Project Instance

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{
    "ob_id": 45674,
    "uuid": "0f6b35f682b6410fb5d0bfcd7a5e5a64",
    "title": "Northwest European Seasonal Weather Prediction from Complex Systems Modelling (NERC grant: NE/V001787/1)",
    "abstract": "Weather conditions have significant socio-economic impacts and producing seasonal forecasts some months ahead would have significant benefits for society. Dynamical seasonal forecasting systems have led to some recent advances in forecasting skill, particularly in winter. However, there is considerable scope for applying machine-learning techniques to the problem. Using a novel Non-linear AutoRegressive Moving Average with eXogenous inputs (NARMAX) systems identification (an interpretable form of machine learning) approach, that identifies and models linear and non-linear dynamic relationships between a range of variables, this project seeks to extend skilful seasonal forecasting to seasons beyond winter, identify factors that contribute skill to the forecast, develop regional seasonal forecasts for Northwest Europe and assess the benefits of skilful probabilistic seasonal forecasts to potential users such as the agri-food industry.\r\n\r\nThese datasets relate to NERC grant: NE/V001787/1.",
    "keywords": "",
    "status": "",
    "publicationState": "",
    "identifier_set": [],
    "observationCollection": [],
    "parentProject": null,
    "subProject": [],
    "responsiblepartyinfo_set": [
        "https://catalogue.ceda.ac.uk/api/v2/rpis/218284/?format=api",
        "https://catalogue.ceda.ac.uk/api/v2/rpis/218285/?format=api",
        "https://catalogue.ceda.ac.uk/api/v2/rpis/218288/?format=api",
        "https://catalogue.ceda.ac.uk/api/v2/rpis/218286/?format=api",
        "https://catalogue.ceda.ac.uk/api/v2/rpis/218287/?format=api"
    ]
}