{
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"@id": "https://doi.org/10.5281/zenodo.1194704",
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"value": "https://zenodo.org/record/1194705"
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"url": "https://zenodo.org/record/1194704",
"additionalType": "Project deliverable",
"name": "Description Of The Predictability Assessment Methodology",
"author": [
{
"name": "Albert Soret",
"givenName": "Albert",
"familyName": "Soret",
"affiliation": {
"@type": "Organization",
"name": "BSC"
},
"@type": "Person"
},
{
"name": "Veónica Torralba",
"givenName": "Veónica",
"familyName": "Torralba",
"affiliation": {
"@type": "Organization",
"name": "BSC"
},
"@type": "Person"
},
{
"name": "Nicola Cortesi",
"givenName": "Nicola",
"familyName": "Cortesi",
"affiliation": {
"@type": "Organization",
"name": "BSC"
},
"@type": "Person"
},
{
"name": "Sergio Lozano Galiana",
"givenName": "Sergio",
"familyName": "Lozano Galiana",
"affiliation": {
"@type": "Organization",
"name": "CENER"
},
"@type": "Person"
},
{
"name": "Javier Sanz Rodrigo",
"givenName": "Javier",
"familyName": "Sanz Rodrigo",
"affiliation": {
"@type": "Organization",
"name": "CENER"
},
"@type": "Person"
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],
"description": "This report describes the methodology that will be used to produce information about wind predictability in the European wind atlas. A review of the state-of-the-art in predictability assessment is reported at different scales, from day ahead (short-term) to subseasonal (medium-term predictability), seasonal and decadal (long-term predictability). The report provides an overview of the potential applications of wind predictability, the sources of predictability, the available prediction systems and the methods to evaluate them.
\nShort-term predictability was first studied in the frame of the European project SAFEWIND. A summary of that work is included here in order to show the continuity of this activity in the NEWA project and how it will be integrated with other scales of prediction. Predictability mapping was carried out using forecasts from numerical weather prediction and comparing them with reanalysis data. Downscaling to site level to produce wind power predictability information, based on information from the planning phase, was also done in order to illustrate how a wind farm developer could anticipate costs of lack of predictability during the operational phase.
\nMedium to long term predictability is studied with global climate models. Preliminary assessment of predictability is also done using reanalysis data as a reference of the real state of the atmosphere. This allows to map predictability skill over Europe. Preliminary results detected at least two windows of opportunity (high forecast skill) over Europe, one at sub-seasonal time scale, for a lead time of 12-18 days, and another at seasonal scale, for a lead time of one month. This skill is found to exist mainly over Central and Northern Europe and to a lesser degree in the Iberian Peninsula during the winter months of December-February.
\nHaving demonstrated that there is statistically significant wind speed skill during winter over some European areas, the end users could gain potential economic value using the forecasts instead of the climatology to base their decisions. Over these areas, novel techniques that employ this probabilistic information effectively to enhance the value of operational business decisions and quantitative risk management in the wind energy sector can be developed.
\nFurther work will try to demonstrate the potential use of predictability with site observations. This will help potential users answer questions like: what can they expect on the use of wind forecast at different time horizons? How predictable the wind resource is at the site of interest? This information will be synthetized in the form of comprehensive maps of predictability skills over Europe so it can be used for spatial planning of wind energy development.",
"license": [
"https://creativecommons.org/licenses/by/4.0",
"info:eu-repo/semantics/openAccess"
],
"keywords": "wind energy, wind resource assessment, predictability, probabilistic, forecasting, seasonal, decadal, sub-seasonal",
"inLanguage": "en",
"datePublished": "2016-03-29",
"schemaVersion": "http://datacite.org/schema/kernel-4",
"publisher": {
"@type": "Organization",
"name": "Zenodo"
},
"funder": {
"@id": "https://doi.org/10.13039/501100000780",
"@type": "Organization",
"name": "European Commission"
},
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"@type": "Organization",
"name": "datacite"
}
}