基于偏最小二乘回归的区域换式风速预报订正技术研究
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摘要
利用偏最小二乘回归技术,将预报产品作为自变量,相应的自动气象站观测资料作为因变量,对2013—2016年冬季浙江省中尺度区域模式预报近地面风速进行订正和评估。所选956站中多数站点风速订正后有所改善,通过定量分析可知浙江西部地区整体改善效果最好,其中效果明显站点占91. 7%;中部地区改善效果明显站点占86. 5%;东部沿海地区改善效果略差,明显改善的站点占67%。各地级市整体表现均不错,除舟山地区为49.9%外,其他地区风速改善比例均超过50%。选取2017年1月20日浙江东北地区沿海大风过程分析发现订正后的风速与观测风速更为接近,在定海大岛站点(靠里)中表现尤为明显,订正后的结果具有显著参考价值。
Based on the partial least square regression, the near-surface wind speed predicted by Zhejiang WRF ADAS Real Time Modeling System in winters of 2013 — 2016 is corrected and evaluated. The forecast product is used as the independent variable while the corresponding observational data of the automatic weather stations are used as the dependent variable. At most of the 956 stations, the correction of wind speed has been improved. Through quantitative analysis, we find that the improvement effect is best for the stations in the western region of Zhejiang, up to 91. 7%. The improved stations in the central part account for 86. 5%. The improvement of the stations in the eastern coastal region is slightly worse, with 67% obviously improved stations. The improvement percentage of wind speed correction, indicates the station performance of each city is good. Except for Zhoushan(49. 9%), the rate of wind speed improvement is more than 50% in other cities. Based on the analysis of the gale process in the northeastern coast Zhejiang on 20 January 2017, the corrected wind speed is closer to the observation, especially at the nearshore stations.
Based on the partial least square regression, the near-surface wind speed predicted by Zhejiang WRF ADAS Real Time Modeling System in winters of 2013 — 2016 is corrected and evaluated. The forecast product is used as the independent variable while the corresponding observational data of the automatic weather stations are used as the dependent variable. At most of the 956 stations, the correction of wind speed has been improved. Through quantitative analysis, we find that the improvement effect is best for the stations in the western region of Zhejiang, up to 91. 7%. The improved stations in the central part account for 86. 5%. The improvement of the stations in the eastern coastal region is slightly worse, with 67% obviously improved stations. The improvement percentage of wind speed correction, indicates the station performance of each city is good. Except for Zhoushan(49. 9%), the rate of wind speed improvement is more than 50% in other cities. Based on the analysis of the gale process in the northeastern coast Zhejiang on 20 January 2017, the corrected wind speed is closer to the observation, especially at the nearshore stations.
引文
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Mabel M C, Fernandez E,2008. Analysis of wind power generation and prediction using ANN:a case study[J]. Renew Energy, 33(5):986-992.
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胡美娟,周年兴,李在军,等,2015.南京市三维生态足迹测算及驱动因子[J].地理与地理信息科学,31(1):91-95. Hu M J,Zhou N X,Li Z J,et al,2015. Calculation and driving factor analysis of three-dimensional ecological footprint in Nanjing City[J].Geogra Geo-Informa Sci,31(1):91-95(in Chinese).
李刚,熊婵,赵丽英,等,2012.基于多维漫反射光谱技术的复杂混合溶液成分检测[J].光谱学与光谱分析,32(2):491-495. Li G,Xiong C,Zhao L Y,et al,2012. Component analysis of complex mixed solution based on multidimensional diffuse reflectance spectroscopy[J]. Spectroscopy and Spectral Analysis,32(2):491-495(in Chinese).
柳艳香,陶树旺,张秀芝,2008.风能预报方法研究进展[J].气候变化研究进展,4(4):209-214. Liu Y X,Tao S W,Zhang X Z,2008.Review on methods of wind power forecasting[J]. Adv Climate Change Res,4(4):209-214(in Chinese).
舒守娟,王元,熊安元,2007.中国区域地理、地形因子对降水分布影响的估算和分析[J].地球物理学报,50(6):1703-1712. Shu S J,Wang Y, Xiong A Y, 2007. Estimation and analysis for geographic and orographic influences on precipitation distribution inChina[J]. Chin J Geophy,50(6):1703-1712(in Chinese).
宋金杰,王元,陈佩燕,等,2011.基于偏最小二乘回归理论的西北太平洋热带气旋强度统计预报方法[J].气象学报,69(5):745-756. Song J J,Wang Y,Chen P Y,et al,2011. A statistical prediction scheme of tropical cyclone intensity over the western North Pacific based on the partial square regression[J]. Acta Meteor Sin,69(5):745-756(in Chinese).
王丙兰,宋丽莉,袁春红,等,2017.河南电网输电线路舞动的气象要素指标研究[J].气象,43(1):108-114. Wang B L,Song L L,Yuan C H,et al,2017. Study of meteorological indices for transmission line galloping in Henan power grid[J]. Meteor Mon,43(1):108-114(in Chinese).
姚日升,涂小萍,蒋璐璐,等,2016.浙江近海冬季大风风速推算和ASCAT风速订正方法探讨[J].气象,42(5):621-627. Yao R S,Tu X P,Jiang L L,et al,2016. Analysis on methods about the winter offshore gale speed estimation and ASCAT wind correction in Zhejiang[J]. Meteor Mon,42(5):621-627(in Chinese).
余江,江志红,俞卫,等,2015.风电场风速数值预报的误差分析及订正[J].气象科学,35(5):587-592. Yu J,Jiang Z H,Yu W,et al,2015. Error analysis and correction of wind speed numerical forecast at wind farm[J]. J Meteor Sci,35(5):587-592(in Chinese).
张颖超,肖寅,邓华,2016.基于ELM的风电场短期风速订正技术研究[J].气象,42(4):466-471. Zhang Y C,Xiao Y,Deng H,2016.Modification technology research of short-term wind speed in wind farm based on ELM method[J]. Meteor Mon,42(4):466-471(in Chinese).
朱智慧,黄宁立,2012.上海沿海WRF模式风速预报的检验和释用[J].大气科学研究与应用,(2):69-76. Zhu Z H,Huang N L,2012. Verification and application of wind speed forecast in Shanghai coastal area by the WRF Model[J]. Atmos Sci Res Appl,(2):69-76(in Chinese).
Alexiadis M C, Fotopoulos P S, Sahsamanoglou H S, et al, 1998.Short-term forecasting of wind speed and related electrical power[J]. Solar Energy,63(1):61-68.
Barbounis T G,Theoharis J B, Alexiadis M C,et al,2006. Long-term wind speed and power forecasting using local recurrent neural network models[J]. IEEE Trans Energy Conv,21(1):273-284.
Bossanyi E A, 1985. Short-term wind prediction using Kalman filters[J].Wind Eng,9(1):1-8.
Box G E P, Jenkins G M,Reinsel G C, 1976. Time Series Analysis:Forecasting and Control[M]. New York:John Wiley and Sons.
Mabel M C, Fernandez E,2008. Analysis of wind power generation and prediction using ANN:a case study[J]. Renew Energy, 33(5):986-992.
Wold H,1966. Estimation of principal components and related models by iterative least squares[M]//Krishnaiaah P R. Multivariate Analysi. New York:Academic Press:391-420.
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