环渤海区域风能资源WRF同化模拟及特征分析

doi:10.11849/zrzyxb.2014.06.011

摘要/Abstract

摘要：

利用WRF模式及其3DVAR同化模块，结合环渤海区域风能资源观测网测风塔观测数据，设计并构建了多种3DVAR 同化方案，用来提高环渤海区域风能资源数值模拟初始场质量。以2010 年4 月12 日至13 日一次大风过程为研究对象，利用该同化方案进行了一系列WRF数值模拟对比试验。选择较优的3DVAR同化方案对环渤海区域70 m高度风能资源进行了2009 年6 月1 日至2010 年5 月31 日一整年的数值模拟。模拟结果表明，较优的3DVAR同化方案能有效地改进区域风能资源数值模拟初始场质量，主要体现在模拟值与实测值更为接近，模拟效果得到显著改善。环渤海区域70 m高度年平均风速和年平均风功率密度呈现出南小北大的分布特征，渤海中部和北部风能较大，沿垂直于海岸线向内陆方向，风能参数急剧减小。

关键词: 环渤海区域, 数值模拟, 3DVAR同化, 风能资源, WRF模式

Abstract:

From the view of improving initial conditions of wind energy resources over Bohai Sea region and using the data of wind tower network over the same region, a 3DVAR data assimilation scheme is designed based on WRF model and its 3DVAR module. By using this scheme, a series of WRF numerical experiments of a strong wind event over the Bohai Sea from 12 to 13 April, 2010 was conducted to evaluate its performance. By selecting the optimal 3DVAR assimilation scheme, one year's WRF numerical simulation of 70 m height wind energy resources over Bohai Sea region between June 1, 2009 to May 31, 2010 was conducted. The results show that the annual average wind speed between measured values and analog values can obviously decrease by using YSU PBL scheme. The optimal WRF numerical model and 3DVAR assimilation can simulate the change regulation of the 70 m height hourly average wind speed in Bohai Sea region. The absolute error and relative error of average wind speed after wind tower data assimilation are smaller than before its assimilation and simulation of hourly wind speed approach observation. The annual average wind speed difference between simulation and observation of each wind tower is no more than 1.5 m/s and wind speed difference of eight wind towers is over 1.0 m/s, meanwhile wind speed difference of more than ten wind towers are below 0.5 m/s. The relative error of annual average wind speed between simulation and observation of all wind towers is 11.3%, of which 12 wind towers'are below 10.0% and three wind towers' are over 20.0%. The 70 m height annual average wind speed and wind power density over Bohai Sea region are showed small in southern part and large in the northern and the wind energy resources of central and northern Bohai Sea are higher than others. The wind speed trends is smaller in Bohai Sea but the inland wind speed affected by terrain changes dramatically. Distribution of the wind energy parameter is obviously affected by coastline and it decreases sharply along inland direction.

Key words: numerical modeling, 3DVAR assimilation, wind energy resources, WRF model, Bohai Sea region

中图分类号:

TK81

董旭光, 孟祥新, 徐洪雄, 陈艳春, 刘焕彬. 环渤海区域风能资源WRF同化模拟及特征分析[J]. 自然资源学报, 2014, 29(6): 1029-1042.

DONG Xu-guang, MENG Xiang-xin, XU Hong-xiong, CHEN Yan-chun, LIU Huan-bin. WRF 3DVAR Data Assimilation Numerical Simulation and Analysis ofWind Energy Resources over Bohai Sea Region[J]. JOURNAL OF NATURAL RESOURCES, 2014, 29(6): 1029-1042.

