  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 short-term wind
forecasting out-perform 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' can
further improve forecast performance. The modes are defined from the
atmospheric classification of wind and pressure fields at the surface level,
and the geopotential height field at the 500hPa level. The data fields are
extracted from the MERRA-2 reanalysis dataset with an hourly temporal
resolution over the UK, atmospheric patterns are classified using
self-organising maps and then clustered to optimise forecast performance. In a
case study based on 6 years of measurements from 23 weather stations in the UK,
a set of three atmospheric modes are found to be optimal for forecast
performance. The skill in the one- to six-hour-ahead forecasts is improved at
all sites compared to persistence and competitive benchmarks. Across the 23
test sites, one-hour-ahead root mean squared error is reduced by between 0.3%
and 4.1% compared to the best performing benchmark, and by and average of 1.6%
over all sites; the six-hour-ahead accuracy is improved by an average of 3.1%.
