Using a Bayesian Regression Approach on Dual-Model Windstorm Simulations to Improve Wind Speed Prediction

Weather prediction accuracy is very important given the devastating effects of extreme-weather events in recent years. Numerical weather prediction systems are used to build strategies to prevent catastrophic losses of human lives and the environment and have evolved with the use of multimodel or single-model ensembles and data-assimilation techniques in an attempt to improve the forecast skill. These techniques require increased computational power (thousands of CPUs) because of the number of model simulations and ingestion of observational data from a wide variety of sources. In this study, the combination of predictions from two state-of-the-science atmospheric models [WRF and RAMS/Integrated Community Limited Area Modeling System (ICLAMS)] using Bayesian and simple linear regression techniques is examined, and wind speed prediction for the northeastern United States is improved using regression techniques. Retrospective simulations of 17 storms that affected the northeastern United States during the period 2004–13 are performed and utilized. Optimal variances are estimated for the 13 training storms by minimizing the root-mean-square error and are applied to four out-of-sample storms [Hurricane Irene (2011), Hurricane Sandy (2012), a November 2012 winter storm, and a February 2013 blizzard]. The results show a 20%–30% improvement in the systematic and random error of 10-m wind speed over all stations and storms, using various storm combinations for the training dataset. This study indicates that 10–13 storms in the training dataset are sufficient to reduce the errors in the prediction, and a selection that is based on occurrence (chronological sequence) is also considered to be efficient.