An artificial intelligence reconstruction of global gridded surface winds
Gridded wind speed data products with global coverage and continuous long-term time series are widely used in many applications, such as evaluating wind energy potential [1] and drought processes [2]. However, some available products do not accurately reproduce observed wind speed trends on land [3], [4], leading to biased or inaccurate conclusions in studies on wind-related phenomena. In-situ weather stations involve direct measurements that accurately preserve wind speed trends. Still, the uneven distribution and incomplete time series have constrained their widespread applications in regional and global analyses. These limitations, which we have encountered firsthand in investigating the global wind stilling and reversal phenomena [4], have inspired us to create a new global gridded surface wind product that preserves observed wind patterns and trends.
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Elsevier
2022
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Online Access: | http://hdl.handle.net/10261/287670 http://dx.doi.org/10.13039/501100011033 http://dx.doi.org/10.13039/501100001809 http://dx.doi.org/10.13039/501100003359 |
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dig-cide-es-10261-2876702023-01-26T14:50:02Z An artificial intelligence reconstruction of global gridded surface winds Zhou, Lihong Liu, Haofeng Jiang, Xin Ziegler, Alan D. Azorín-Molina, César Liu, Jiang Zeng, Zhenzhong National Natural Science Foundation of China Southern University of Science and Technology (China) Ministerio de Ciencia, Innovación y Universidades (España) Agencia Estatal de Investigación (España) Generalitat Valenciana Gridded wind speed data products with global coverage and continuous long-term time series are widely used in many applications, such as evaluating wind energy potential [1] and drought processes [2]. However, some available products do not accurately reproduce observed wind speed trends on land [3], [4], leading to biased or inaccurate conclusions in studies on wind-related phenomena. In-situ weather stations involve direct measurements that accurately preserve wind speed trends. Still, the uneven distribution and incomplete time series have constrained their widespread applications in regional and global analyses. These limitations, which we have encountered firsthand in investigating the global wind stilling and reversal phenomena [4], have inspired us to create a new global gridded surface wind product that preserves observed wind patterns and trends. This work was supported by the National Natural Science Foundation of China (42071022), the start-up fund provided by Southern University of Science and Technology (29/Y01296122), and Highlight Project on Water Security and Global Change of Southern University of Science and Technology (G02296302). Cesar Azorin-Molina was supported by Evaluación y atribución de la variabilidad de la velocidad media y las rachas máximas de viento: causas del fenómeno stilling (RTI2018-095749-A-100), Cambios observados, proyecciones futuras e índices de la velocidad del viento y sus extremos en la Comunidad Valenciana (AICO/2021/023), and the Spanish National Research Centre Interdisciplinary Thematic Platform PTI-CLIMA. We thank Met Office (HadISD), ECMWF (ERA5), and CMIP6 for providing wind speed data used in this work. 2023-01-26T14:50:02Z 2023-01-26T14:50:02Z 2022 2023-01-26T14:50:02Z artículo doi: 10.1016/j.scib.2022.09.022 issn: 2095-9273 e-issn: 2095-9281 Science Bulletin 67(20): 2060-2063 (2022) http://hdl.handle.net/10261/287670 10.1016/j.scib.2022.09.022 http://dx.doi.org/10.13039/501100011033 http://dx.doi.org/10.13039/501100001809 http://dx.doi.org/10.13039/501100003359 #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095749-A-I00/ES/EVALUACION Y ATRIBUCION DE LA VARIABILIDAD DE LA VELOCIDAD MEDIA Y LAS RACHAS MAXIMAS DE VIENTO: CAUSAS DEL FENOMENO STILLING/ http://dx.doi.org/10.1016/j.scib.2022.09.022 Sí none Elsevier National Natural Science Foundation of China Chinese Academy of Sciences |
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Gridded wind speed data products with global coverage and continuous long-term time series are widely used in many applications, such as evaluating wind energy potential [1] and drought processes [2]. However, some available products do not accurately reproduce observed wind speed trends on land [3], [4], leading to biased or inaccurate conclusions in studies on wind-related phenomena. In-situ weather stations involve direct measurements that accurately preserve wind speed trends. Still, the uneven distribution and incomplete time series have constrained their widespread applications in regional and global analyses. These limitations, which we have encountered firsthand in investigating the global wind stilling and reversal phenomena [4], have inspired us to create a new global gridded surface wind product that preserves observed wind patterns and trends. |
author2 |
National Natural Science Foundation of China |
author_facet |
National Natural Science Foundation of China Zhou, Lihong Liu, Haofeng Jiang, Xin Ziegler, Alan D. Azorín-Molina, César Liu, Jiang Zeng, Zhenzhong |
format |
artículo |
author |
Zhou, Lihong Liu, Haofeng Jiang, Xin Ziegler, Alan D. Azorín-Molina, César Liu, Jiang Zeng, Zhenzhong |
spellingShingle |
Zhou, Lihong Liu, Haofeng Jiang, Xin Ziegler, Alan D. Azorín-Molina, César Liu, Jiang Zeng, Zhenzhong An artificial intelligence reconstruction of global gridded surface winds |
author_sort |
Zhou, Lihong |
title |
An artificial intelligence reconstruction of global gridded surface winds |
title_short |
An artificial intelligence reconstruction of global gridded surface winds |
title_full |
An artificial intelligence reconstruction of global gridded surface winds |
title_fullStr |
An artificial intelligence reconstruction of global gridded surface winds |
title_full_unstemmed |
An artificial intelligence reconstruction of global gridded surface winds |
title_sort |
artificial intelligence reconstruction of global gridded surface winds |
publisher |
Elsevier |
publishDate |
2022 |
url |
http://hdl.handle.net/10261/287670 http://dx.doi.org/10.13039/501100011033 http://dx.doi.org/10.13039/501100001809 http://dx.doi.org/10.13039/501100003359 |
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