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

Abstract

<p>Changes in the North Atlantic Oscillation (NAO) heavily influence the weather across the UK and the rest of Europe. Due to an imperfect reconstruction of the polar jet stream and associated pressure systems, there is reason to believe that errors in numerical weather prediction models may also depend on the prevailing behaviour of the NAO. To address this, information regarding the NAO is incorporated into statistical post-processing methods through a regime-dependent mixture model, which is then applied to wind speed forecasts from the Met Office's global ensemble prediction system, MOGREPS-G. The mixture model offers substantial improvements upon conventional post-processing methods when the wind speed depends strongly on the NAO, but the additional complexity of the model can hinder forecast performance in other instances. A measure of regime-dependency is thus defined that can be used to differentiate between situations when the numerical model output is, and is not, expected to benefit from regime-dependent post-processing. Implementing the regime-dependent mixture model only when this measure exceeds a certain threshold is found to further improve predictive performance, while also producing more accurate forecasts of extreme wind speeds.</p>

Characterizing renewable energy compound events across Europe using a logistic regression‐based approach

“…Ravestein et al (2018) showed the association between the NAO variability with periods of persistent low wind speeds over Europe, and thus a reduced wind power generation and colder temperatures. Recently, Allen et al (2021) exploit this association when statistically post‐processing wind speed forecasts. In Europe, mean wind generation shows strong weather regime dependent fluctuations: cyclonic regimes that can explain periods of overproduction and blocked regimes that can explain underproduction (Grams et al, 2017).…”

Evaluating forecasts for high-impact events using transformed kernel scores

“…Although analytical expressions of the twCRPS have been derived for particular families of parametric distributions (e.g. Allen et al, 2021), the integral in Equation 6 is often evaluated using numerical techniques. The following proposition provides an alternative representation of the twCRPS as a kernel score, implying a straightforward approach to computing this integral when F is an empirical distribution function.…”

Statistical and machine learning methods for postprocessing ensemble forecasts of wind gusts

“…A particular challenge for weather prediction is given by the need to better incorporate physical information and constraints into the forecasting models. Physical information about large-scale weather conditions, or weather regimes, forms a particularly relevant example in the context of postprocessing (Rodwell et al, 2018), with recent studies demonstrating benefits of regimedependent approaches (Allen et al, 2020(Allen et al, , 2021. For wind gusts in European winter storms, Pantillon et al (2018) found that a simple EMOS approach may substantially deteriorate forecast performance of the raw ensemble predictions during specific meteorological conditions.…”