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

2021 ◽  
Author(s):  
Sam Allen ◽  
Gavin Evans ◽  
Piers Buchanan ◽  
Frank Kwasniok
Keyword(s):  
Wind Speed ◽  
North Atlantic Oscillation ◽  
Mixture Model ◽  
North Atlantic ◽  
Ensemble Prediction ◽  
Post Processing ◽  
Processing Methods ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

<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>

scholarly journals Seasonal statistical–dynamical prediction of the North Atlantic Oscillation by probabilistic post-processing and its evaluation

2020 ◽  
Vol 27 (1) ◽  
pp. 121-131 ◽  
Author(s):  
André Düsterhus
Keyword(s):  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
Statistical Prediction ◽  
Post Processing ◽  
Probabilistic Prediction ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

Abstract. Dynamical models of various centres have shown in recent years seasonal prediction skill of the North Atlantic Oscillation (NAO). By filtering the ensemble members on the basis of statistical predictors, known as subsampling, it is possible to achieve even higher prediction skill. In this study the aim is to design a generalisation of the subsampling approach and establish it as a post-processing procedure. Instead of selecting discrete ensemble members for each year, as the subsampling approach does, the distributions of ensembles and statistical predictors are combined to create a probabilistic prediction of the winter NAO. By comparing the combined statistical–dynamical prediction with the predictions of its single components, it can be shown that it achieves similar results to the statistical prediction. At the same time it can be shown that, unlike the statistical prediction, the combined prediction has fewer years where it performs worse than the dynamical prediction. By applying the gained distributions to other meteorological variables, like geopotential height, precipitation and surface temperature, it can be shown that evaluating prediction skill depends highly on the chosen metric. Besides the common anomaly correlation (ACC) this study also presents scores based on the Earth mover's distance (EMD) and the integrated quadratic distance (IQD), which are designed to evaluate skills of probabilistic predictions. It shows that by evaluating the predictions for each year separately compared to applying a metric to all years at the same time, like correlation-based metrics, leads to different interpretations of the analysis.

scholarly journals Seasonal statistical-dynamical prediction of the North Atlantic Oscillation by probabilistic post-processing

2019 ◽  
Author(s):  
André Düsterhus
Keyword(s):  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
Statistical Prediction ◽  
Post Processing ◽  
Probabilistic Prediction ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

Abstract. Dynamical models of various centres have shown in recent years seasonal prediction skill of the North Atlantic Oscillation (NAO). By filtering the ensemble members on the basis of statistical predictors, known as subsampling, it is possible to achieve even higher prediction skill. In this study the aim is to design a generalisation of the subsampling approach and establish it as a post-processing procedure. Instead of selecting discrete ensemble members for each year, as the subsampling approach does, the distributions of ensembles and statistical predictors are combined to create a probabilistic prediction of the winter NAO. By comparing the combined statistical-dynamical prediction with the predictions of its single components, it can be shown that it achieves similar results to the statistical prediction. At the same time it can be shown, that unlike the statistical prediction the combined prediction has less years where it performs worse than the dynamical prediction. By applying the gained distributions to other meteorological variables, like geopotential height, precipitation and surface temperature it can be shown that evaluating prediction skill depends highly on the chosen metric. Besides the common anomaly correlation (ACC) this study also presents a score basing on the Earth Mover’s Distance (EMD), which is designed to evaluate skill of probabilistic predictions. It shows that by evaluating the predictions separately compared to applying a metric on all years at the same time, like correlation based metrics, leads to different interpretations of the analysis.

scholarly journals Influence of the North Atlantic oscillation on the atmospheric levels of benzo[a]pyrene over Europe

2021 ◽  
Author(s):  
Pedro Jiménez-Guerrero ◽  
Nuno Ratola
Keyword(s):  
Spatial Pattern ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Transport Model ◽  
Regional Scale ◽  
The South ◽  
Climatic Fluctuations ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

AbstractThe atmospheric concentration of persistent organic pollutants (and of polycyclic aromatic hydrocarbons, PAHs, in particular) is closely related to climate change and climatic fluctuations, which are likely to influence contaminant’s transport pathways and transfer processes. Predicting how climate variability alters PAHs concentrations in the atmosphere still poses an exceptional challenge. In this sense, the main objective of this contribution is to assess the relationship between the North Atlantic Oscillation (NAO) index and the mean concentration of benzo[a]pyrene (BaP, the most studied PAH congener) in a domain covering Europe, with an emphasis on the effect of regional-scale processes. A numerical simulation for a present climate period of 30 years was performed using a regional chemistry transport model with a 25 km spatial resolution (horizontal), higher than those commonly applied. The results show an important seasonal behaviour, with a remarkable spatial pattern of difference between the north and the south of the domain. In winter, higher BaP ground levels are found during the NAO+ phase for the Mediterranean basin, while the spatial pattern of this feature (higher BaP levels during NAO+ phases) moves northwards in summer. These results show deviations up to and sometimes over 100% in the BaP mean concentrations, but statistically significant signals (p<0.1) of lower changes (20–40% variations in the signal) are found for the north of the domain in winter and for the south in summer.

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