Multi-model ensemble forecasting of 10-m wind speed over eastern China based on machine learning optimization

IF 2.3 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES Atmospheric and Oceanic Science Letters Pub Date : 2023-09-01 DOI:10.1016/j.aosl.2023.100402
Ting Lei , Jingjing Min , Chao Han , Chen Qi , Chenxi Jin , Shuanglin Li
{"title":"Multi-model ensemble forecasting of 10-m wind speed over eastern China based on machine learning optimization","authors":"Ting Lei ,&nbsp;Jingjing Min ,&nbsp;Chao Han ,&nbsp;Chen Qi ,&nbsp;Chenxi Jin ,&nbsp;Shuanglin Li","doi":"10.1016/j.aosl.2023.100402","DOIUrl":null,"url":null,"abstract":"<div><p>Wind substantially impacts human activity and electricity generation. Thus, accurately forecasting the short-term wind speed is of profound societal and economic significance. Based on 100 weather stations in eastern China, the authors first evaluate the performance of the 10-m wind forecast products from five operational forecast models. Among them, the Japan Meteorological Agency (JMA) model performs best in reducing the forecasting errors. Then, the authors establish a 10-m wind speed multimodel ensemble forecast based on the five numerical models’ outputs and machine learning methods, combining dynamic and statistical methods. Feature engineering and machine learning algorithm optimization are conducted for each site separately. The forecast performance of this method is compared to the JMA model and multimodel ensemble forecast by ridge regression at lead times of 24–96 h. The results demonstrate that the multimodel ensemble method based on machine learning optimization can reduce the forecast error of JMA by more than 39%, and the improvement in forecast skill is most evident in November. In addition, it performs better than the ensemble forecast by ridge regression.</p></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":"16 5","pages":"Article 100402"},"PeriodicalIF":2.3000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Atmospheric and Oceanic Science Letters","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1674283423000880","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Wind substantially impacts human activity and electricity generation. Thus, accurately forecasting the short-term wind speed is of profound societal and economic significance. Based on 100 weather stations in eastern China, the authors first evaluate the performance of the 10-m wind forecast products from five operational forecast models. Among them, the Japan Meteorological Agency (JMA) model performs best in reducing the forecasting errors. Then, the authors establish a 10-m wind speed multimodel ensemble forecast based on the five numerical models’ outputs and machine learning methods, combining dynamic and statistical methods. Feature engineering and machine learning algorithm optimization are conducted for each site separately. The forecast performance of this method is compared to the JMA model and multimodel ensemble forecast by ridge regression at lead times of 24–96 h. The results demonstrate that the multimodel ensemble method based on machine learning optimization can reduce the forecast error of JMA by more than 39%, and the improvement in forecast skill is most evident in November. In addition, it performs better than the ensemble forecast by ridge regression.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于机器学习优化的中国东部10 m风速多模式集合预报
风极大地影响着人类活动和发电。因此,准确预报短期风速具有深远的社会和经济意义。基于中国东部100个气象站数据,对5种业务预报模式的10米风预报产品的性能进行了评价。其中,日本气象厅(JMA)模式在减少预报误差方面表现最好。然后,基于5个数值模型的输出和机器学习方法,结合动态和统计方法,建立10 m风速多模式集合预报。对每个站点分别进行特征工程和机器学习算法优化。结果表明,基于机器学习优化的多模型集成预报方法可使JMA的预报误差降低39%以上,预报技能的提高在11月份最为明显。此外,它比脊回归的集合预报效果更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Atmospheric and Oceanic Science Letters
Atmospheric and Oceanic Science Letters METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
4.20
自引率
8.70%
发文量
925
审稿时长
12 weeks
期刊最新文献
Implications of the extremely hot summer of 2022 on urban ozone control in China Impacts of global biogenic isoprene emissions on surface ozone during 2000–2019 Enhanced nitrous acid (HONO) formation via NO2 uptake and its potential contribution to heavy haze formation during wintertime A portable instrument for measurement of atmospheric Ox and NO2 based on cavity ring-down spectroscopy Vertical distributions of VOCs in the Tibetan Plateau background region
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1