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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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[1] Thériault J M, Stewart R E, Henson W.On the dependence of winter precipitation types on temperature, precipitation rate, and associated features.J Appl Meteor Climatol, 2010, 49(7):1429-1442. doi: 10.1175/2010JAMC2321.1 [2] 胡宁, 符娇兰, 孙军, 等. 北京一次冬季极端降水过程中相态转换预报的误差分析. 气象学报, 2021, 79(2): 328-339. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202102011.htmHu N, Fu J L, Sun J, et al. Errors in the forecast of precipitation type transition in an extreme winter precipitation event in Beijing. Acta Meteor Sinica, 2021, 79(2): 328-339. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202102011.htm [3] 智协飞, 吴佩, 俞剑蔚, 等. GFS模式地形高度偏差对地面2 m气温预报的影响. 大气科学学报, 2019, 42(5): 652-659. https://www.cnki.com.cn/Article/CJFDTOTAL-NJQX201905002.htmZhi X F, Wu P, Yu J W, et al. Impact of topographic altitude bias of the GFS model on the 2 m air temperature forecast. Trans Atmos Sci, 2019, 42(5): 652-659. https://www.cnki.com.cn/Article/CJFDTOTAL-NJQX201905002.htm [4] Hart K A, Steenburgh W J, Onton D J, et al. An evaluation of mesoscale-model-based model output statistics(MOS) during the 2002 Olympic and Paralympic Winter Games. Wea Forecasting, 2004, 19(2): 200-218. doi: 10.1175/1520-0434(2004)019<0200:AEOMMO>2.0.CO;2 [5] 曾晓青, 赵声蓉, 段云霞. 基于MOS方法的风向预测方案对比研究. 气象与环境学报, 2013, 29(6): 140-144. https://www.cnki.com.cn/Article/CJFDTOTAL-LNQX201306022.htmZeng X Q, Zhao S R, Duan Y X. Comparison of wind direction forecast schemes based on MOS method. J Meteor Environ, 2013, 29(6): 140-144. https://www.cnki.com.cn/Article/CJFDTOTAL-LNQX201306022.htm [6] 韩念霏, 杨璐, 陈明轩, 等. 京津冀站点风温湿要素的机器学习订正方法. 应用气象学报, 2022, 33(4): 489-500. doi: 10.11898/1001-7313.20220409Han N F, Yang L, Chen M X, et al. Machine learning correction of wind, temperature and humidity elements in Beijing-Tianjin-Hebei Region. J Appl Meteor Sci, 2022, 33(4): 489-500. doi: 10.11898/1001-7313.20220409 [7] Glahn H R, Lowry D A. The use of model output statistics (MOS) in objective weather forecasting. J Appl Meteor, 1972, 11(8): 1203-1211. doi: 10.1175/1520-0450(1972)011<1203:TUOMOS>2.0.CO;2 [8] 刘还珠, 赵声蓉, 陆志善, 等. 国家气象中心气象要素的客观预报——MOS系统. 应用气象学报, 2004, 15(2): 181-191. http://qikan.camscma.cn/article/id/20040223Liu H Z, Zhao S R, Lu Z S, et al. Objective element forecasts at NMC—A MOS system. J Appl Meteor Sci, 2004, 15(2): 181-191. http://qikan.camscma.cn/article/id/20040223 [9] 张华, 叶燕华. 利用最近资料改进MOS预报的方法. 高原气象, 2003, 22(2): 127-131. doi: 10.3321/j.issn:1000-0534.2003.02.005Zhang H, Ye Y H. A new way for improving MOS forecast with the latest real observational data. Plateau Meteor, 2003, 22(2): 127-131. doi: 10.3321/j.issn:1000-0534.2003.02.005 [10] 杞明辉, 肖子牛, 晏红明. 一种改进的考虑环流特征的MOS预报方法. 高原气象, 2003, 22(4): 405-409. doi: 10.3321/j.issn:1000-0534.2003.04.015Qi M H, Xiao Z N, Yan H M. An improved method MOS forecast based on circulation characteristics. Plateau Meteor, 2003, 22(4): 405-409. doi: 10.3321/j.issn:1000-0534.2003.04.015 [11] 罗菊英, 周建山, 闫永财. 