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徐景峰, 宋林烨, 陈明轩, 等. 2023. 冬奥会复杂山地百米尺度10 m风速预报的机器学习订正对比试验[J]. 大气科学, 47(3): 805−824. doi: 10.3878/j.issn.1006-9895.2209.22117
引用本文: 徐景峰, 宋林烨, 陈明轩, 等. 2023. 冬奥会复杂山地百米尺度10 m风速预报的机器学习订正对比试验[J]. 大气科学, 47(3): 805−824. doi: 10.3878/j.issn.1006-9895.2209.22117
XU Jingfeng, SONG Linye, CHEN Mingxuan, et al. 2023. Comparative Machine Learning-Based Correction Experiment for a 10 m Wind Speed Forecast at a 100 m Resolution in Complex Mountainous Areas of the Winter Olympic Games [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 47(3): 805−824. doi: 10.3878/j.issn.1006-9895.2209.22117
Citation: XU Jingfeng, SONG Linye, CHEN Mingxuan, et al. 2023. Comparative Machine Learning-Based Correction Experiment for a 10 m Wind Speed Forecast at a 100 m Resolution in Complex Mountainous Areas of the Winter Olympic Games [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 47(3): 805−824. doi: 10.3878/j.issn.1006-9895.2209.22117

冬奥会复杂山地百米尺度10 m风速预报的机器学习订正对比试验

Comparative Machine Learning-Based Correction Experiment for a 10 m Wind Speed Forecast at a 100 m Resolution in Complex Mountainous Areas of the Winter Olympic Games

  • 摘要: 本文以传统机器学习算法XGBoost和深度学习算法CU-Net为基础,针对北京快速更新无缝隙融合与集成预报系统(RISE系统)预报的北京冬奥会延庆及张家口赛区100米分辨率的冬季近地面10 m风速数据,进行每日逐小时起报的未来逐6小时间隔的冬奥高山站点及其周边地区风速预报偏差订正方法研究和对比分析。对于站点订正,首先将RISE系统预测的10 m风速插值到对应的自动气象站站点,然后根据风速等级表归类,针对每个分类单独构建XGBoost模型,每个区间模型合并后形成L-XGBoost,使用均方根误差和预报准确率作为评分标准,结果表明风速归类的L-XGBoost算法订正效果比不归类的原始XGBoost模型有一定提升,说明在传统机器学习中加入归类方法有助于改善复杂山地站点风速预报技巧。对于站点及其周边地区风速订正,本文在CU-Net模型基础上,通过引入不同深度的CU-Net子网络,构建了新的算法模型CU-Net++,并考虑了预报日变化误差和复杂地形对10 m风速的影响,以自动气象站为中心构建空间小区域样本数据,对RISE系统风速预报偏差进行订正。试验结果表明,CU-Net和CU-Net++均可以充分挖掘时间和空间维度的风场变化规律,且CU-Net++模型风速订正结果优于CU-Net模型,有效降低了RISE产品的格点风速预报误差,也发现预报误差和复杂地形的引入对10 m风速偏差订正起到重要的正向作用。

     

    Abstract: Based on a traditional machine learning algorithm (XGBoost), a deep learning algorithm (CU-Net), and the winter wind speed data from 10 m near the ground with a resolution of 100 m, this paper studied and compared the correction methods for wind speed forecast deviation in the mountainous stations and surrounding areas of the Yanqing and Zhangjiakou competition areas (Beijing Winter Olympic Games) using the rapid-refresh integrated seamless ensemble (RISE) system. For station correction, the 10-m wind speed predicted by the RISE system is interpolated to the corresponding automatic weather station. Subsequently, a separate XGBoost model is constructed for each classification according to the wind speed rating table. Afterward, each interval model was combined to form L-XGBoost, using the root mean square error and forecast accuracy as its scoring standard. Investigations revealed that the correction effect of the L-XGBoost algorithm for wind speed classification was better than the original XGBoost model without classification, indicating that introducing a classification method to traditional machine learning helped improve the wind speed prediction skills of the complex mountain stations. Subsequently, for the wind speed correction of the station and its surrounding areas based on the CU-Net model, this paper constructed a new algorithm model (CU-Net++) by introducing the CU-Net sub-networks with different depths, considering the influence of daily forecast errors and complex terrains on the 10-m wind speed. This paper also constructed spatial small-area sample data, considering the automatic weather station as the center, to correct the wind speed prediction deviation of the RISE system. The test results indicated that although both CU-Net and CU-Net++ fully mined the wind field change rules in time and space dimensions, the wind speed correction results of the CU-Net++ model performed better than those of the CU-Net model, effectively reducing the grid wind speed prediction error of RISE products. Hence, introducing prediction error and complex terrain plays an important positive role in the deviation correction of a surface 10 m wind speed-based investigation.

     

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