参考文献

[1] 周荣卫, 何晓凤, 朱蓉.MM5/CALMET 模式系统在风能资源评估中的应用[J].自然资源学报, 2010, 25(12): 2101-2113.
[2] 穆振海, 徐家良, 柯晓新, 等.高分辨率数值模式在风能资源评估中的应用初探[J].应用气象学报, 2006, 17(2): 152-159.
[3] 何晓凤, 周荣卫, 朱蓉.MM5 与CFD软件相结合对复杂地形风资源模拟初探——以鄱阳湖地区为例[J].资源科学, 2010, 32(4): 650-655.
[4] 孟昭翰, 徐焕, 杜慧珠.中国东南沿海风能资源评价[J].自然资源学报, 1991, 6(1): 1-11.
[5] 薛桁, 朱瑞兆, 杨振斌, 等.中国风能资源贮量估算[J].太阳能学报, 2001, 22(2): 167-170.
[6] 李艳, 王元, 汤剑平.中国近地层风能资源的时空变化特征[J].南京大学学报: 自然科学, 2007, 43(3): 280-290.
[7] Archer C L, Jacobson M Z.Evaluation of global wind power[J].Journal of Geophysical Research, 2005, 110, D12110, doi:10.1029/2004JD005462.
[8] Troen I, Erik L P.EuropeanWind Atlas[M].Roskilde, Denmark: Risoe National Laboratory, 1989.
[9] 李晓燕, 余志.基于MM5 的沿海风资源数值模拟方法研究[J].太阳能学报, 2005, 26(3): 400-408.
[10] 龚强, 袁国恩, 张云秋, 等.MM5 模式在风能资源普查中的应用试验[J].资源科学, 2006, 28(1): 145-150.
[11] 袁春红, 薛桁, 杨振斌.近海区域风速数值模拟试验分析[J].太阳能学报, 2004, 25(6): 740-743.
[12] Yu W, Benoit R, Girard C, et al.Wind energy simulation toolkit (WEST): A wind mapping system for use by the windenergy industry[J].Wind Eegineering, 2006, 30: 15-33.
[13] 张德, 朱蓉, 罗勇, 等.风能模拟系统WEST在中国风能数值模拟中的应用[J].高原气象, 2008, 27(1): 202-207.
[14] 邢旭煌, 朱蓉, 翟盘茂, 等.海南省及其近海风能资源的高分辨率数值模拟[J].热带海洋学报, 2009, 25(4): 421-426.
[15] 董旭光, 刘焕彬, 曹洁, 等.山东省近海区域风能资源动力降尺度研究及其开发利用[J].资源科学, 2011, 33(1): 2608-2613.
[16] 文明章, 吴滨, 林秀芳, 等.福建沿海70 米高度风能资源分布特点及评估[J].资源科学, 2011, 33(7): 1346-1352.
[17] 任永健, 刘敏, 袁业畅, 等.湖北省风能资源的高分辨率数值模拟试验[J].自然资源学报, 2012, 27(6): 1035-1043.
[18] 张鸿雁, 丁裕国, 刘敏, 等.湖北省风能资源分布的数值模拟[J].气象与环境科学, 2008, 31(2): 35-38.
[19] Brower M, Bailey B, Zack J.Applications and validations of the MesoMap wind mapping system in different climatic regimes[R].Washington D C: AmericanWind Energy Association, ProceedingsWindpower, 2001.
[20] Lee D K, Lee H H, Lee J.Tuning of 3DVAR and its application to high resolution radar data assimilation system[R].The 7th WRF users'Workshop, 2006.
[21] 盛春岩, 薛德强, 雷霆, 等.雷达资料同化与提高模式水平分辨率对短时预报影响的数值对比试验[J].气象学报, 2006, 64(3): 293-308.
[22] 高山红, 齐伊玲, 张守宝, 等.利用循环3DVAR改进黄海海雾数值模拟初始场Ⅰ: WRF数值试验[J].中国海洋大学学报, 2010, 40(10): 1-9.
[23] 李红莉, 沈桐立, 公颖.云导风资料同化在伴随模式同化系统中的应用[J].气象科技, 2006, 34(4): 358-363.
[24] 徐枝芳, 龚建东, 李泽椿.复杂地形下地面观测资料同化III.两种解决模式地形与观测站地形高度差异方法的对比分析[J].大气科学, 33(6): 1137-1147.
[25] 梁爱民, 张庆红, 刘开宇.华北地区一次大雾过程的三维变分同化试验[J].气象学报, 2007, 65(5): 792-804.
[26] 陈锋, 冀春晓, 董美莹, 等.雷达径向风速同化对台风麦莎模拟的影响[J].气象, 2012, 38 (1): 1170-1181.
[27] 沈杭锋, 翟国庆, 章元直, 等.应用云迹风资料同化的江南飑线模拟试验[J].浙江大学学报: 理学版, 2010, 37(6): 705-721.
[28] 王改利, 刘黎平, 邱崇践, 等.多普勒激光雷达风场反演方法研究[J].大气科学, 2010, 34(1): 143-153.
[29] 王遂缠, 胡向军, 张新荣, 等.雷达资料同化在甘肃局地暴雨天气个例中的应用[J].高原气象, 2011, 30(3): 711-718.
[30] 高郁东, 万齐林, 何金海.三维变分同化雷达视风速的改进方案及其数值试验[J].气象学报, 2011, 69(4): 631-643.
[31] 马艳, 杨育强, 高荣珍, 等.局地资料同化在2008 青岛奥帆赛风场预报中的应用[J].气象, 2008, 34(S1): 212-218.
[32] 王珏.地面资料同化方案设计[D].南京：南京信息工程大学, 2007: 11-12.