Modification Tests for the Coefficient of Turbulent Mixing Length Scale in QNSE Scheme in the WRF Model
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摘要: 边界层参数化方案中湍流混合对数值模拟起着重要的作用，湍流混合作用的恰当描述对于温度、湿度、风场以及降水的准确模拟至关重要。我国长江中下游流域人口密集，暴雨灾害频发，很有必要寻找一种适合该地区降水模拟的边界层参数化方案。本文运用WRF（Weather Research and Forecasting）中尺度数值模式，以QNSE（Quasi-Normal Scale Elimination）边界层参数化方案为基础，将其中湍流混合长度尺度系数调整为可变参数。本文将
Noh et al.（2003） 提出的Prandtl公式与Janjić提出的修正湍流长度尺度系数的方法相结合，通过考虑非局地项的强迫、地表稳定度与边界层高度对湍流长度尺度系数的影响，强调大气的动力结构特征与热力结构特征对湍流混合的共同影响，从而提高QNSE边界层参数化方案在不同地理环境下的模拟能力。文章通过进行长江中下游地区的典型暴雨试验，将调整参数后的QNSE方案与原方案进行比较，重点分析调整参数后的方案与原方案对关键基本气象要素场、边界层结构特征以及降水的模拟能力，并将模拟结果与观测结果进行对比，结果表明：调整参数后的方案一定程度上改进了地表温度、边界层结构以及降水的模拟效果。进一步研究表明，调整参数后的方案主要通过改变边界层混合缓解水汽混合比、位温模拟方面的误差。Abstract: Appropriate description of turbulence in PBL (planetary boundary layer) is essential for numerical weather prediction and simulation. Proper treatment of turbulence may have an important influence on the simulation of meteorological fields, such as temperature, moisture, wind speed and precipitation. Heavy rainfall event is frequent in the lower reaches of the Yangtze River and it is necessary to obtain a suitable PBL scheme for this region due to its dense habitability. In this study, based on the QNSE (Quasi-Normal Scale Elimination) planetary boundary layer scheme in the WRF (Weather Research and Forecasting) Model, the coefficients of the TLS (turbulent mixing length scale) in the Mellor-Yamada formulation were modified to be varying based on the application of the Prandtl formula. The MCTLS (modified coefficients of TLS) are dependent on the PBL height and surface stability, and a nonlocal term was imposed on MCTLS, thereby emphasizing the comprehensive impacts of the atmospheric dynamic and thermal structures on turbulent mixing. WRF model simulations using the original PBL scheme and the PBL scheme with the MCTLS were conducted over the lower reaches of the Yangtze River and results were compared to measurements. Improvements in the near-surface temperature, the planetary boundary layer structure and the rainfall simulations have been found. More specifically, the simulations with the MCTLS were shown to alleviate biases in the potential temperature and water vapor mixing ratio by altering turbulent mixing. -
图 1 湍流长度尺度系数随Prandtl数的变化趋势
Figure 1. Variations of the coefficients of turbulent mixing length scale as a function of Prandtl numbe
图 2 模拟区域设置以及站点分布：(a) Lambert投影下的三重嵌套区域；(b) 华东部分地面自动站 (实心点) 和高空探空站 (空心圈)
Figure 2. (a) Model domains in the Lambert projection and (b) locations of national weather stations (NWS) (dots) and sounding stations (hollow circles) in eastern China.
图 3 调整参数化后的方案 (MCTLS试验) 与原方案模拟的 (a, b, c) 地面温度、(d, e, f) 地面温度露点差的统计检验结果：(a, d) 平均偏差；(b, e) 平均绝对偏差；(c, f) 均方根误差随积分时间 (从2009年7月23日12时到7月24日12时) 的变化情况。虚线是控制试验，实线是MCTLS试验，横坐标为积分12~36小时，纵坐标是偏差值, T2和Td分别代表地面温度与地面露点
Figure 3. Statistical tests of (a, b, c) surface temperature and (d, e, f) surface dew point depression: (a, d) Biases; (b, e) absolute biases; (c, f) RMSEs (root-mean-square errors) between mesurements and WRF simulations at national weather stations (NWS) during the 12-36 h forecast period from 1200 UTC 23 July to 1200 UTC 24 July 2009. Dashed lines indicate control run, solid lines indicate MCTLS run. Horizontal ordinate is integration time from 12 to 36 h, T2 and Td denote surface temperature and surface dew point, respectively
图 4 (a, b, c) 区域平均位温与 (d, e, f) 水汽混合比廓线：(a, d)2009年7月24日00时 (协调世界时，下同); (b, e)2009年7月24日06时; (c, f)2009年7月24日12时
Figure 4. Profiles of (a, b, c) potential temperature and (d, e, f) water vapor mixing ratio averaged over the sounding sites: (a, d) At 0000 UTC 24 July 2009; (b, e) at 0600 UTC 24 July 2009; (c, f) at 1200 UTC 24 July 2009
