基于数值模式的月尺度近地层气象要素预报技术研究
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摘要
利用WRF模式对美国NCEP发布的CFS气候预测业务产品在中国区域内进行动力降尺度预报,可得到预报时效为45天的逐6小时、30 km分辨率基础气象要素预测产品。再利用全国气象站观测资料和3个风电场70 m高度风速、温度观测资料对2015年冬季预测结果进行检验评估和分析,最后通过线性方法对地面要素预测结果和70 m高度风速、温度预测结果进行统计订正。结果表明:(1)2 m温度和相对湿度的全国预报平均绝对误差分别为4.71℃和18.81%,在华东、华中和华南地区误差较小;(2)10 m风速预报平均绝对误差为2.42 m/s,在东北、华北和西北地区误差较小;(3)线性订正后,2 m气温、相对湿度和10 m风速的预报绝对误差分别减小1.05℃、5.29%和1.47 m/s,并且订正后误差随时间变化更平稳;(4)订正后70 m高度风速和温度的预报绝对误差均减小,风速平均误差减小最大可达1.29 m/s(B塔),气温平均绝对误差减小最大可达3℃(C塔)。研究结果表明,基于CFS产品和WRF模式的、与月尺度风电预报关系密切的气象要素预报性能较好,未来可将该方法尝试于风电场的月尺度功率预测产品研发。
A non-hydrostatic Weather Research and Forecasting model(WRF) was used toconduct a downscaling monthly forecast based on the products of Climate Forecast System(CFS), made operational at NCEP for the winter from 1 December of 2014 to 28 February of 2015, for continental China to generate6-hourly predictionsof meteorological elements witha horizontal resolution of 30 ×30 kilometersfor a valid period of 45 days. The downscaled temperature and relative humidity, both at the 2m height, and wind speed at the 10 m height were validated against 2820 national meteorological stations from China Meteorological Administration, and temperature and wind speed at the 70 m height were validated against 3wind masts in Northern China, Southern China and Southwestern China. A linear approach was conducted to all forecasts to correct downscaling bias. The results are shown as follows:(1) The absolute errors of temperature and relative humidity at the 2m height, which was 4.71 ℃ and 18.81%, respectively, were smaller over Eastern China, Middle China and Southern China.(2) The absolute errors of wind speed at the2 m height was 2.42 m/s and smaller over Northeastern China, Northern China and Northwestern China.(3)The linear correction reduced the temperature at the 2m height from WRF downscaling forecasts by 1.05 ℃and relative humidity at the 2m height by 5.29% and wind speed at the 10 m height by 1.47 m/s. Besides,statistical correction smoothed forecast bias of temperature, relative humidity and wind speed over the 45 days.(4) The linear corrected wind speed and temperature bias at the 70 m height decreased wind speed by1.29 m/s at thewind mast of site B and temperature by 3 ℃ at the wind mast of site C. This paper presents a promising case for monthly forecast in wind power by using RCM downscaling nested in the CGCM.
A non-hydrostatic Weather Research and Forecasting model(WRF) was used toconduct a downscaling monthly forecast based on the products of Climate Forecast System(CFS), made operational at NCEP for the winter from 1 December of 2014 to 28 February of 2015, for continental China to generate6-hourly predictionsof meteorological elements witha horizontal resolution of 30 ×30 kilometersfor a valid period of 45 days. The downscaled temperature and relative humidity, both at the 2m height, and wind speed at the 10 m height were validated against 2820 national meteorological stations from China Meteorological Administration, and temperature and wind speed at the 70 m height were validated against 3wind masts in Northern China, Southern China and Southwestern China. A linear approach was conducted to all forecasts to correct downscaling bias. The results are shown as follows:(1) The absolute errors of temperature and relative humidity at the 2m height, which was 4.71 ℃ and 18.81%, respectively, were smaller over Eastern China, Middle China and Southern China.(2) The absolute errors of wind speed at the2 m height was 2.42 m/s and smaller over Northeastern China, Northern China and Northwestern China.(3)The linear correction reduced the temperature at the 2m height from WRF downscaling forecasts by 1.05 ℃and relative humidity at the 2m height by 5.29% and wind speed at the 10 m height by 1.47 m/s. Besides,statistical correction smoothed forecast bias of temperature, relative humidity and wind speed over the 45 days.(4) The linear corrected wind speed and temperature bias at the 70 m height decreased wind speed by1.29 m/s at thewind mast of site B and temperature by 3 ℃ at the wind mast of site C. This paper presents a promising case for monthly forecast in wind power by using RCM downscaling nested in the CGCM.