基于数值预报及上级指导产品的本地气温MOS预报方法. 气象科技, 2014, 42(3): 443-450. doi: 10.3969/j.issn.1671-6345.2014.03.015Luo J Y, Zhou J S, Yan Y C. Local temperature MOS forecast method based on numerical forecast products and superior guidance. Meteor Sci Technol, 2014, 42(3): 443-450. doi: 10.3969/j.issn.1671-6345.2014.03.015 [12] 张玉涛, 佟华, 孙健. 一种偏差订正方法在平昌冬奥会气象预报的应用. 应用气象学报, 2020, 31(1): 27-41. doi: 10.11898/1001-7313.20200103Zhang Y T, Tong H, Sun J. Application of a bias correction method to meteorological forecast for the Pyeongchang Winter Olympic Games. J Appl Meteor Sci, 2020, 31(1): 27-41. doi: 10.11898/1001-7313.20200103 [13] 钱莉, 兰晓波, 杨永龙. 最优子集神经网络在武威气温客观预报中的应用. 气象, 2010, 36(5): 102-107. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201005017.htmQian L, Lan X B, Yang Y L. The application of optimal subset neural network to temperature objective forecast in Wuwei. Meteor Mon, 2010, 36(5): 102-107. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201005017.htm [14] 韩丰, 杨璐, 周楚炫, 等. 基于探空数据集成学习的短时强降水预报试验. 应用气象学报, 2021, 32(2): 188-199. doi: 10.11898/1001-7313.20210205Han F, Yang L, Zhou C X, et al. An experimental study of the short-time heavy rainfall event forecast based on ensemble learning and sounding data. J Appl Meteor Sci, 2021, 32(2): 188-199. doi: 10.11898/1001-7313.20210205 [15] 谭江红, 陈伟亮, 王珊珊. 一种机器学习方法在湖北定时气温预报中的应用试验. 气象科技进展, 2018, 8(5): 46-50. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKZ201805015.htmTan J H, Chen W L, Wang S S. Using a machine learning method for temperature forecast in Hubei Province. Adv Meteor Sci Tech, 2018, 8(5): 46-50. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKZ201805015.htm [16] 赵琳娜, 卢姝, 齐丹, 等. 基于全连接神经网络方法的日最高气温预报. 应用气象学报, 2022, 33(3): 257-269. doi: 10.11898/1001-7313.20220301Zhao L N, Lu S, Qi D, et al. Daily maximum air temperature forecast based on fully connected neural network. J Appl Meteor Sci, 2022, 33(3): 257-269. doi: 10.11898/1001-7313.20220301 [17] 冯汉中, 陈永义. 处理非线性分类和回归问题的一种新方法(Ⅱ)——支持向量机方法在天气预报中的应用. 应用气象学报, 2004, 15(3): 355-365. http://qikan.camscma.cn/article/id/20040345Feng H Z, Chen Y Y. A new method for non-linear classify and non-linear regression Ⅱ: Application of support vector machine to weather forecast. J Appl Meteor Sci, 2004, 15(3): 355-365. http://qikan.camscma.cn/article/id/20040345 [18] Radhika Y, Shashi M. Atmospheric temperature prediction using support vector machines. Int J Comput Theor Eng, 2009, 1(1): 55-58. [19] 谢舜, 孙效功, 张苏平, 等. 基于SVD与机器学习的华南降水预报订正方法. 应用气象学报, 2022, 33(3): 293-304. doi: 10.11898/1001-7313.20220304Xie S, Sun X G, Zhang S P, et al. Precipitation forecast correction in South China based on SVD and machine learning. J Appl Meteor Sci, 2022, 33(3): 293-304. doi: 10.11898/1001-7313.20220304 [20] 陈昱文, 黄小猛, 李熠, 等. 基于ECMWF产品的站点气温预报集成学习误差订正. 应用气象学报, 2020, 31(4): 494-503. doi: 10.11898/1001-7313.20200411Chen Y W, Huang X M, Li Y, et al. Ensemble learning for bias correction of station temperature forecast based on ECMWF products. J Appl Meteor Sci, 2020, 31(4): 494-503. doi: 10.11898/1001-7313.20200411 [21] 嵇磊, 王在文, 陈敏, 等. 