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
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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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Key words:
- 100 m scale forecast /
- Complex mountain /
- Machine learning /
- Wind speed correction
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图 1 研究区域和站点分布（填色为海拔高度，单位：m）
Figure 1. Study area and station distribution (the shaded part indicates altitude. units: m)
图 2 L-XGBoost模型架构组成
Figure 2. Schematic showing the L-XGBoost model architecture’ s composition
图 6 预报时效为（a）6 h、（b）12 h、（c）18 h、（d）24 h的各风力区间10 m全风速订正结果。横坐标为风速区间，左坐标为RMSE，右坐标为误差减小率，绿色图例为模型订正后相比RISE（Rapid-refresh Itegrated Seamless Ensemble）原始预报RMSE减少百分比
Figure 6. Schematic showing the 10-m full wind speed correction results for each wind speed interval with forecast time effects of (a) 6 h, (b) 12 h, (c) 18 h, and (d) 24 h. Abscissa indicated wind speed interval, the left coordinate indicates RMSE, and the right coordinate indicates error reduction rate, the green legend shows the percentage reduction in RMSE compared to the original forecast of RISE after model correction
图 7 预报时效为（a）6 h、（b）12 h、（c）18 h、（d）24 h的各风力区间10 m全风速订正结果（横坐标为风速区间，纵坐标为预报准确率和准确率提升百分比，绿色图例为模型订正后相比RISE原始预报准确率增加百分比）
Figure 7. Schematic showing the 10 m full wind speed correction results for each wind speed interval with forecast time effects of (a) 6 h, (b) 12 h, (c) 18 h, and (d) 24 h (abscissa indicates wind speed intervals, the ordinate indicates wind prediction accuracy, The green legend shows the percentage increase in accuracy of the original forecast compared to RISE after model correction)
图 8 预报时效18 h情况下L-XGBoost子模型特征重要性权重分布。（a）−（g）分别表示风速等级1~7子模型的特征权重，横坐标为特征分数，纵坐标为特征名称
Figure 8. Weight distribution showing the feature importance of L-XGBoost sub-models after 18 h forecast. (a)−(g) represent the feature weights of sub models with wind speed levels 1−7, respectively. The horizontal axis represents the feature score, and the vertical axis represents the feature name
图 9 XGBoost和L-XGBoost两种模型10 m风速订正后的预报RMSE（左坐标）和误差减小率（右坐标）：（a）A1489站；（b）A1491站；（c）A1703站；（d）B1649站
Figure 9. Forecasted RMSE (left coordinate) and error reduction rate (right coordinate) after a 10 m wind speed correction with the XGBoost and L-XGBoost models: (a) The A1489 station; (b) the A1491 station; (c) the A1703 station; (d) the B1649 station
图 11 二海陀站及其周边样本（A1491）在预报时效6 h、12 h、18 h、24 h下10 m风速订正结果对比：（a，c，e，g）RISE系统原始预报的RMSE；（b，d，f，g）CU-Net++订正后的RMSE
Figure 11. Comparison of 10 m wind speed correction results between the Erhaituo Station and its surrounding samples (A1491) under prediction time of 6 h, 12 h, 18 h, and 24 h: (a, c, e, g) Originally predicted RMSE by the RISE system; (b, d, f, g) the CU-Net++ corrected RMSE
图 12 竞速三号站及其周边样本（A1703）在预报时效6 h、12 h、18 h、24 h下10 m风速订正结果对比：（a，c，e，g）RISE系统原始预报的RMSE；（b，d，f，g）CU-Net++订正后的RMSE
Figure 12. Comparison of 10 m wind speed correction results between the Racing Station 3 and its surrounding samples (A1703) under prediction time of 6 h, 12 h, 18 h, and 24 h: (a, c, e, g) Originally predicted RMSE by the RISE system; (b, d, f, g) the CU-Net++ corrected RMSE
表 1 风力等级
Table 1. Wind grade
风力等级 风速/m s−1 描述 1 [0，1.5) 软风 2 [1.5，3.3) 轻风 3 [3.3，5.4) 微风 4 [5.4，7.9) 和风 5 [7.9，10.7) 清劲风 6 [10.7，13.8) 强风 7 ≥13.8 疾风 
