Vol.33, NO.2, 2022
Turn off MathJax
Article Contents
Article Navigation >  Journal of Applied Meteorological Science  > 2022  >  33(2): 129-141
Liu Yujue, Huang Qianqian, Zhang Hanbin, et al. Refined assessment of wind environment over Winter Olympic competition zone based on large eddy simulation. J Appl Meteor Sci, 2022, 33(2): 129-141. DOI:  10.11898/1001-7313.20220201.
Citation: Liu Yujue, Huang Qianqian, Zhang Hanbin, et al. Refined assessment of wind environment over Winter Olympic competition zone based on large eddy simulation. J Appl Meteor Sci, 2022, 33(2): 129-141. DOI:  10.11898/1001-7313.20220201.
shu

Refined Assessment of Wind Environment over Winter Olympic Competition Zone Based on Large Eddy Simulation

DOI: 10.11898/1001-7313.20220201

  • Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089

  • Received Date: 2021-12-03
  • Rev Recd Date: 2022-02-14
  • Publish Date: 2022-03-31
    Abstract
  • The near-surface wind field over complex terrain is highly non-uniform due to the influence of topographical fluctuations. Therefore, it is extremely difficult to carry out high-density observational experiments and refined assessment of wind environment in these areas. In this case, high-resolution wind field data from numerical simulations is essential for the evaluation and analysis of near-surface wind environment. A refined wind environment assessment is carried out for Xiaohaituo alpine skiing field, Yanqing competition zone of Beijing Winter Olympic Games. Firstly, the weather patterns over the alpine skiing field during the winter competition periods from 2009 to 2021 (every February and March) are divided into 5 main types according to the Lamb-Jenkinson (L-J) atmospheric circulation classification scheme, and further classified into 93 secondary circulation types according to the wind directions and speed at 700 hPa. Secondly, the coupled model system of mesoscale meteorology and large eddy simulation (RMAPS-LES) developed by Beijing Institute of Urban Meteorology is used to simulate the wind field at a horizontal resolution of 37 m for typical cases under 93 weather patterns. The comparison between the simulation results and observations at 11 automatic weather stations shows that the model simulation performs reasonably well. The results show that the average deviation of 2 m temperature is less than 2℃, which perfectly meets the accuracy requirements of weather forecast service demand. Although 10 m wind speed is slightly overestimated for the whole typical cases of different weather patterns, the average deviation is within 3 m·s-1, and the average deviation of 10 m wind direction simulation is 10.43°-12.36°, which shows a good prediction skill. Finally, based on the wind field simulation results of different weather patterns, a ten-year winter wind environment assessment is carried out to provide detailed spatial distribution characteristics of the wind field, risk ranges, locations of gale, and the risk probability of exceeding the wind speed thresholds of the sport events, so as to provide technical supports for the organization, time arrangement, track design and wind hazard protection of 2022 Beijing Winter Olympic Games. The method provides an effective way for wind energy assessment and forecast over complex terrain, as well as meteorological services for wind-sensitive activities, such as outdoor mega-events over complex terrain, small-scale environmental design for large-scale architectural complex over mountain terrain, mountain fire disaster prevention and mitigation, mountain pollution forecasting and nuclear proliferation assessment.
    • FullText(HTML)
    References(36)

  • Fig. 1  Computational domain for RMAPS-LES of Yanqing competition zone with the terrain elevation and state boundaries

    (a)d01-d04, (b)d04 (⊙ and ☆ denote automatic weather stations)

    DownLoad: Full-Size Img PowerPoint

    Fig. 2  Domain and 16 grid points (black dots) for L-J circulation type classification

    (the red dot denotes the center of competition zone)

    DownLoad: Full-Size Img PowerPoint

    Fig. 3  Main circulation types from Feb to Mar during 2009-2021 of Yanqing competition zone

    (the percentage denotes the proportion)

    DownLoad: Full-Size Img PowerPoint

    Fig. 4  Wind rose of 700 hPa at the center of Yanqing competition zone from Feb to Mar during 2009-2021

    DownLoad: Full-Size Img PowerPoint

    Fig. 5  Errors of 2 m temperature and 10 m wind speed, direction at automatic weather stations

    (the marker denotes the median error, the whisker denotes the standard deviation of error)
    (a)2 m temperature of 31 secondary circulation types, (b)2 m temperature of three wind speed groups, (c)10 m wind speed of 31 secondary circulation types, (d)10 m wind speed of three wind speed groups, (e)10 m wind direction of 31 secondary circulation types, (f)10 m wind direction of three wind speed groups

