For numerical simulation and character of wind field with high resolution in complex terrain, mesoscale meteorological model WRF with data assimilation technology of nudging was used to realize numerical simulation of wind field with 1 km horizontal resolution in Hami area where terrain is very complex. By comparison with mast observation data, the simulation result after assimilation was close to observation data, the absolute error of wind speed simulation results decrease 0.25 m·s-1. The simulation results corrected the problem that overestimate wind speed in breeze condition and underestimated in high wind condition. And simulation results of wind profile was also more consistent with observations. Through analysis of simulation results of wind field in Hami complex terrain by 1 km horizontal resolution with 10 min interval output, some conclusions were draw as the followings:(1) As terrain is very complex in Hami area, the wind speed distribution is very different. In April, the high wind area is mainly distributed in the north area of mountain and the pass area that is south of mountain in west region. While in July, the high wind area is located in the south area of pass in west region and north region. (2) Wind shear was negative in breeze condition under complex terrain, and the smaller the wind speed, the larger the negative shear value, and negative shear value was higher in more complex terrain. While in high wind condition, the vertical shear was positive even in complex terrain, but the positive value was less than that in flat area. And the positive value was greater with wind speed increasing in flat area. (3) The temporal and spatial distribution of the proportion in full-load wind condition was very different when wind speed is between 12 and 25 m·s-1 as terrain is complex in Hami area. The wind speed was high in April, the proportion of full-load wind speed was more than 20% in most areas, especially in north area and pass area in west region, the proportion was even more than 50%, the average wind speed of full-load at 80 m was higher 0.60~0.80 m·s-1 than that at 60 m, and the difference was larger than that of monthly average wind speed. (4) The variation of wind direction with 10 min interval was not obvious in high wind condition, while in breeze condition, the variation was bigger. The variation value was bigger in flat area than that in complex area. (5) The vertical variation of wind direction was more consistent with wind speed. The smaller the wind speed, the greater the vertical variation of wind direction, and terrain will be more complex, the vertical variation will be greater.
[1]Bullock O R, Kiran A, Jerold A H, et al, 2014. An observation-based investigation of nudging in WRF for downscaling surface climate information to 12-km grid spacing[J]. J Appl Meteor Climate, 53:20-33. DOI:10.1175/JAMC-D-13-030.1.
[2]Gryning S E, Batchvarova E, Floors R, 2013. A study on the effect of nudging on long-term boundary layer profiles of wind and Weibull distribution parameters in a rural coastal area[J]. J Appl Meteor Climate, 52:1201-1207. DOI:10.1175/JAMC-D-12-0319.1.
[3]Jimenez P A, Dudhia J, 2012. Improving the representation of resolved and unresolved topographic effects on surface wind in the WRF model[J]. J Appl Meteor Climate, 51:300-316. DOI:10.1175/JAMC-D-11-084.1.
[4]Jimenez P A, Dudhia J, 2013. On the ability of the WRF model to reproduce the surface wind direction over complex terrain[J]. J Appl Meteor Climate, 52:1610-1617. DOI:10.1175/JAMC-D-12-0266.1.
[5]Li J, Tang J P, Fang J, 2015. High-resolution numerical simulation of typhoon Longwang(2005) with spectrum nudging technique[J]. J Trop Meteor, 121(4):311-325. DOI:10.16555/j. 1006-8775.2015.04.001.
[6]Liu Y B, Chen F, Warner T, et al, 2006. Verification of a mesoscale data-assimilation and forecasting system for the Oklahoma city area during the joint urban 2003 field project[J]. J Appl Meteor Climate, 45:912-929. DOI:10.1175/JAM2383.1.
[7]Lo C F, Yang Z L, Roger A P, 2008. Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) model[J]. J Geophys Res, 113:D09112.
[8]Stuffer D R, Searnan N L, 1990. Use of the four-dimensional data assimilation in a limited-area mesoscale model Part Ⅰ:Experiment with synoptic-scale data[J]. Mon Wea Rev, 118:1250-1277.
[9]Zhang H L, Pu Z X, Zhang X B, 2013. Examination of errors in near-surface temperature and wind from WRF numerical simulations in regions of complex terrain[J]. Wea Forecasting, 28(3):893-914. DOI:10.1175/WAF-D-12-00109.1.
[10]Dong B J, Li J, Sun J H, et al, 2016. Vertical structure and variation characteristics of wind field in low-level atmosphere in the southeastern margin of Qinghai-Xizang Plateau[J]. Plateau Meteor, 35(3):597-607. DOI:10.7522/j.issn. 1000-0534.2015.00041.<br/>董保举, 李建, 孙绩华, 等, 2016.青藏高原东南缘大理低层风场垂直结构与变化特征[J].高原气象, 35(3):597-607.
[11]Gong Q, Yuan G E, Zhang Y Q, et al, 2006. Application of MM5 model in wind energy resources survey[J]. Res Sci, 28(1):145-150.<br/>龚强, 袁国恩, 张云秋, 等, 2006. MM5模式在风能资源普查中的应用试验[J].资源科学, 28(1):145-150.
