Advanced wind speed prediction using convective weather variables through machine learning application

Abstract

High precision and reliable wind speed forecasting is a challenge for meteorologists. We used multiple nonparametric tree-based machine learning techniques, for predicting the maximum wind speed at 10 m using selected convective weather variables. Analysis is based on 127 convective storms from 2005 to 2013. The study evaluated two error models - the Bayesian Additive Regression Trees (BART) and the Quantile Regression Forests (QRF) - and compares them in terms of point estimates and prediction intervals. The error model performances were evaluated based on different error metrics evaluating both the bias and random error of point estimates and the prediction intervals using ensemble verification statistics. The study showed that error modeling based on QRF is superior to BART, especially in terms of point estimate and prediction interval results. Wind speed prediction through QRF was successfully verified using systematic and random error metrics, and ensemble verification statistics of the corresponding prediction intervals. The model generated realizations of wind speed that successfully encapsulated the reference wind speed and notably reduced systematic and random error. The predicted wind speed from QRF can potentially support emergency preparedness efforts associated with severe weather impacts.