    DownLoad: Full-Size Img PowerPoint

    Fig. 6  Average wind speed distribution of alpine skiing field

    (the upper part of plot is a three-dimensional map in the region alpine skiing field, the bottom part shows the top view in planar, white dots denote automatic weather stations)

    DownLoad: Full-Size Img PowerPoint

    Fig. 7  Maximum wind speed distribution of alpine skiing field

    (the others same as in Fig. 6)

    DownLoad: Full-Size Img PowerPoint

    Fig. 8  Probability of wind speed exceeding the threshold of alpine skiing of all wind speed groups

    (the others same as in Fig. 6)
    (a)critical decision point 11 m·s-1, (b)significant decision point 14 m·s-1, (c)factor to consider 17 m·s-1

    DownLoad: Full-Size Img PowerPoint

    Fig. 9  Probability of wind speed exceeding the threshold of alpine skiing of small wind speed group

    (the others same as in Fig. 6)
    (a)critical decision point 11 m·s-1, (b)significant decision point 14 m·s-1, (c)factor to consider 17 m·s-1

    DownLoad: Full-Size Img PowerPoint

    Table  1  L-J circulation classification scheme

    条件 大类环流型 小类环流型
    |ξ| < V 平直气流型D DE(1)，DS(4)，DSW(6)，DWSW(11)，
    DWNW(16)，DNW(21)，DNWN(26)，DN(29)
    V≤|ξ|≤2V且ξ≥0 气旋-平直气流混合型C-h C-hE，C-hS，C-hSW(7)，C-hWSW(12)，
    C-hWNW(17)，C-hNW(22)，C-hNWN，C-hN
    V≤|ξ|≤2V且ξ < 0 反气旋-平直气流混合型A-h A-hE(2)，A-hS，A-hSW(8)，A-hWSW(13)，
    A-hWNW(18)，A-hNW(23)，A-hNWN(27)，A-hN(30)
    |ξ|>2V且ξ≥0 气旋型C CE，CS(5)，CSW(9)，CWSW(14)，
    CWNW(19)，CNW(24)，CNWN，CN
    |ξ|>2V且ξ < 0 反气旋型A AE(3)，AS，ASW(10)，AWSW(15)，
    AWNW(20)，ANW(25)，ANWN(28)，AN(31)
    注：括号内数字表示小类环流型序号。
    DownLoad: Download CSV

    Table  2  Secondary circulation types from Feb to Mar during 2009-2021 of Yanqing competition zone

    风向 大类环流型
    D C-h A-h C A
    E 13 0 6 0 6
    S 42 2 5 7 2
    SW 80 13 6 19 9
    WSW 197 45 23 54 42
    WNW 430 89 139 71 145
    NW 331 43 247 37 187
    NWN 189 5 180 5 152
    N 71 0 85 0 84
    DownLoad: Download CSV