[12]Huang J H, Li J N, Wei X L, et al, 2006. Assimilation of QuickScat data and its impact on prediction of typhoon Vongfong (2002)[J]. Acta Scientiarum Naturalium Univeritatis Sunyatseni, 45(4):116-120.<br/>黄嘉宏, 李江南, 魏晓琳, 等, 2006.同化Quickscat资料对台风Vongfong(2002)数值模拟的影响[J].中山大学学报(自然科学版), 45(4):116-120.
[13]Li F, Zhou H, Zhou L B, et al, 2017. Study of boundary layer parameterization schemes' applicability of WRF model over complex underlying surfaces in Southeast Tibet[J]. Plateau Meteor, 36(2):340-357. DOI:10.7522/j.issn. 1000-0534.2016.00041.<br/>李斐, 邹捍, 周立波, 等, 2017. WRF模式中边界层参数化方案在藏东南复杂下垫面适用性研究[J].高原气象, 36(2):340-357.
[14]Li X X, Huang T, Wang X, et al, 2017. Analysis of characters of wind field in surface layer in Lanzhou New District[J]. Plateau Meteor, 36(4):1001-1009. DOI:10.7522/j.issn. 1000-0534.2016.00092.<br/>李晓霞, 黄涛, 王兴, 等, 2017.兰州新区近地层风场时空特征分析[J].高原气象, 36(4):1001-1009.
[15]Li Y, Wang Y, 2007. Numerical replication for wind energy over island-like complex terrain with very high resolution[J]. Acta Energiae Solaris Sinica, 28(6):663-669.<br/>李艳, 王元, 2007.岛屿型复杂地形地貌条件下有效风能分布的甚高分辨率数值模拟[J].太阳能学报, 28(6):663-669.
[16]Sun X J, Li Y, Zhang Y H, et al, 2017. Near-surface wind simulation over acrid lakeshore area and sensitivity studies using the WRF-LES[J]. Plateau Meteor, 36(3):853-844. DOI:10.7522/j.issn. 1000-0534.2016.00058.<br/>孙学金, 李岩, 张燕鸿, 等, 2017.基于WRF-LES的干旱湖区近地面风场模拟与敏感性研究[J].高原气象, 36(3):853-844.
[17]Wang L X, Wang Y, La X L, et al, 2017. Study on the simulation of boundary layer height in Lanzhou in winter using WRF model with different boundary layer parameterization schemes[J]. Plateau Meteor, 36(1):162-171. DOI:10.7522/j.issn. 1000-0534.2016.00011.<br/>王丽霞, 王颖, 赖锡柳, 等, 2017. WRF模式不同边界层参数化方案模拟兰州冬季边界层高度的研究[J].高原气象, 36(1):162-171.
[18]Wang T T, Gao X Q, Yin X Z, et al, 2016. Effects of parameterized physical processes of WRF model on simulation of rainstorm in Jiuquan, Gansu Province[J]. Plateau Meteor, 35(5):1257-1269. DOI:10.7522/j.issn. 1000-0534.2015.00096.<br/>王田田, 高晓清, 尹宪志, 等, 2016. WRF模式物理过程参数化方案对酒泉地区暴雨模拟的影响[J].高原气象, 35(5):1257-1269.
[19]Xin Y, Tang J P, Zhao Y Z, et al, 2010. Simulation of surface wind using MM5 model with different resolutions-A case study of Dabancheng and Xiaocaohu wind farms from April to September 2006[J]. Plateau Meteor, 29(4):884-893.<br/>辛渝, 汤剑平, 赵逸舟, 等, 2010.模式不同分辨率对新疆达坂城-小草湖风区地面风场模拟结果的分析[J].高原气象, 29(4):884-893.
[20]Zhang A Z, Qi L L, Ji F, et al, 2005. Advancement in data assimilation method research[J]. Meteor Sci Technol, 33(5):385-393.<br/>张爱忠, 齐琳琳, 纪飞, 等, 2005.资料同化方法研究进展[J].气象科技, 33(5):385-393.
[21]Zhang X P, Yin Y, 2013. Evaluation of the four PBL schemes in WRF model over complex topographic areas[J]. Trans Atmos Sci, 36(1):68-76.<br/>张小培, 银燕, 2013.复杂地形地区WRF模式四种边界层参数化方案的评估[J].大气科学学报, 36(1):68-76.
[22]Zhou R W, He X F, Zhu R, 2010a. Application of MM5/Calmet model system in wind energy resource assessment[J]. J Natural Res, 25(12):2101-2113.<br/>周荣卫, 何晓凤, 朱蓉, 2010a. MM5/Calmet模式系统在风能资源评估中的应用[J].自然资源学报, 25(12):2101-2113.
[23]Zhou R W, He X F, Zhu R, et al, 2010b. Numerical simulation of the development potential of wind energy resources over China's offshore areas[J]. Res Sci, 32(8):1434-1443.<br/>周荣卫, 何晓凤, 朱蓉, 等, 2010b.中国近海风能资源开发潜力数值模拟[J].资源科学, 32(8):1434-1443.