图 5 (a, b) 地面风速的均方根误差 (单位：m s−1) 与 (c, d) 上海嘉定站风廓线 (单位：m s−1)。(a) MCTLS试验与 (b) 原方案模拟的地面风速与实况在积分第24~36小时的均方根误差；(c)2009年7月23日09时；(d)2009年7月23日13时
Figure 5. Comparisons of root-mean-square error (RMSE) of 10-meter wind speed and PBL wind speed between observations and simulations (units: m s−1). (a) RMSE between observations and MCTLS run, (b) RMSE between observations and control run for the 24-36 h forecast period from 0000 UTC 24 July to 1200 UTC 24 July 2009, (c) wind speed profile at Jiading of Shanghai at 0900 UTC 23 July 2009, (d) wind speed profile at Jiading of Shanghai at 1300 UTC 23 July 2009
图 6 2009年7月23日12时至7月24日12时模拟的累计降水与实况 (单位：mm)：(a) 实况；(b) 控制试验；(c) MCTLS试验模拟结果
Figure 6. Observed and simulated 24-hour accumulated rainfall in the lower reaches of Yangtze River for the 12-36 h forecast period of the 23 Jul 2009 run initiated at 0000 UTC (units: mm): (a) Observations; (b) control run; (c) MCTLS run
图 7 2009年7月23日12时至24日12时降水区 (28°N~32°N, 114°E~121.5°E) 区域平均降水 (单位：mm) 的时间序列：(a) 区域平均逐3小时降水量；(b) 区域平均逐小时降水量
Figure 7. Time series of simulated and observed domain-average rainfall (units: mm) over the strong rainfall region (28°N-32°N, 114°E-121.5°E) from 1200 UTC 23 Jul to 1200 UTC 24 Jul 2009: (a) Time series of 3-hour accumulated rainfall; (b) time series of hourly rainfall
图 9 不同雨区的平均边界层高度 (单位：m)：(a) 无降水站平均边界层高度；(b) 小雨站点平均边界层高度；(c) 中雨以上站点平均边界层高度
Figure 9. Planetary boundary layer heights (units: m) in the (a) non-rainfall areas, (b) small rainfall areas, and (c) medium rainfall areas
图 10 强降水区 (28°N~32°N, 114°E~121.5°E) 平均湍流动能随时间演变情况 (单位：m2 s−2)：(a) MCTLS试验模拟的湍流动能；(b) 控制试验模拟的湍流动能
Figure 10. Temporal variations of turbulence kinetic energy (TKE, units: m2 s−2) averaged over the strong rainfall region (28°N-32°N, 114°E-121.5°E): (a) MCTLS test; (b) control test
表 1 模拟试验方案设计
Table 1. Design summary of different numerical experiments
试验 边界层参数化方案 地面层参数化方案 MCTLS试验 湍流长度尺度系数调整后的QNSE方案 QNSE方案 控制试验 QNSE方案 QNSE方案 
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[1] Ayotte K W, Sullivan P P, Andrén A, et al. 1996. An evaluation of neutral and convective planetary boundary-layer parameterizations relative to large eddy simulations[J]. Bound.-Layer Meteor., 79 (1-2):131-175, doi: 10.1007/BF00120078. [2] Bougeault P, Lacarrere P. 1989. Parameterization of orography-induced turbulence in a mesobeta-scale model[J]. Mon. Wea. Rev., 117 (8):1872-1890, doi:10.1175/1520-0493(1989)117<1872:POOITI>2.0.CO;2. [3] Bright D R, Mullen S L. 2002. The sensitivity of the numerical simulation of the southwest monsoon boundary layer to the choice of PBL turbulence parameterization in MM5[J]. Wea. Forecasting, 17 (1):99-114, doi:10.1175/1520-0434(2002)017<0099:TSOTNS>2.0.CO;2. [4] Brown A R. 1996. Evaluation of parametrization schemes for the convective boundary layer using large-eddy simulation results[J]. Bound.-Layer Meteor., 81 (2):167-200, doi: 10.1007/BF00119064. [5] Dudhia J. 1989. Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model[J]. J. Atmos. Sci., 46 (20):3077-3107, doi:10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2. [6] Dudhia J. 1996. A multi-layer soil temperature model for MM5[C]//Proceedings of the 6th PSU/NCAR Mesoscale Model Users' Workshop. Boulder, CO, PSU/NCAR, 49-50. [7] Grell G A, Dévényi D. 2002. A generalized approach to parameterizing convection combining ensemble and data assimilation techniques[J]. Geophys. Res. Lett., 29 (14):38-1-38-4, doi: 10.1029/2002GL015311. [8] Holtslag A A M, Van Meijgaard E, De Rooy W C. 1995. A comparison of boundary layer diffusion schemes in unstable conditions over land[J]. Bound.