利用WRF-3DVAR同化系统和Evenson提出的方法在分析时刻添加扰动形成扰动型初始场,并在此基础上同化常规观测资料(包括固定站点的探空观测和常规地面观测),通过比较WR-F3DVAR系统对整层大气的初始(分析)场及预报场的改进,检验了同化常规观测资料后WRF模式对研究区域内近地层风速的预报效果。结果表明,同化常规观测资料对初始场有改进,且对低层大气风场和温度场的改进较FNL资料明显;GFS同化常规观测资料后生成的初始场和FNL资料提供的初始场对风速和温度的预报在不同气压层存在不同的预报时效,低层(1000 hPa)风速和温度预报在前36 h改善明显,而在较高的500 hPa和200 hPa上风速预报在前36 h改进明显,温度预报则只在前12 h得到了改善;无论是采用FNL资料还是同化常规观测资料作为初始场,对近地层风速预报均在前36 h有改进,表明对于近地面风速这种短时间内变化较大的变量,同化与之时间和空间上较为接近的资料可能改善其预报效果。
Using WR-F3DVAR assimilation system, add the perturbations with the method advanced by Evenson at initial time to generate disturbance type initial fields and assimilate conventional observation data (including radiosonde observation and conventional ground observation), comparing the improvement effects of the whole atmosphere for initial and forecast fields with WR-F3DVAR system, the forecast effects of the local atmosphere for near-surface wind was also verified. The results show that: Compared to the GFS forecast directly, assimilation of conventional data improves initial fields, especially for wind and temperature fields in lower layer compared with the initial fields produced by FNL data; The initial fields produced by FNL data and assimilation of conventional data have the different valid forecast time at different pressure levels, the forecast effects of wind and temperature are improved within 36 h at lower pressure level (1000 hPa), on 500 hPa and 200 hPa, the valid forecast time of wind and temperature is 36 h and 12 h respectively; No matter FNL data or assimilation of conventional data, the forecast improvements of near-surface layer wind are only within 36 h, this implies that the forecasting effects of variables as wind, which changes greatly in short time, may improve remarkably if assimilating data that close to variables itself in spatial and temporal.
[1]王澄海, 刘纯. 我国风电建设中风资源评估存在的问题和应对措施[J]. 中国电机工程学报, 2011, 31(增刊): 242-245.
[2]Seo J W, Chang Y S. Characteristics of the monthly mean sea surface winds and wind waves near the Korean marginal seas in the 2002 year computed using MM5/KMA and WAVEWATHC-III model[J]. Journal of the Korean Society of Oceanography, 2003, 8(3): 262-273.
[3]Kim Y K, Jeon J H. Improvement in the simulation of sea surface wind over the complex coastal area using WRF model[J]. Journal of Korea Society for Atmospheric Environment, 2006, 22(3): 309-323.
[4]Storm B, Dudhia J, Basu S, et al. Evaluation of the weather research and forecasting model on forecasting low-level jets: Implications for Wind Energy[J]. Wind Energy, 2009, 12: 81-90.
[5]王澄海, 胡菊, 靳双龙, 等. 中尺度WRF模式在西北西部地区低层风场模拟中的应用和检验[J]. 干旱气象, 2011, 29(2): 161-167.
[6]辛渝, 汤剑平, 赵逸舟, 等. 模式不同分辨率对新疆达坂-小草湖风区地面风场模拟结果的分析[J]. 高原气象, 2010, 29(4): 884-893.
[7]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.
[8]程兴宏, 陶树旺, 魏磊, 等. 基于WRF模式和自适应偏最小二乘回归法的风能预报试验研究[J]. 高原气象, 2012, 31(5): 1461-1469.
[9]王兴, 马鹏里, 张铁军, 等. MM5模式及CALMET模型对甘肃酒泉地区风能资源的数值模拟[J]. 高原气象, 2012, 31(2): 428-435.
[10]惠小英, 高晓清, 桂俊祥, 等. 酒泉风电基地高分辨率风能资源的数值模拟[J]. 高原气象, 2011, 30(2): 538-544.
[11]穆海振, 徐家良, 杨永辉. 数值模拟在上海海上风能资源评估中的应用[J]. 高原气象, 2008, 27(增刊): 196-202.
[12]Arya S P. Introduction to Micrometeorology[M]. Orlando: Academic Press, 1998: 67-75.
[13]Zhang D L, Zheng W Z. Diurnal cycles of surface winds and temperature as simulated by five boundary layer parameterizations[J]. J Appl Meteor, 2004, 4(3): 157-169.
[14]Wang C H, Jin S L, Hu J, et al. Comparing Different Boundary Layer Schemes of WRF by Simulation the Low-Level Wind over Complex Terrain[C]. The International Workshop on Environment Management and Energy Economic (EMEE, 2011), 2011, 8(PartI): 8-10, doi: 10.1109/AIMSEC.2011.6009632.
[15]王澄海, 靳双龙, 杨世莉. 新疆"2. 28"大风过程中热、 动力作用的模拟分析[J]. 中国沙漠, 2011, 31(2): 511-516.
[16]Kalnay E. Atmospheric Modeling, Data Assimilation and Predictability[M]. London: Press Syndicate of the University of Cambridge, 2003.
[17]Stauffer R D, Seaman N L. Use of four-dimensional data assimilation in a limited-area mesoscale model Part II: Effect of data assimilation within the planetary boundary layer[J]. Mon Wea Rev, 1991, 119: 734-754.
[18]Liu Y B, Warner T, Liu Y W, et al. Simultaneous nested modeling from the synoptic scale to the LES scale for wind energy applications[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2011, doi:10.1016/j.jweia.2011.01.013.
[19]Liu Y B, Warner T T, Bowers J F, et al. The operational mesogamma-scale analysis and forecast system of the U.S. Army Test and Evaluation Command. Part I: overview of the modeling system, the forecast products, and how the products are used[J]. J Appl Meteor Climate, 2008, 47: 1077-1092.
[20]Haupt S E, Wiener G, Liu Y B, et al. A Wind Power Forecasting System to Optimize Power Integration[C]. ASME 2011 5th International Conference on Energy Sustainability collocated with ASME 2011 9th International Conference on Fuel Cell Science, Engineering and Technology (ES2011), 2011, 2215-2222.
[21]张利红, 蒋丽娟, 陈朝平, 等. 探空观测资料在西南暴雨中的同化试验[J]. 高原山地气象研究, 2009, 29(3): 31-38.
[22]Barker D M, Huang W, Guo Y R, et al. A three dimensional variational data assimilation system for MM5: Implementation and initial results[J]. Mon Wea Rev, 2004, 132: 897-914.
[23]Skamarock W C, Klemp J B, Dudhia J, et al. A Description of the Advanced Research WRF Version 3[M]. Boulder: NCAR/ TN-475+STR.125, 2008.
[24]Ruggiero F H, Sashegyi K D, Madala R V, et al. The use of surface observations in four-dimensional data assimilation using a mesoscale model[J]. Mon Wea Rev, 1996, 124(5): 1018-1033.
[25]Evensen G. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics[J]. J Geophys Res, 1994, 99: 10143-10162.
[26]Parrish D F, Derber J C. The National Meteorological Center's spectral statistical interpolation analysis system[J]. Mon Wea Rev, 1992, 120: 1747-1763.
[27]Taylor K E. Summarizing multiple aspects of model performance in a single diagram[J]. J Geophys Res, 2001, 106: 7183-7192.