Improved very short-term spatio-temporal wind forecasting using atmospheric regimes

We present a regime-switching vector autoregressive method for very short-term wind speed forecasting at multiple locations with regimes based on large-scale meteorological phenomena. Statistical methods for wind speed forecasting based on recent observations outperform numerical weather prediction for forecast horizons up to a few hours, and the spatio-temporal interdependency between geographically dispersed locations may be exploited to improve forecast skill. Here, we show that conditioning spatio-temporal interdependency on atmospheric modes derived from gridded numerical weather data can further improve forecast performance. Atmospheric modes are based on the clustering of surface wind and sea-level pressure fields, and the geopotential height field at the 5000-hPa level. The data fields are extracted from the MERRA-2 reanalysis dataset with an hourly temporal resolution over the UK; atmospheric patterns are clustered using self-organising maps and then grouped further to optimise forecast performance. In a case study based on 6years of measurements from 23 weather stations in the UK, a set of 3 atmospheric modes are found to be optimal for forecast performance. The skill of 1- to 6-hour-ahead forecasts is improved at all sites compared with persistence and competitive benchmarks. Across the 23 test sites, 1-hour-ahead root mean squared error is reduced by between 0.3% and 4.1% compared with the best performing benchmark and by an average of 1.6% over all sites; the 6-hour-ahead accuracy is improved by an average of 3.1%.