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Skilful nowcasting of extreme precipitation with NowcastNet
Extreme precipitation is a considerable contributor to meteorological disasters and there is a great need to mitigate its socioeconomic effects through skilful nowcasting that has high resolution, long lead times and local details(1–3). Current methods are subject to blur, dissipation, intensity or...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10356617/ https://www.ncbi.nlm.nih.gov/pubmed/37407824 http://dx.doi.org/10.1038/s41586-023-06184-4 |
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author | Zhang, Yuchen Long, Mingsheng Chen, Kaiyuan Xing, Lanxiang Jin, Ronghua Jordan, Michael I. Wang, Jianmin |
author_facet | Zhang, Yuchen Long, Mingsheng Chen, Kaiyuan Xing, Lanxiang Jin, Ronghua Jordan, Michael I. Wang, Jianmin |
author_sort | Zhang, Yuchen |
collection | PubMed |
description | Extreme precipitation is a considerable contributor to meteorological disasters and there is a great need to mitigate its socioeconomic effects through skilful nowcasting that has high resolution, long lead times and local details(1–3). Current methods are subject to blur, dissipation, intensity or location errors, with physics-based numerical methods struggling to capture pivotal chaotic dynamics such as convective initiation(4) and data-driven learning methods failing to obey intrinsic physical laws such as advective conservation(5). We present NowcastNet, a nonlinear nowcasting model for extreme precipitation that unifies physical-evolution schemes and conditional-learning methods into a neural-network framework with end-to-end forecast error optimization. On the basis of radar observations from the USA and China, our model produces physically plausible precipitation nowcasts with sharp multiscale patterns over regions of 2,048 km × 2,048 km and with lead times of up to 3 h. In a systematic evaluation by 62 professional meteorologists from across China, our model ranks first in 71% of cases against the leading methods. NowcastNet provides skilful forecasts at light-to-heavy rain rates, particularly for extreme-precipitation events accompanied by advective or convective processes that were previously considered intractable. |
format | Online Article Text |
id | pubmed-10356617 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-103566172023-07-21 Skilful nowcasting of extreme precipitation with NowcastNet Zhang, Yuchen Long, Mingsheng Chen, Kaiyuan Xing, Lanxiang Jin, Ronghua Jordan, Michael I. Wang, Jianmin Nature Article Extreme precipitation is a considerable contributor to meteorological disasters and there is a great need to mitigate its socioeconomic effects through skilful nowcasting that has high resolution, long lead times and local details(1–3). Current methods are subject to blur, dissipation, intensity or location errors, with physics-based numerical methods struggling to capture pivotal chaotic dynamics such as convective initiation(4) and data-driven learning methods failing to obey intrinsic physical laws such as advective conservation(5). We present NowcastNet, a nonlinear nowcasting model for extreme precipitation that unifies physical-evolution schemes and conditional-learning methods into a neural-network framework with end-to-end forecast error optimization. On the basis of radar observations from the USA and China, our model produces physically plausible precipitation nowcasts with sharp multiscale patterns over regions of 2,048 km × 2,048 km and with lead times of up to 3 h. In a systematic evaluation by 62 professional meteorologists from across China, our model ranks first in 71% of cases against the leading methods. NowcastNet provides skilful forecasts at light-to-heavy rain rates, particularly for extreme-precipitation events accompanied by advective or convective processes that were previously considered intractable. Nature Publishing Group UK 2023-07-05 2023 /pmc/articles/PMC10356617/ /pubmed/37407824 http://dx.doi.org/10.1038/s41586-023-06184-4 Text en © The Author(s) 2023 https://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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zhang, Yuchen Long, Mingsheng Chen, Kaiyuan Xing, Lanxiang Jin, Ronghua Jordan, Michael I. Wang, Jianmin Skilful nowcasting of extreme precipitation with NowcastNet |
title | Skilful nowcasting of extreme precipitation with NowcastNet |
title_full | Skilful nowcasting of extreme precipitation with NowcastNet |
title_fullStr | Skilful nowcasting of extreme precipitation with NowcastNet |
title_full_unstemmed | Skilful nowcasting of extreme precipitation with NowcastNet |
title_short | Skilful nowcasting of extreme precipitation with NowcastNet |
title_sort | skilful nowcasting of extreme precipitation with nowcastnet |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10356617/ https://www.ncbi.nlm.nih.gov/pubmed/37407824 http://dx.doi.org/10.1038/s41586-023-06184-4 |
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