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
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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.

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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。
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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
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