Recalibrating wind‐speed forecasts using regime‐dependent ensemble model output statistics

Abstract

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 Global Ensemble Forecasting System (GEFS) reforecast data, in the hope of alleviating errors owing to changes in the synoptic‐scale atmospheric state. When the wind speed depends strongly on the underlying weather regime, the resulting forecasts have the potential to provide substantial improvements in skill relative to conventional post‐processing techniques. This is particularly pertinent at longer lead times, where there is more improvement to be gained over 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.

Incorporating the North Atlantic Oscillation into the post‐processing of MOGREPS‐G wind speed forecasts

“…This work investigates how weather types can be used to calibrate ensembles of weather forecasts, focusing in particular on how such approaches can be applied in an operational setting. A mixture model approach to include regime information into statistical post-processing methods has previously been proposed (Allen et al, 2019(Allen et al, , 2020, though these studies have utilised large sets of data, which are not always readily available to forecasting centres. To circumvent this lack of data, this work combines a conventional ensemble model output statistics approach with a regime-dependent method, producing a prediction system that can adapt to issue the most relevant forecast given the current circumstances.…”

“…It is important to note that the weights in this case are functions of time, rather than parameters as such, highlighting that the weights will change depending on the prevailing behaviour of the atmosphere. Potential approaches to calculate the mixture model weights are discussed in Allen et al (2020), where it is found that, in comparison with alternative choices, the regimes predicted by high-resolution numerical weather models provide a reasonable estimate of the future synoptic-scale state. As well as recording the regime that manifests, the Met Office store the daily regime that is forecast by their global deterministic model, up to 6 days in advance.…”

“…It remains to identify circumstances in which regime-dependent post-processing should be beneficial. Allen et al (2020) consider the spread of the average wind speeds between regimes at a variety of locations, and show that improvements gained by including the regime information are correlated with this between-regime spread. That is, locations whose wind speeds are more heavily influenced by changes in the weather regime tend to benefit most from regime-dependent post-processing.…”

“…The stations have been divided into coastal, inland, and mountainous locations, from which it can be seen that, just as locations on the west coast of the UK and Ireland were earlier identified as being associated with higher average wind speeds, they are also particularly affected by the phase of the North Atlantic Oscillation. The measure of regime dependency introduced here is similar to that used in Allen et al (2020), but can better account for the different climatological frequencies of the regimes. Larger values suggest a stronger regime influence and hence advocate the use of regime-dependent post-processing, whereas if this quantity lies below the chosen threshold, then the truncated logistic distribution is issued as the forecast, and the regime information is ignored.…”

“…Previous work on the regime‐dependent post‐processing of ensemble forecasts has considered large, often simulated archives of data (Allen et al ., 2020). In practice, however, regular updates of the numerical model configuration or the data assimilation scheme are common, limiting the amount of data available on which to train post‐processing methods.…”