    Table  3  Schemes used in RMAPS-LES simulations

    参数化方案 d01 d02 d03 d04
    边界层方案 YSU LES LES LES
    微物理方案 New Thompson New Thompson New Thompson New Thompson
    长、短波辐射方案 RRTMG RRTMG RRTMG RRTMG
    近地层方案 Revised MM5 Revised MM5 Revised MM5 Revised MM5
    陆面过程方案 Noah Noah Noah Noah
    积云方案 Kain-Fritsch 无 无 无
    DownLoad: Download CSV
  • [1]
    Taylor P A, Teunissen H W. The Askervein Hill Project: Overview and background data. Bound-Layer Meteor, 1987, 39(1): 15-39.
    [2]
    Bechmann A, Sørensen N N, Berg J J, et al. The Bolund Experiment, Part Ⅱ: Blind comparison of microscale flow models. Bound-Layer Meteor, 2011, 141(2): 245-271. doi:  10.1007/s10546-011-9637-x
    [3]
    Fernando H, Mann J, Palma J, et al. The perdigo: Peering into microscale details of mountain winds. Bull Amer Meteor Soc, 2018, 100(5): 799-819.
    [4]
    Li F, Ruan Z, Wang H Y, et al. A calibration method of wind profile radar echo intensity with Doppler velocity spectrum. J Appl Meteor Sci, 2021, 32(3): 315-331. doi:  10.11898/1001-7313.20210305
    [5]
    Yan J M, Zhao B K, Zhang S, et al. Observation analysis and application evaluation of wind profile radar to diagnosing the boundary layer of landing typhoon. J Appl Meteor Sci, 2021, 32(3): 332-346. doi:  10.11898/1001-7313.20210306
    [6]
    Stefano S, Bianca A, Joan C, et al. Exchange processes in the atmospheric boundary layer over mountainous terrain. Atmosphere, 2018, 9(3): 102-134. doi:  10.3390/atmos9030102
    [7]
    Jiménez P A, Kosović B. Implementation and Evaluation of a Three Dimensional PBL Parameterization for Simulations of the Flow over Complex Terrain. 17th Annual WRF Users' Workshop, 2016.
    [8]
    Zhu R, Xu D H. Multi-scale turbulent planetary boundary layer parameterization in mesoscale numerical simulation. J Appl Meteor Sci, 2004, 15(5): 543-555. doi:  10.3969/j.issn.1001-7313.2004.05.004
    [9]
    Cuxart J. When can a high-resolution simulation over complex terrain be called LES?. Earth Sci, 2015, 3(87): 1-6.
    [10]
    Xu Y, Chen Z H, Yang H Q, et al. Comparison of short-term forecast method of wind power in wind farm. J Appl Meteor Sci, 2013, 24(5): 625-630. doi:  10.3969/j.issn.1001-7313.2013.05.012
    [11]
    Xu J J, Hu F, Xiao Z N, et al. Analog bias correction of numerical model on wind power prediction. J Appl Meteor Sci, 2013, 24(6): 731-740. doi:  10.3969/j.issn.1001-7313.2013.06.010
    [12]
    Lilly D K. On the Application of the Eddy Viscosity Concept in the Inertial Sub-range of Turbulence//NCAR Manuscript No. 123. National Center for Atmospheric Research, 1966.
    [13]
    Deardorff J W. Numerical investigation of neutral and unstable planetary boundary layers. J Atmos Sci, 1972, 29: 91-115. doi:  10.1175/1520-0469(1972)029<0091:NIONAU>2.0.CO;2
    [14]
    Moeng C H, Sullivan P P. A Comparison of shear- and buoyancy-driven planetary boundary layer flows. J Atmos Sci, 1994, 51(7): 999-1022. doi:  10.1175/1520-0469(1994)051<0999:ACOSAB>2.0.CO;2
    [15]
    Chow K F, Street R L, Xue M, et al. Explicit filtering and reconstruction turbulence modeling for large-eddy simulation of neutral boundary layer flow. J Atmos Sci, 2005, 62(7): 2058-2077. doi:  10.1175/JAS3456.1
    [16]
    Mirocha J D, Lundquist J K, Kosović B. Implementation of a nonlinear subfilter turbulence stress model for large-eddy simulation in the advanced research WRF model. Mon Wea Rev, 2010, 138(11): 4212-4228. doi:  10.1175/2010MWR3286.1
    [17]
    Liu M J, Zhang X, Chen B D. Assessment of the suitability of planetary boundary layer schemes at 'grey zone' resolutions. Chinese J Atmos Sci, 2018, 42(1): 52-69. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK201801004.htm
    [18]
    Liu Y, Warner T, Liu Y, et al. Simultaneous nested modeling from the synoptic scale to the LES scale for wind energy applications. J Wind Eng Ind Aerod, 2011, 99(4): 308-319. doi:  10.1016/j.jweia.2011.01.013
    [19]
    Talbot C, Bouzeid E, Smith J. Nested mesoscale large-eddy simulations with WRF: Performance in real test cases. J Hydrometeorol, 2013, 13(5): 1421-1441. http://www.researchgate.net/profile/Elie_Bou-Zeid2/publication/235342660_Nested_Mesoscale_Large-Eddy_Simulations_with_WRF_Performance_in_Real_Test_Cases/links/00b7d520a51ffb7ec4000000.pdf
    [20]
