MURAL - Maynooth University Research Archive Library

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

    Duesterhus, André (2020) Seasonal statistical–dynamical prediction of the North Atlantic Oscillation by probabilistic post-processing and its evaluation. Nonlinear Processes in Geophysics, 27. pp. 121-131. ISSN 1023-5809

    Download (6MB) | Preview

    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...

    Add this article to your Mendeley library


    Dynamical models of various centres have shownin recent years seasonal prediction skill of the North AtlanticOscillation (NAO). By filtering the ensemble members onthe basis of statistical predictors, known as subsampling, it ispossible to achieve even higher prediction skill. In this studythe aim is to design a generalisation of the subsampling ap-proach and establish it as a post-processing procedure.Instead of selecting discrete ensemble members for eachyear, as the subsampling approach does, the distributions ofensembles and statistical predictors are combined to createa probabilistic prediction of the winter NAO. By compar-ing the combined statistical–dynamical prediction with thepredictions of its single components, it can be shown that itachieves similar results to the statistical prediction. At thesame time it can be shown that, unlike the statistical predic-tion, the combined prediction has fewer years where it per-forms worse than the dynamical prediction.By applying the gained distributions to other meteorolog-ical variables, like geopotential height, precipitation and sur-face temperature, it can be shown that evaluating predic-tion skill depends highly on the chosen metric. Besides thecommon anomaly correlation (ACC) this study also presentsscores based on the Earth mover’s distance (EMD) and theintegrated quadratic distance (IQD), which are designed toevaluate skills of probabilistic predictions. It shows that byevaluating the predictions for each year separately comparedto applying a metric to all years at the same time, likecorrelation-based metrics, leads to different interpretations ofthe analysis.

    Item Type: Article
    Keywords: Seasonal statistical–dynamical prediction; North Atlantic Oscillation; probabilistic post-processing;
    Academic Unit: Faculty of Social Sciences > Geography
    Faculty of Social Sciences > Research Institutes > Irish Climate Analysis and Research Units, ICARUS
    Item ID: 13959
    Identification Number:
    Depositing User: André Düsterhus
    Date Deposited: 09 Feb 2021 13:53
    Journal or Publication Title: Nonlinear Processes in Geophysics
    Publisher: American Geophysical Union
    Refereed: No
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

    Repository Staff Only(login required)

    View Item Item control page


    Downloads per month over past year

    Origin of downloads