Surface wind speed prediction in the Canadian Arctic using non-linear machine learning methods

• Published in: Atmosphere-Ocean
• Other Authors: Zeng, Zhen (author), Hsieh, William W. (author), Burrows, William R. (author), Giles, Andrew (author), Shabbar, Amir (author)
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2011
• Subjects: Arctic; Paulatuk
• Online Access: https://doi.org/10.1080/07055900.2010.549102

Two non-linear, machine-learning/statistical methods, i.e., Bayesian neural network (BNN) and support vector regression (SVR), plus multiple linear regression (MLR), were used to forecast surface wind speeds at lead times of 12, 24, 48 and 72h. Three different schemes, a statistical downscaling model (Scheme 1) using daily reforecast data from the National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS), an autoregressive model (Scheme 2) based on past wind observations, and a full model (Scheme 3) combining the two, were investigated in this study for the October-March winds from two meteorological stations in the Canadian Arctic (Clyde River and Paulatuk). At very short lead times, Scheme 2 provides better wind speed prediction than Scheme 1, but its forecast scores decrease rapidly with lead time. Scheme 3 generally performs best, especially at shorter lead times. All the linear and non-linear downscaling methods have significantly higher forecast scores at the two stations than the GFS reforecast. The non-linear methods tended to have slightly better forecast scores than linear methods (MLR and the linear version of SVR). There is particular interest in high-wind events, defined as having wind speeds over 22 knots (11.3m s-1). After rescaling, the continuous wind predictions from Scheme 3 were classified into two types high-wind event or non-event. For high-wind event forecasting, the non-linear methods have marginally better binary forecast scores than the linear methods for Clyde River but not for Paulatuk. The alternative approach of using support vector classification (SVC) did not perform better, but weighting the high-wind events more heavily than the non-events during model training improved the binary forecast scores.