    Rai R K, Berg L K, Kosović B, et al. Comparison of measured and numerically simulated turbulence statistics in a convective boundary layer over complex terrain. Bound-Layer Meteor, 2016, 163(1): 1-21.
    [21]
    Zuo Q, Zhang Q H. Application of large eddy simulation for a winter radiation fog event in North China. Acta Scientiarum Naturalium Universitatis Pekinensis, 2016, 52(5): 819-828. https://www.cnki.com.cn/Article/CJFDTOTAL-BJDZ201605007.htm
    [22]
    Xue L, Chu X, Rasmussen R, et al. A case study of radar observations and WRF LES simulations of the impact of ground-based glaciogenic seeding on orographic clouds and precipitation. Part Ⅱ: AgI dispersion and seeding signals simulated by WRF. J Appl Meteor Climatol, 2014, 53(10): 2264-2286. doi:  10.1175/JAMC-D-14-0017.1
    [23]
    Gerber F, Besic N, Sharma V, et al. Spatial variability in snow precipitation and accumulation in COSMO-WRF simulations and radar estimations over complex terrain. The Cryosphere, 2018, 12(10): 3137-3160. doi:  10.5194/tc-12-3137-2018
    [24]
    Liu Y J, Liu Y B, Muoz-Esparza D, et al. Simulation of flow fields in complex terrain with WRF-LES: Sensitivity assessment of different PBL treatments. J Appl Meteor Climatol, 2020, 59: 1481-1501. doi:  10.1175/JAMC-D-19-0304.1
    [25]
    Zhang Y T, Tong H, Sun J. Application of a bias correction method to meteorological forecast for the Pyeongchang Winter Olympic Games. J Appl Meteor Sci, 2020, 31(1): 27-41. doi:  10.11898/1001-7313.20200103
    [26]
    Mailhot J, Bélair S, Charron M, et al. Environment Canada's experimental numerical weather prediction systems for the Vancouver 2010 Winter Olympic and Paralympic Games. Bull Amer Meteor Soc, 2010, 91(8): 69-82.
    [27]
    Chen M X, Quan J N, Miao S G, et al. Enhanced weather research and forecasting in support of the Beijing 2022 Winter Olympic and Paralympic Games. WMO Bull, 2018, 62(2): 58-61.
    [28]
    Whiteman C D. Mountain Meteorology: Fundamentals and Applications. New York: Oxford University Press, 2000.
    [29]
    Jia, C H, Dou J J, Miao S G, et al. Analysis of characteristics of mountain-valley winds in the complex terrain area over Yanqing-Zhangjiakou in the winter. Acta Meteor Sinica, 2019, 77(3): 475-488. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201903007.htm
    [30]
    Wei F Y. Progresses on climatological statistical diagnosis and prediction methods—In commemoration of the 50 anniversaries of CAMS establishment. J Appl Meteor Sci, 2006, 17(6): 736-742. doi:  10.3969/j.issn.1001-7313.2006.06.011
    [31]
    Jenkinson A F, Collison F P. An Initial Climatology of Gales over the North Sea//Synoptic Climatology Branch Memorandum. Bracknell. Meteorological Office, 1977: 18.
    [32]
    Hao L S, Li W J. Relationships of regional circulation patterns in North China and Hebei Province climate changes. Transactions of Atmospheric Sciences, 2009, 32(5): 618-626. doi:  10.3969/j.issn.1674-7097.2009.05.004
    [33]
    Liu Y J, Miao S G, Hu F. Refined numerical simulation of complex terrain flow field over Xiaohaituo Mountain. Plateau Meteorology, 2018, 37(5): 1388-1401. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201805021.htm
    [34]
    Smagorinsky J. General circulation experiments with the primitive equations. I. The basic experiment. Mon Wea Rev, 1963, 91: 99-152. doi:  10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2
    [35]
    Liu Y J, Miao S G, Liu L, et al. Effects of a modified sub-grid-scale terrain parameterization scheme on the simulation of low-layer wind over complex terrain. J Appl Meteor Sci, 2019, 30(1): 70-81. doi:  10.11898/1001-7313.20190107
    [36]
    Xue H L, Shen X S, Su Y. Parameterization of turbulent orographic form drag and implementation in GRAPES. J Appl Meteor Sci, 2011, 22(2): 169-181. http://qikan.camscma.cn/article/id/20110206
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中
  • -->

Catalog

    Figures(9)  / Tables(3)

    Get Citation
    PDF
    XML
    Article views (1243) PDF downloads(192) Cited by()
    • Received : 2021-12-03
    • Accepted : 2022-02-14
    • Published : 2022-03-31
    Journal Online
    Contact

    © 2020 Journal of Applied Meteorological Science   京ICP备15006967号-1

    Supported by Beijing Renhe Information Technology Co. Ltd

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return