引文
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[2]常蕊,朱蓉,柳艳香,等.基于均生函数的风电场风速短临预报模型[J].气象,2013,39(2):226-233.
[3]孙川永.风电场风电功率短期预报技术研究[D].兰州:兰州大学,2009:5-9.
[4]刘永前,韩爽,胡永生.风电场出力短期预报研究综述[J].现代电力,2007,24(5):6-11.
[5]韩爽.风电场功率短期预测方法研究[D].北京:华北电力大学,2008:3-4.
[6]孟祥星,田成微,冬雷,等.灰色理论用于风力发电容量中长期预测的研究[J].电力系统保护与控制,2011,39(21):81-85.
[7]田武文,吴素良,王娜.月尺度降水的客观预测方法研究[J].高原气象,2010,29(4):1 072-1 077.
[8]王慧娟,吴洪星,仵建勋.降尺度方法在月预报中的应用研究[J].气象水文海洋仪器,2011(1):27-31.
[9]王冀,宋瑞艳,郭文利.统计降尺度方法在北京月尺度预测中的应用[J].气象,2011,37(6):693-700.
[10]金荣花,马杰,毕宝贵.10~30d延伸期预报研究进展和业务现状[J].沙漠与绿洲气象,2010,4(2):1-5.
[11]康志明,鲍媛媛,周宁芳.我国中期和延伸期预报业务现状以及发展趋势[J].气象科技进展,2013,3(1):18-24.
[12]MURPHY J.An evaluation of statistical and dynamical techniques for downscaling local climate[J].J Clim,12(8):2 256-2 284.
[13]BOE J,L.TERRAY L,HABETS F,et al.Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies[J].Int J Climatol,2007,27(12):1 643-1 655.
[14]WANG X L,SWAIL V R,COX A.Dynamical versus statistical downscaling methods for ocean waveheights[J].Int J Climatol,2010,30(3):317-332.
[15]SALATHE E P,STEED R,MASS C F,et al.A high-resolution climate model for the U.S.Pacific Northwest:Mesoscale feedbacks and local responses to climate change[J].J Clim,2008,21(21):5 708-5 726.
[16]WANG W,C B,MICHAEL D,et al.ARW Users Guide V3-20130107[Z].http://www2.mmm.ucar.edu/wrf/users/docs/user_guide_V3.4/ARWUsers Guide V3.pdf,2012,5.1-5.3
[17]肖玉华,康岚,徐琳娜,等.西南区域中尺度数值模式预报性能及其与天气过程关系初探[J].气象,2013,39(10):1 258-1 264.
[18]YUAN X,LIANG X Z,WOOD E F.WRF ensemble downscaling seasonal forecasts of China winter precipitation during 1982—2008[J].Clim Dyn,2011,39(7):2 041-2 058.
[19]SAHA S,MOORTHI S,WU X R,et al.The NCEP climate forecast system version 2[J].J Clim,2014,27(6):2 185-2 208.
[20]LUO L,TANG W,LIN Z,et al.Evaluation of summer temperature and precipitation predictions from NCEP CFSv2 retrospective forecast over China[J].Clim Dyn,2013,41(7-8):2 213-2 230.
[21]LANG Y,YE A,GONG W,et al.Evaluating skill of seasonal precipitation and temperature predictions of NCEP CFSv2 forecasts over 17hydroclimatic regions in China[J].J Hydrometeorology,2014,15(4):1 546-1 559.
[22]乐群,曹俊武,林振山,等.中国月平均温度的气候噪声和潜在可预报性[J].气象学报,1999,57(5):604-612.
[23]孙逸涵,何晓凤,周荣卫.不同背景场近地层风速的预报效果检验[J].资源科学,2013,35(12):2 481-2 490.
[24]王婧,徐枝芳,范广洲,等.GRAPES_RAFS系统2m温度偏差订正方法研究[J].气象,2015,41(6):719-726.
[25]何晓凤,周荣卫,孙逸涵.3个全球模式对近地层风场预报能力的对比检验[J].高原气象,2014,33(5):1 316-1 322.