-Layer Meteor., 76 (1-2):69-95, doi: 10.1007/BF00710891. [9] Hong S Y, Dudhia J, Chen S H. 2004. A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation[J]. Mon. Wea. Rev., 132 (1):103-120, doi:10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2. [10] Hong S Y, Pan H L. 1996. Nonlocal boundary layer vertical diffusion in a medium-range forecast model[J]. Mon. Wea. Rev., 124 (10):2322-2339, doi:10.1175/1520-0493(1996)124<2322:NBLVDI>2.0.CO;2. [11] Hong S Y, Noh Y, Dudhia J. 2006. A new vertical diffusion package with an explicit treatment of entrainment processes[J]. Mon. Wea. Rev., 134 (9):2318-2341, doi: 10.1175/MWR3199.1. [12] Hu X M, Nielsen-Gammon J W, Zhang F Q. 2010a. Evaluation of three planetary boundary layer schemes in the WRF model[J]. J. Appl. Meteor. Climatol., 49 (9):1831-1844, doi: 10.1175/2010JAMC2432.1. [13] Hu X M, Zhang F Q, Nielsen-Gammon J W. 2010b. Ensemble-based simultaneous state and parameter estimation for treatment of mesoscale model error:A real-data study[J]. Geophys. Res. Lett., 37 (8):L08802, doi: 10.1029/2010GL043017. [14] Hu Y Q, Zuo H C. 2003. The influence of convergence movement on turbulent transportation in the atmospheric boundary layer[J]. Adv. Atmos. Sci., 20 (5):794-798, doi: 10.1007/BF02915404. [15] Janjić Z I. 2002. Nonsingular implementation of Mellor-Yamada level 2.5 scheme in the NCEP MESO model[R]. NCEP Office Note No. 437, 61pp. [16] Jankov I, Schultz P J, Anderson C J, et al. 2007. The impact of different physical parameterizations and their interactions on cold season QPF in the American river basin[J]. J. Hydrometeor., 8 (5):1141-1151, doi: 10.1175/JHM630.1. [17] Jung Y, Xue M, Zhang G F. 2010. Simultaneous estimation of microphysical parameters and the atmospheric state using simulated polarimetric radar data and an ensemble Kalman filter in the presence of an observation operator error[J]. Mon. Wea. Rev., 138 (2):539-562, doi: 10.1175/2009MWR2748.1. [18] Mellor G L, Yamada T. 1982. Development of a turbulence closure model for geophysical fluid problems[J]. Rev. Geophys., 20 (4):851-875, doi: 10.1029/RG020i004p00851. [19] Mlawer E J, Taubman S J, Brown P D, et al. 1997. Radiative transfer for inhomogeneous atmospheres:RRTM, a validated correlated-k model for the longwave[J]. J. Geophys. Res., 102 (D14):16663-16682, doi: 10.1029/97JD00237. [20] Monin A S, Obukhov A M. 1954. Basic laws of turbulent mixing in the atmosphere near the ground[J]. Tr Akad Nauk SSSR Geofiz Inst, 24 (151):163-187. [21] Nakanishi M. 2001. Improvement of the Mellor-Yamada turbulence closure model based on large-eddy simulation data[J]. Bound.-Layer Meteor., 99 (3):349-378, doi: 10.1023/A:1018915827400. [22] Nakanishi M, Niino H. 2004. An improved Mellor-Yamada level-3 model with condensation physics:Its design and verification[J]. Bound.-Layer Meteor., 112 (1):1-31, doi: 10.1023/B:BOUN.0000020164.04146.98. [23] Nakanishi M, Niino H. 2009. Development of an improved turbulence closure model for the atmospheric boundary layer[J]. J. Meteor. Soc. Japan, 87 (5):895-912, doi: 10.2151/jmsj.87.895. [24] Nielsen-Gammon J W, Hu X M, Zhang F Q, et al. 2010. Evaluation of planetary boundary layer scheme sensitivities for the purpose of parameter estimation[J]. Mon. Wea. Rev., 138 (9):3400-3417, doi: 10.1175/2010MWR3292.1. [25] Noh Y, Cheon W G, Hong S Y, et al. 2003. Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data[J]. Bound.