人工智能技术能否提高地面气温预报的精度——记AI Challenger 2018全球天气预报挑战赛. 气象学报, 2019, 77(5): 960-964. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201905014.htmJi L, Wang Z W, Chen M, et al. How much can AI techniques improve surface air temperature forecast: A report from AI Challenger 2018 Global Weather Forecast Contest. Acta Meteor Sinica, 2019, 77(5): 960-964. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201905014.htm [22] 王在文, 郑祚芳, 陈敏, 等. 支持向量机非线性回归方法的气象要素预报. 应用气象学报, 2012, 23(5): 562-570. http://qikan.camscma.cn/article/id/20120506Wang Z W, Zheng Z F, Chen M, et al. Prediction of meteorological elements based on nonlinear support vector machine regression method. J Appl Meteor Sci, 2012, 23(5): 562-570. http://qikan.camscma.cn/article/id/20120506 [23] Li H C, Yu C, Xia J J, et al. A model output machine learning method for grid temperature forecasts in the Beijing Area. Adv Atmos Sci, 2019, 36(10): 1156-1170. [24] 王倩倩, 权建农, 程志刚, 等. 2019年冬季北京海陀山局地环流特征及机理分析. 气象学报, 2022, 80(1): 93-107. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202201007.htmWang Q Q, Quan J N, Cheng Z G, et al. Local circulation characteristics and mechanism analysis of Haituo Mountain in Beijing during winter 2019. Acta Meteor Sinica, 2022, 80(1): 93-107. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202201007.htm [25] 王颖, 任国玉. 中国高空温度变化初步分析. 气候与环境研究, 2005, 10(4): 780-790. https://www.cnki.com.cn/Article/CJFDTOTAL-QHYH200504009.htmWang Y, Ren G Y. Change in free atmospheric temperature over China during 1961-2004. Climatic Environ Res, 2005, 10(4): 780-790. https://www.cnki.com.cn/Article/CJFDTOTAL-QHYH200504009.htm [26] Chen Z S, Chen Y N, Xu J H, et al. Upper-air temperature change trends above arid region of Northwest China during 1960-2009. Theor Appl Climatol, 2015, 120: 239-248. [27] 柏玲, 陈忠升, 王祖静, 等. 1980~2013年新疆高空大气温度变化特征. 地理科学, 2016, 36(3): 458-465. https://www.cnki.com.cn/Article/CJFDTOTAL-DLKX201603018.htmBai L, Chen Z S, Wang Z J, et al. Upper-air temperature change of Xinjiang during 1980-2013. Scientia Geographica Sinica, 2016, 36(3): 458-465. https://www.cnki.com.cn/Article/CJFDTOTAL-DLKX201603018.htm [28] 翁笃鸣, 孙治安. 我国山地气温直减率的初步研究. 地理研究, 1984, 3(2): 24-34. https://www.cnki.com.cn/Article/CJFDTOTAL-DLYJ198402002.htmWeng D M, Sun Z A. A preliminary study of the lapse rate of surface air temperature over mountainous regions of China. Geographical Research, 1984, 3(2): 24-34. https://www.cnki.com.cn/Article/CJFDTOTAL-DLYJ198402002.htm [29] Pepin N, Benham D, Taylor K. Modeling lapse rates in the maritime uplands of Northern England: Implications for climate change. Arctic, Antarctic, and Alpine Research, 1999, 31(2): 151-164. [30] Burges J C. A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc, 1998, 2(2): 121-167. [31] Friedman J H. Greedy function approximation : A gradient boosting machine. The Annals of Statistics, 2001, 29(5): 1189-1232. -
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