MURAL - Maynooth University Research Archive Library

    Dynamical–statistical seasonal forecasts of winter and summer precipitation for the Island of Ireland

    Golian, Saeed and Murphy, Conor and Wilby, Robert L. and Matthews, Tom and Donegan, Seán and Quinn, Dáire Foran and Harrigan, Shaun (2022) Dynamical–statistical seasonal forecasts of winter and summer precipitation for the Island of Ireland. International Journal of Climatology, 42 (11). pp. 5714-5731. ISSN 1097-0088

    Download (9MB) | Preview

    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...

    Add this article to your Mendeley library


    Seasonal precipitation forecasting is highly challenging for the northwest fringes of Europe due to complex dynamical drivers. Hybrid dynamical–statistical approaches offer potential to improve forecast skill. Here, hindcasts of mean sea level pressure (MSLP) from two dynamical systems (GloSea5 andSEAS5) are used to derive two distinct sets of indices for forecasting winter(DJF) and summer (JJA) precipitation over lead-times of 1–4 months. These indices provide predictors of seasonal precipitation via a multiple linear regression model (MLR) and an artificial neural network (ANN) applied to four Irish rainfall regions and the Island of Ireland. Forecast skill for each model, lead time, and region was evaluated using the correlation coefficient (r) and mean absolute error (MAE), benchmarked against (a) climatology, (b) bias corrected precipitation hindcasts from both GloSea5 and SEAS5, and (c) a zero-order forecast based on rainfall persistence. The MLR and ANN models produced skilful precipitation forecasts with leads of up to 4 months. In all tests, our hybrid method based on MSLP indices outperformed the three benchmarks(i.e., climatology, bias corrected, and persistence). With correlation coefficients ranging between 0.38 and 0.81 in winter, and between 0.24 and 0.78 in summer, the ANN model outperformed MLR in both seasons in most regions and lead-times. Forecast skill for summer was comparable to that in winter and for some regions/lead times even superior. Our results also show that climatology and persistence performed better than direct use of bias corrected dynamical outputs in most regions and lead-times in terms of MAE. We conclude that the hybrid dynamical–statistical approach developed here—by leveraging useful information about MSLP from dynamical systems—enables more skilful seasonal precipitation forecasts for Ireland, and possibly other locations in west-ern Europe, in both winter and summer.

    Item Type: Article
    Keywords: artificial neural network; dynamical models; mean sea level pressure; precipitation; regression; seasonal forecasting;
    Academic Unit: Faculty of Social Sciences > Geography
    Faculty of Social Sciences > Research Institutes > Irish Climate Analysis and Research Units, ICARUS
    Item ID: 17485
    Identification Number:
    Depositing User: Conor Murphy
    Date Deposited: 04 Sep 2023 15:16
    Journal or Publication Title: International Journal of Climatology
    Publisher: Royal Meteorological Society
    Refereed: Yes
    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