Project Instance

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{
    "ob_id": 33450,
    "uuid": "bcac810c563d45a898330179487b0947",
    "title": "Numerical weather prediction based event attribution (an ECMWF special project)",
    "abstract": "The aim of this project, which began in 2021, was to explore how operational weather forecast models could be used to examine the human influence in individual extreme weather events. There are several benefits that this approach might provide compared to conventional climate model based attribution:\r\n\r\n- The use of a forecast model that was demonstrably able to predict particular weather event in question means providing confidence that the model is able to simulate the key processes involved in the development of the event.\r\n- Similarly, the use of a forecast model ensures unequivocally that the event in question is being analysed, rather than a mixture of similar events that share a particular characteristic.\r\n- Some recent extreme weather events are so exceptional that there is little precedent in either the observational record or climate model simulations. Using forecast models that were able to predict such events provides a meaningful approach for studying these events.",
    "keywords": "",
    "status": "ongoing",
    "publicationState": "preview",
    "identifier_set": [],
    "observationCollection": [],
    "parentProject": null,
    "subProject": [],
    "responsiblepartyinfo_set": [
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/149402/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/149400/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/149399/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/149403/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/149401/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/149404/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/149405/?format=api"
    ]
}