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An Integrated Correction Method for 2 m Temperature and Its Application to Yanqing Competition Zone of Olympic Winter Games
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摘要: 为提升北京冬(残)奥会气象服务保障能力，利用2018—2021年1月1日—3月28日欧洲中期天气预报中心(ECMWF)模式预报产品以及冬奥延庆赛区8个自动气象站的2 m气温实况，通过基于地形修正的模式偏差订正和支持向量机算法，构建赛区不同海拔高度站点72 h预报时效内逐3 h的2 m气温集成订正方法。2022年北京冬(残)奥会前夕及赛事期间应用评估表明：集成订正方法对延庆赛区2 m气温的预报准确率为0.856，平均绝对偏差为1.08℃，订正效果较单一订正方法更优，尤其针对海拔高度高出模式地形高度的站点订正性能更为突出，同时，对超阈值及关键过程的气温订正效果也表现较好。对于延庆赛区大多数站点而言，该方法订正的72 h预报时效内逐3 h的2 m气温平均绝对偏差总体上表现出一定的日变化特征，且0~24 h, 24~48 h, 48~72 h预报时效之间偏差变化相对平稳，但不同站点的日变化趋势存在差异。随着预报时效增加，该方法订正的2 m气温平均绝对偏差的变化趋势表现出海拔依赖性。Abstract: The snow events in Olympic Winter Games and Paralympic Winter Games are held outdoors in mountainous areas and especially weather sensitive. Unfavorable weather conditions can lead to delays or cancellations of competitions, and even affect the safety of athletes. With dramatic variability in the surface meteorological conditions at venues due to local topographic effects, the provision of timely and accurate high-quality weather prediction pose serious challenges to forecasters. Over the past decades, the accuracy of numerical weather prediction models is gradually improving, but there are still some systematic errors because of the inherent modeling deficiencies, especially in areas characterized by highly variable orography. Fortunately, the weather prediction accuracy is usually improved with the help of post-processing techniques.In order to improve the meteorological service capability of the snow events, an integrated correction method for 2 m temperature prediction, composed of the model bias correction based on terrain correction and support vector machine algorithm, is proposed at sites with different altitudes within 72 h at 3 h interval. European Center for Medium Range Weather Forecasts (ECMWF) model data from 1 January to 28 March during 2018-2021 and the observation of 2 m temperature at eight automatic weather stations in Yanqing competition zone are used. The performance of the integrated correction method is evaluated before and during Olympic Winter Games and Paralympic Winter Games. The results show that the accuracy of the correction method is 0.856 and the mean absolute error is 1.08℃ for 2 m temperature prediction in Yanqing competition zone. The integrated correction method is better than the single algorithm, and performs well in 2 m temperature prediction exceeding the threshold and key weather process forecasts. The performance of the integrated correction method is more outstanding especially for the sites higher than the terrain height of the model. For most of the sites, the mean absolute error of 2 m temperature predicted by the integrated correction method within 72 h at 3 h interval generally shows a certain diurnal variation, and the variation of mean absolute error within 0-24 h, 24-48 h and 48-72 h lead time is relatively stable, but the daily variation trend is different at different sites. With the increase of lead times, the mean absolute error of 2 m temperature predicted by the integrated correction method shows the altitude dependence. The relevant research results may be extended to other complex mountainous areas to improve the weather prediction accuracy.
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图 1 北京冬奥延庆赛区地形高度(阴影) 及主要自动气象站点(空心三角) 分布
Fig. 1 Terrain height (the shaded) of Yanqing competition zone of Beijing Winter Olympics and main automatic weather stations (hollow triangles)
图 2 利用模式不同高度层温度基于地形订正方法的2 m气温效果评估
Fig. 2 Evaluation of 2 m temperature predicted by the terrain correction method using temperatures at different model levels
图 3 2022年1月1日—3月15日不同方法的2 m气温订正效果评估
Fig. 3 Evaluation of 2 m temperature corrected by different methods from 1 Jan to 15 Mar in 2022
图 4 2022年1月1日—3月15日基于不同订正方法的2 m气温平均绝对偏差随预报时效变化
Fig. 4 Mean absolute error of 2 m temperature corrected by different methods varing with different lead times from 1 Jan to 15 Mar in 2022
图 5 2022年1月1日—3月15日不同方法对超阈值2 m气温订正的平均绝对偏差
Fig. 5 Mean absolute errors of 2 m temperature exceeding the threshold corrected by different methods from 1 Jan to 15 Mar in 2022
图 6 不同方法订正的2022年2月11日20:00— 2月14日20:00的2 m气温与实况对比
Fig. 6 Comparison of 2 m temperature corrected by different methods to observation from 2000 BT 11 Feb to 2000 BT 14 Feb in 2022
图 7 不同方法订正的2022年3月7日08:00—8日20:00的2 m气温与实况对比
Fig. 7 Comparison of 2 m temperature corrected by different methods to observation from 0800 BT 7 Mar to 2000 BT 8 Mar in 2022
表 1 最优的支持向量机模型参数及其性能
Table 1 Optimal parameters of support vector machine model and corresponding performance
站号 参数 评估指标 C γ 准确率 平均绝对偏差/℃ A1701 0.09 100 0.858 1.07 A1703 0.10 1000 0.823 1.20 A1705 0.02 100 0.857 1.10 A1708 0.06 1000 0.806 1.23 A1710 0.02 1000 0.873 1.01 A1711 0.05 1000 0.866 1.07 A1712 0.02 1 0.864 1.07 A1489 0.10 100 0.713 1.53 
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表 2 2022年1月1日—3月15日不同方法不同预报时效内2 m气温订正的平均绝对偏差(单位：℃)
Table 2 Mean absolute errors of 2 m temperature corrected by different methods within different lead times from 1 Jan to 15 Mar in 2022 (unit:℃)
站号 方法 预报时效 0~24 h 24~48 h 48~72 h A1701 地形订正 1.22 1.20 1.36 支持向量机 1.27 1.16 1.23 集成订正 1.14* 1.11* 1.23* A1703 地形订正 1.25 1.21 1.38 支持向量机 1.27 1.17 1.32 集成订正 1.11* 1.05* 1.19* A1705 地形订正 0.81 0.95 1.16 支持向量机 0.85 0.91 1.13 集成订正 0.77* 0.89* 1.10* A1708 地形订正 0.93 1.09 1.26 支持向量机 1.06 1.08 1.15 集成订正 0.96 1.06* 1.17 A1710 地形订正 1.07 1.05 1.26 支持向量机 1.12 1.06 1.23 集成订正 0.95* 0.92* 1.10* A1711 地形订正 1.20 1.20 1.37 支持向量机 1.07 0.95 1.11 集成订正 1.01* 0.96 1.10* A1712 地形订正 0.75 0.89 1.06 支持向量机 0.83 0.86 1.02 集成订正 0.75* 0.88 1.05 A1489 地形订正 1.49 1.54 1.64 支持向量机 1.30 1.33 1.38 集成订正 1.34 1.40 1.45 注：*表示集成订正方法不高于地形订正方法和支持向量机方法。
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表 3 不同站点2 m气温阈值(单位：℃)
Table 3 Thresholds of 2 m temperature at different stations (unit:℃)
站号 超低温阈值 超高温阈值 A1701 -15.8 -4.9 A1703 -13.6 -2.7 A1705 -10.6 -0.6 A1708 -9.1 0.7 A1710 -12.6 -2.9 A1711 -12.0 -1.5 A1712 -10.2 -0.3 A1489 -8.0 2.1
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