-Layer Meteor., 107 (2):401-427, doi: 10.1023/A:1022146015946. [26] Pagowski M. 2004. Some comments on PBL parameterizations in WRF[C]//Proceedings of the Joint 5th WRF/14th MM5 Users' Workshop. Boulder, CO:National Center for Atmospheric Research. [27] Pagowski M. 2008. Nakanishi and Niino improved Mellor-Yamada 1.5 and 2-order closures (2004, 2006) implementation in WRF & 1D WRF PBL model as a tool for development and testing[C]//WRF Users' Workshop, PBL Group Meeting. Boulder, CO, USA. [28] Pleim J E, Chang J S. 1992. A non-local closure model for vertical mixing in the convective boundary layer[J]. Atmos. Environ., 26 (6):965-981, doi: 10.1016/0960-1686(92)90028-J. [29] Pleim J E. 2007a. A combined local and nonlocal closure model for the atmospheric boundary layer. Part Ⅰ:Model description and testing[J]. J Appl. Meteorol. Climatol., 46 (9):1383-1395, doi: 10.1175/JAM2539.1. [30] Pleim J E. 2007b. A combined local and nonlocal closure model for the atmospheric boundary layer. Part Ⅱ:Application and evaluation in a mesoscale meteorological model[J]. J. Appl. Meteor. Climatol., 46 (9):1396-1409, doi: 10.1175/JAM2534.1. [31] Priestley C H B. 1959. Turbulent Transfer in the Lower Atmosphere[M]. Chicago:The University of Chicago Press, 130pp. [32] Skamarock W C, Klemp J B, Dudhia J, et al. 2008. A description of the advanced research WRF version 3[R]. NCAR NCAR Technical Note NCAR/TN-475+STR, 113pp, doi:10.5065/D68S4MVH. [33] 斯塔尔. 1991.边界层气象学导论[M].北京:气象出版社, 738pp.Stull R B. 1991. An Introduction to Boundary Layer Meteorology[M]. Yang C X, Trans. Beijing:China Meteorological Press, 738pp. [34] Sukoriansky S. 2008. Implementation of Quasi-Normal Scale Elimination model of stably stratified turbulence in WRF[R]. Report on WRF-DTC Visit of Semion Sukoriansky-June. [35] Sukoriansky S, Galperin B. 2008. A quasi-normal scale elimination (QNSE) theory of turbulent flows with stable stratification and its application in weather forecast systems[C]//6th IASME/WSEAS International Conference on Heat Transfer, Thermal Engineering and Environment (THE'08). Rhodes, Greece, 376-380. [36] Sukoriansky S, Galperin B, Perov V. 2005. Application of a new spectral theory of stably stratified turbulence to the atmospheric boundary layer over sea ice[J]. Bound.-Layer Meteor., 117 (2):231-257, doi: 10.1007/s10546-004-6848-4. [37] Sušelj K, Sood A. 2010. Improving the Mellor-Yamada-Janjić parameterization for wind conditions in the marine planetary boundary layer[J]. Bound.-Layer Meteor., 136 (2):301-324, doi: 10.1007/s10546-010-9502-3. [38] Tong M J, Xue M. 2008a. Simultaneous estimation of microphysical parameters and atmospheric state with simulated radar data and ensemble square root Kalman filter. Part Ⅱ:Parameter estimation experiments[J]. Mon. Wea. Rev., 136 (5):1649-1668, doi: 10.1175/2007mwr2071.1. [39] Tong M J, Xue M. 2008b. Simultaneous estimation of microphysical parameters and atmospheric state with simulated radar data and ensemble square root Kalman filter. Part Ⅰ:Sensitivity analysis and parameter identifiability[J]. Mon. Wea. Rev., 136 (5):1630-1648, doi: 10.1175/2007mwr2070.1. [40] Troen I B, Mahrt L. 1986. A simple model of the atmospheric boundary layer; sensitivity to surface evaporation[J]. Boundary-Layer Meteorology, 37 (1):129-148. https://www.researchgate.net/publication/226452404_A_simple_model_of_the_atmospheric_boundary_layer_Sensitivity_to_surface_evaporation [41] Xie B, Fung J C H, Chan A, et al. 2012. Evaluation of nonlocal and local planetary boundary layer schemes in the WRF model[J]. J. Geophys. Res.:Atmos., 117 (D12), doi: 10.1029/2011JD017080. [42] 徐慧燕, 朱业, 刘瑞, 等. 2013.长江下游地区不同边界层参数化方案的试验研究[J].大气科学, 37 (1):149-159. doi: 10.3878/j.issn.1006-9895.2012.12021Xu Huiyan, Zhu Ye, Liu Rui, et al. 2013. Simulation experiments with different planetary boundary layer schemes in the lower reaches of the Yangtze River[J]. Chinese J. Atmos. Sci. (in Chinese), 37 (1):149-159, doi:10.3878/j.issn. 1006-9895.2012.12021. [43] Xu H Y, Zhai G Q, Wang D H, et al. 2015. An evaluation of the Mellor-Yamada-Janjić formulation parameters for the QNSE scheme in the WRF model over the lower Yangtze River valley[J]. Terr. Atmos. Ocean. Sci., 26 (3):283-299, doi: 10.3319/TAO.2014.11.24.01(A). -
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