ECMWF global coupled atmosphere, ocean and sea-ice dataset for the Year of Polar Prediction 2017–2020
The Year Of Polar Prediction (YOPP) dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF) contains initial condition and forecast model output from the operational global, coupled numerical weather prediction system. The dataset has been created to support model forecast evaluati...
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ftpubmed:oai:pubmedcentral.nih.gov:7710753 2023-05-15T18:17:46+02:00 ECMWF global coupled atmosphere, ocean and sea-ice dataset for the Year of Polar Prediction 2017–2020 Bauer, Peter Sandu, Irina Magnusson, Linus Mladek, Richard Fuentes, Manuel 2020-12-02 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7710753/ https://doi.org/10.1038/s41597-020-00765-y en eng Nature Publishing Group UK http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7710753/ http://dx.doi.org/10.1038/s41597-020-00765-y © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. CC0 PDM CC-BY Sci Data Data Descriptor Text 2020 ftpubmed https://doi.org/10.1038/s41597-020-00765-y 2020-12-06T02:14:16Z The Year Of Polar Prediction (YOPP) dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF) contains initial condition and forecast model output from the operational global, coupled numerical weather prediction system. The dataset has been created to support model forecast evaluation, predictability studies and model error analyses over polar areas, which are strongly affected by climate change with yet unknown feedbacks on global circulation. The dataset complements YOPP observation and modeling research activities that represent a key deliverable of the World Meteorological Organization’s Polar Prediction Program. The dataset covers the period from mid-2017 until the end of the MOSAiC field campaign, expected for autumn 2020. Initial conditions and forecasts up to day-15 are included for the atmosphere and land surface for the entire period, and for ocean and sea-ice model components after June 2019. In addition, tendencies from model dynamics and individual physical processes are included for the first two forecast days. These are essential for characterizing the contribution of individual processes to model state evolution and, hence, for diagnosing sources of model error. Text Sea ice PubMed Central (PMC) Scientific Data 7 1 |
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Data Descriptor Bauer, Peter Sandu, Irina Magnusson, Linus Mladek, Richard Fuentes, Manuel ECMWF global coupled atmosphere, ocean and sea-ice dataset for the Year of Polar Prediction 2017–2020 |
topic_facet |
Data Descriptor |
description |
The Year Of Polar Prediction (YOPP) dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF) contains initial condition and forecast model output from the operational global, coupled numerical weather prediction system. The dataset has been created to support model forecast evaluation, predictability studies and model error analyses over polar areas, which are strongly affected by climate change with yet unknown feedbacks on global circulation. The dataset complements YOPP observation and modeling research activities that represent a key deliverable of the World Meteorological Organization’s Polar Prediction Program. The dataset covers the period from mid-2017 until the end of the MOSAiC field campaign, expected for autumn 2020. Initial conditions and forecasts up to day-15 are included for the atmosphere and land surface for the entire period, and for ocean and sea-ice model components after June 2019. In addition, tendencies from model dynamics and individual physical processes are included for the first two forecast days. These are essential for characterizing the contribution of individual processes to model state evolution and, hence, for diagnosing sources of model error. |
format |
Text |
author |
Bauer, Peter Sandu, Irina Magnusson, Linus Mladek, Richard Fuentes, Manuel |
author_facet |
Bauer, Peter Sandu, Irina Magnusson, Linus Mladek, Richard Fuentes, Manuel |
author_sort |
Bauer, Peter |
title |
ECMWF global coupled atmosphere, ocean and sea-ice dataset for the Year of Polar Prediction 2017–2020 |
title_short |
ECMWF global coupled atmosphere, ocean and sea-ice dataset for the Year of Polar Prediction 2017–2020 |
title_full |
ECMWF global coupled atmosphere, ocean and sea-ice dataset for the Year of Polar Prediction 2017–2020 |
title_fullStr |
ECMWF global coupled atmosphere, ocean and sea-ice dataset for the Year of Polar Prediction 2017–2020 |
title_full_unstemmed |
ECMWF global coupled atmosphere, ocean and sea-ice dataset for the Year of Polar Prediction 2017–2020 |
title_sort |
ecmwf global coupled atmosphere, ocean and sea-ice dataset for the year of polar prediction 2017–2020 |
publisher |
Nature Publishing Group UK |
publishDate |
2020 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7710753/ https://doi.org/10.1038/s41597-020-00765-y |
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Sea ice |
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Sea ice |
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Sci Data |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7710753/ http://dx.doi.org/10.1038/s41597-020-00765-y |
op_rights |
© The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. |
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CC0 PDM CC-BY |
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https://doi.org/10.1038/s41597-020-00765-y |
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Scientific Data |
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