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A long-term, temporally consistent, gridded daily meteorological dataset for northwestern North America
We describe a spatially contiguous, temporally consistent high-resolution gridded daily meteorological dataset for northwestern North America. This >4 million km(2) region has high topographic relief, seasonal snowpack, permafrost and glaciers, crosses multiple jurisdictional boundaries and conta...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6335611/ https://www.ncbi.nlm.nih.gov/pubmed/30644851 http://dx.doi.org/10.1038/sdata.2018.299 |
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author | Werner, A. T. Schnorbus, M. A. Shrestha, R. R. Cannon, A. J. Zwiers, F. W. Dayon, G. Anslow, F. |
author_facet | Werner, A. T. Schnorbus, M. A. Shrestha, R. R. Cannon, A. J. Zwiers, F. W. Dayon, G. Anslow, F. |
author_sort | Werner, A. T. |
collection | PubMed |
description | We describe a spatially contiguous, temporally consistent high-resolution gridded daily meteorological dataset for northwestern North America. This >4 million km(2) region has high topographic relief, seasonal snowpack, permafrost and glaciers, crosses multiple jurisdictional boundaries and contains the entire Yukon, Mackenzie, Saskatchewan, Fraser and Columbia drainages. We interpolate daily station data to 1/16° spatial resolution using a high-resolution monthly 1971–2000 climatology as a predictor in a thin-plate spline interpolating algorithm. Only temporally consistent climate stations with at least 40 years of record are included. Our approach is designed to produce a dataset well suited for driving hydrological models and training statistical downscaling schemes. We compare our results to two commonly used datasets and show improved performance for climate means, extremes and variability. When used to drive a hydrologic model, our dataset also outperforms these datasets for runoff ratios and streamflow trends in several, high elevation, sub-basins of the Fraser River. |
format | Online Article Text |
id | pubmed-6335611 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-63356112019-01-18 A long-term, temporally consistent, gridded daily meteorological dataset for northwestern North America Werner, A. T. Schnorbus, M. A. Shrestha, R. R. Cannon, A. J. Zwiers, F. W. Dayon, G. Anslow, F. Sci Data Data Descriptor We describe a spatially contiguous, temporally consistent high-resolution gridded daily meteorological dataset for northwestern North America. This >4 million km(2) region has high topographic relief, seasonal snowpack, permafrost and glaciers, crosses multiple jurisdictional boundaries and contains the entire Yukon, Mackenzie, Saskatchewan, Fraser and Columbia drainages. We interpolate daily station data to 1/16° spatial resolution using a high-resolution monthly 1971–2000 climatology as a predictor in a thin-plate spline interpolating algorithm. Only temporally consistent climate stations with at least 40 years of record are included. Our approach is designed to produce a dataset well suited for driving hydrological models and training statistical downscaling schemes. We compare our results to two commonly used datasets and show improved performance for climate means, extremes and variability. When used to drive a hydrologic model, our dataset also outperforms these datasets for runoff ratios and streamflow trends in several, high elevation, sub-basins of the Fraser River. Nature Publishing Group 2019-01-15 /pmc/articles/PMC6335611/ /pubmed/30644851 http://dx.doi.org/10.1038/sdata.2018.299 Text en Copyright © 2019, The Author(s) http://creativecommons.org/licenses/by/4.0/ 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 made available in this article. |
spellingShingle | Data Descriptor Werner, A. T. Schnorbus, M. A. Shrestha, R. R. Cannon, A. J. Zwiers, F. W. Dayon, G. Anslow, F. A long-term, temporally consistent, gridded daily meteorological dataset for northwestern North America |
title | A long-term, temporally consistent, gridded daily meteorological dataset for northwestern North America |
title_full | A long-term, temporally consistent, gridded daily meteorological dataset for northwestern North America |
title_fullStr | A long-term, temporally consistent, gridded daily meteorological dataset for northwestern North America |
title_full_unstemmed | A long-term, temporally consistent, gridded daily meteorological dataset for northwestern North America |
title_short | A long-term, temporally consistent, gridded daily meteorological dataset for northwestern North America |
title_sort | long-term, temporally consistent, gridded daily meteorological dataset for northwestern north america |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6335611/ https://www.ncbi.nlm.nih.gov/pubmed/30644851 http://dx.doi.org/10.1038/sdata.2018.299 |
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