service Weather Forecasting Offices. The analysis of thetop fields will hopefully provide forecasters insight into the performance of theAnalog-Based Severe Probability Guidance, leading to an increase in the level ofconfidence in using the product in National Weather Service operations.