1194 Improvement of Wind Speed Prediction Using Statistical and Analog Techniques for NE U.S

Wednesday, 25 January 2017
4E (Washington State Convention Center )
Jaemo Yang, Univ. of Connecticut, Storrs, CT; and M. Astitha, L. D. Monache, and S. Alessandrini

The accuracy of weather prediction has become very important for the Northeast U.S. given the devastating effects of extreme weather events in the recent years. Weather forecasting systems are used towards building strategies to prevent catastrophic losses for human lives and the environment, and concurrently the systems has been built with advanced super-computing resources. Despite the technological advancements in the systems, many challenges, involved in improving forecasting skills as well as reducing uncertainties, remain regarding extreme weather predictability. This study examines the combination of Bayesian regression and analog ensemble techniques to improve the prediction of storms that have impacted NE U.S., mostly defined by the occurrence of high wind speeds (i.e. thunderstorms, winter storms, blizzards and hurricanes). The predicted wind speed, wind direction and temperature by two state-of-the-science atmospheric models (WRF and RAMS/ICLAMS) are combined using the mentioned techniques, exploring various ways that those variables influence the minimization of the prediction error (systematic and random). This study is focused on retrospective simulations of 146 storms that affected the NE U.S. in the period 2005-2016. In order to evaluate the techniques, leave-one-out cross validation procedure was implemented regarding 145 storms as the training dataset. In the Bayesian regression framework, optimal variances are estimated for the training partition by minimizing the root mean square error and are applied to the out-of-sample storm. The analog ensemble method selects a set of past observations that corresponded to the best analogs of the numerical weather prediction and provides a set of ensemble members of the selected observation dataset. The set of ensemble members can then be used in a deterministic or probabilistic approach. The preliminary results show improvement of the 10-m wind speed bias and error by 20-30% in all observation-model pairs for 146 storms. This presentation will illustrate how the long record of predicted extreme events is valuable and discuss the various combinations of atmospheric predictors and techniques in the improvement of wind speed prediction.
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