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    Verification and bias correction of ECMWF forecasts for Irish weather stationsto evaluate their potential usefulness in grass growth modelling


    McDonnell, Jack and Lambkin, Keith and Fealy, Rowan and Hennessy, Deirdre and Shalloo, Laurence and Brophy, Caroline (2017) Verification and bias correction of ECMWF forecasts for Irish weather stationsto evaluate their potential usefulness in grass growth modelling. Meteorological Applications, 25. pp. 292-301.

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    Official URL: https://rmets.onlinelibrary.wiley.com/doi/epdf/10....


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    Abstract

    Typical weather in Ireland provides conditions favourable for sustaining grass growth throughout most ofthe year. This affords grass based farming a significant economic advantage due to the low input costs associated withgrass production. To optimize the productivity of grass based systems, farmers must manage the resource over short timescales. While research has been conducted into developing predictive grass growth models for Ireland to support on-farmdecision making, short term weather forecasts have not yet been incorporated into these models. To assess their potentialfor use in predictive grass growth models, deterministic forecasts from the European Centre for Medium-Range WeatherForecasts (ECMWF) were verified for lead times up to 10 days using observations from 25 Irish weather stations. Forecastsof air temperature variables were generally precise at all lead times, particularly up to 7 days. Verification of ECMWF soiltemperature forecasts is limited, but here they were shown to be accurate at all depths and most precise at greater depths suchas 50 cm. Rainfall forecasts performed well up to approximately 5 days. Seven bias correction techniques were assessed tominimize systematic biases in the forecasts. Based on the root mean squared error values, no large improvement was identifiedfor rainfall forecasts on equivalent ECMWF forecasts, but the optimum bias corrections improved air and soil temperatureforecasts greatly. Overall, the results demonstrated that forecasts predict observations accurately up to approximately a weekin advance and therefore could prove valuable in grass growth prediction at farm level in Ireland

    Item Type: Article
    Keywords: forecast verification; bias correction; Ireland; air temperature; rainfall; soil temperature; grass growth; agriculture;
    Academic Unit: Faculty of Social Sciences > Research Institutes > Irish Climate Analysis and Research Units, ICARUS
    Item ID: 10961
    Identification Number: https://doi.org/10.1002/met.1691
    Depositing User: Rowan Fealy
    Date Deposited: 24 Jul 2019 15:38
    Journal or Publication Title: Meteorological Applications
    Publisher: Wiley
    Refereed: Yes
    URI:

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