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表 2 风速区间对应样本数
Table 2. Number of samples corresponding to wind speed range
风速等级 样本个数 1 70102 2 89019 3 63754 4 31125 5 10293 6 3840 7 1756
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表 3 数据增强后风速区间对应样本数
Table 3. Number of samples corresponding to wind speed range after data enhancement
风速等级 样本个数 1 70102 2 89019 3 63754 4 31125 5 10293 6 11443 7 5287
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表 4 单个样本组成
Table 4. Single sample composition
模型特征 标签 静态数据 实况数据 预测数据 误差 elev ws2at，wd2at，tst，
tdst，rhst，qst$ \text{usf}{\text{c}}_{t}^{\text{18}} $，$ \text{vsf}{\text{c}}_{t}^{\text{18}} $，
$ \text{uvf}{\text{c}}_{t}^{\text{18}} $$ {d}_{{t-24}}^{18} $，$ {d}_{{t-18}}^{\text{18}} $ ws2at+18
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表 5 L-XGBoost模型和XGBoost模型对10 m风速订正结果RMSE指标对比
Table 5. Comparison of correction results of L-XGBoost and XGBoost models for 10-m wind speed in terms of RMSE
预报时效/h RMSE/m s−1 订正预报RMSE相对比原RMSE的变化百分比 RISE XGBoost L-XGBoost XGBoost_Rate L-XGBoost_Rate 6 1.839 1.408 1.331 23.38% 27.62% 12 1.869 1.467 1.405 21.5% 24.83% 18 1.916 1.491 1.429 22.13% 25.42% 24 1.927 1.493 1.442 22.53% 25.17%
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表 6 L-XGBoost模型和XGBoost模型对10 m风速订正结果FA指标对比
Table 6. Comparison of correction results of L-XGBoost and XGBoost models for 10-m wind speed in terms of Forecast Accuracy （FA）
预报时效/h 预报准确率 订正预报准确率相比RISE原始准确率变化百分比 RISE XGBoost L-XGBoost XGBoost_Rate L-XGBoost_Rate 6 67.75% 78.15% 80.87% 10.40% 13.12% 12 67.13% 76.09% 79.00% 8.96% 11.87% 18 66.63% 75.30% 78.09% 8.67% 11.46% 24 65.61% 75.77% 78.44% 10.16% 12.83%
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表 7 两组对照试验的单个样本组成
Table 7. Single sample composition of two groups of control experiments
特征 特征尺寸 标签 标签尺寸 （P, Wnow） （2×48×48） L （1×48×48） （P, Wnow, H, E1, E2） （5×48×48） L （1×48×48）
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表 8 预报时效6 h下各种方法10 m 风速订正试验结果评分表
Table 8. Scoring table for 10 m wind speed correction test results of various methods under 6 h forecast time
模型 RMSE评分/m s−1 提升百分比 RISE原始 1.6600 / CU-Net（不加误差和地形） 1.2815 22.80% CU-Net（加误差和地形） 1.2467 24.90% CU-Net++（不加误差和地形） 1.2438 25.07% CU-Net++（加误差和地形） 1.2263 26.13%
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表 9 预报时效12 h下各种方法10 m风速订正试验结果评分表
Table 9. Scoring table for 10 m wind speed correction test results of various methods under 12 h forecast time
模型 RMSE评分/m s−1 提升百分比 RISE原始 1.6908 / CU-Net（不加误差和地形） 1.3044 22.85% CU-Net（加误差和地形） 1.2931 23.52% CU-Net++（不加误差和地形） 1.2799 24.30% CU-Net++（加误差和地形） 1.2694 24.92%
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表 10 预报时效18 h下各种方法10 m风速订正试验结果评分表
Table 10. Scoring table for 10 m wind speed correction test results of various methods under 18 h forecast time
模型 RMSE评分/m s−1 提升百分比 RISE原始 1.7285 / CU-Net（不加误差和地形） 1.329 23.11% CU-Net（加误差和地形） 1.3105 24.18% CU-Net++（不加误差和地形） 1.3097 24.23% CU-Net++（加误差和地形） 1.3026 24.64%
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表 11 预报时效24 h下各种方法10 m风速订正试验结果评分表
Table 11. Scoring table for 10 m wind speed correction test results of various methods under 24 h forecast time
模型 RMSE评分/m s−1 提升百分比 RISE原始 1.7353 / CU-Net（不加误差和地形） 1.3485 22.29% CU-Net（加误差和地形） 1.3382 22.88% CU-Net++（不加误差和地形） 1.3328 23.19% CU-Net++（加误差和地形） 1.3267 23.54%
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