Raw output from deterministic numerical weather prediction models is typically subject
to systematic biases. Although ensemble forecasts provide invaluable information
regarding the uncertainty in a prediction, they themselves often misrepresent the
weather that occurs. Given their widespread use, the need for high-quality wind
speed ...
Raw output from deterministic numerical weather prediction models is typically subject
to systematic biases. Although ensemble forecasts provide invaluable information
regarding the uncertainty in a prediction, they themselves often misrepresent the
weather that occurs. Given their widespread use, the need for high-quality wind
speed forecasts is well-documented. Several statistical approaches have therefore been
proposed to recalibrate ensembles of wind speed forecasts, including a heteroscedastic
truncated regression approach. An extension to this method that utilises the prevailing
atmospheric flow is implemented here in a quasigeostrophic simulation study and
on GEFS reforecast data, in the hope of alleviating errors owing to changes in
the synoptic-scale atmospheric state. When the wind speed strongly depends on the
underlying weather regime, the resulting forecasts have the potential to provide
substantial improvements in skill upon conventional post-processing techniques. This
is particularly pertinent at longer lead times, where there is more improvement to be
gained upon current methods, and in weather regimes associated with wind speeds that
differ greatly from climatology. In order to realise this potential, an accurate prediction
of the future atmospheric regime is required.
