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

藏东南地区是青藏高原山地复杂下垫面的典型代表,其边界层大气过程异常复杂,给数值模拟和预报带来较大困难。大气边界层参数化方案的选取关系到能否正确模拟和预报局地大气过程。本研究采用中尺度模式WRF(Weather Research and Forecasting Model)对藏东南林芝地区对流和稳定边界层大气过程进行模拟,与2013年夏季"藏东南地区复杂下垫面地气交换观测实验"资料对比,研究ACM2,Boulac,M YJ,QNSE和YSU5种边界层参数化方案在青藏高原复杂下垫面的适用性。结果表明:对于水汽混合比垂直结构的模拟,Boulac和MYJ方案分别在模拟对流边界层和稳定边界层时能力最优。ACM 2方案最适宜藏东南复杂下垫面条件下的位温和风速垂直分布的模拟。各边界层参数化方案模拟对流边界层高度均较实际观测偏低,其中,QNSE方案模拟的边界层高度最接近观测。同一种边界层参数化方案对于夜间稳定边界层和正午对流边界层的模拟能力也不相同。该地区边界层风场受地形影响显著,风速较小,模拟的近地层风场较观测偏弱,MYJ和QNSE方案对近地层风场的模拟效果较好。     

WRF模式对福建沿海风电场风速预测的效果分析

在WRF模式中选取不同的边界层、近地面层以及陆面过程参数化方案,设计了4种不同物理过程参数化方案,组合模拟福建沿海某测风塔站2010年1月1—11日和7月1—11日的逐时风速,将数值模拟结果和同期测风塔观测数据进行对比,以寻求最佳参数化方案。经分析比较,采用MYJ边界层方案,Monin-Obukhov近地面层方案以及Noah陆面过程方案的方案2模拟效果最好。使用该方案对2010年1月和7月的风速进行模拟,按不同风速级别分别对数值模拟结果进行对比分析,结果表明:方案2对6~15m/s风速模拟的平均相对误差在20%左右,能够满足风电预测的精度需求;而对0~6m/s风速模拟的误差相对较大,这可能是由于模式地形分辨率不够精细以及风塔所处海陆交界处的特殊位置,使低风速容易受地面扰动以及海陆气流影响所致。     

不同参数化方案对风预报效果影响个例研究

中尺度气象模式对风场的预报效果与参数化方案的适应性紧密相关.以内蒙古高原丘陵地形、江苏平缓的海陆交界地形2种典型下垫面试验风电场为模拟区域,分别用WRFV3.2 （Weather Research and Forecast Model）模式自带的6种物理过程参数化组合方案预报了2010年1月和7月两个风电场区域的风速和风向,对比分析了参数化组合方案差异对风场预报的影响.结果表明：①内蒙古试验区,边界层MRF方案描述的边界层结构较MYJ方案合理;微物理方案WSM3在夏季的风速预报能力优于WSM6,而冬季相反;复杂地形区域的风场预报需考虑陆面过程参数化方案,尤其是夏季降水发生后,陆面过程对于边界层结构的影响增大,选用Noah优于无陆面过程.②江苏试验区,边界层MRF方案描述的边界层结构较MYJ方案合理;1月陆面过程RUC方案优于陆面热量扩散和Noah;7月陆面热量扩散方案优于RUC和Noah.③风向预报6个方案的预报风向统计与实际记录风向统计有较好的一致性,风向概率分布相似,盛行风向一致且稳定.     

中尺度数值模拟中的边界层多尺度湍流参数化方案

该文在多尺度湍流理论的研究成果基础上, 将边界层湍流风谱与平均量的梯度相联系, 建立了边界层多尺度湍流参数化子模式, 之后放入MM5模式中进行了个例模拟研究, 并与MM5模式附带的M RF边界层参数化、Blackadar高分辨率边界层参数化的模拟结果进行比较和分析。结果表明, 多尺度湍流理论能够反映出实际大气边界层中热量垂直输送的规律, 将其用于中尺度数值预报模式的边界层物理过程参数化是可行的; 多尺度湍流参数化在地表层和边界层内各个层次上都着重考虑含能量最大的涡的作用以及水平热力不均匀性的影响, 因此在地形和下垫面比较复杂的区域, 对中尺度天气系统的模拟有进一步发展的前景。     

WRF模式对2013年1月华北一次大雾的数值对比试验

采用WRF模式系统模拟了2013年1月22—23日华北大雾天气过程,针对不同的边界层方案、微物理方案和陆面方案,设计了三组数值试验方案,并利用地面观测资料对其模拟效果进行了对比分析,找出了部分最适合于此次大雾过程模拟的参数化方案,它们分别是TEMF边界层方案、Goddard微物理方案和RUC陆面方案。此次数值试验结果还表明,WRF模式对大雾天气地面风速风向的模拟能力较好,其他气象要素存在一定的误差,用10 m液态水含量和10 m相对湿度指标共同诊断大雾天气能有效提高大雾判识准确率。     

基于WRF模式的青岛沿海地区近地面风的敏感性研究

沿海地区受风的影响较大,不仅受大尺度季风的影响还受到中小尺度局地风的影响,利用WRF模式从参数化组合方案、土地利用数据、水平网格精度以及耦合UCM四个方面对青岛沿海地区近地面风进行了模拟和比较。结果表明,微物理方案和边界层方案对研究区近地面风的影响较大,WSM3 + YSU + Revised MM5 Monin-Obukhov + KF的参数化组合方案对研究区的模拟效果较好,相关系数在0.50~0.75之间,均方根误差在1.44~1.61之间。LUCC2015和TM2015土地利用数据能够更好地反映研究区的土地利用类型的分布特征,两者的模拟效果优于MODIS2001和MODIS2013。随着水平分辨率的提高,对地形刻画越精准,模拟效果越好,并且WRF+UCM方案能显著降低城市地区近地面风速,优化模拟结果。沿海地区近地面风的影响因素较为复杂,考虑模式的参数化方案组合、土地利用数据、地形和城市等多种因素,以期为沿海地区近地面风的模拟提供依据。      

复杂地形地区WRF模式四种边界层参数化方案的评估

为更加精确地模拟复杂地形地区大气边界层中气象要素,将NASA发布的SRTM3(约90 m分辨率)地形高度数据引入中尺度气象模式WRF(weather research and forecasting)中,结合四种边界层参数化方案(YSU、ACM2、MYN 2.5 level TKE(简称MYN)、Bougeault and Lacarrere TKE(简称BL))及模式自带地形数据GTOPO30(约1 km分辨率),模拟了2008年4月24－25日安徽黄山及周边地区大气边界层气象要素场变化特征,并对模式输出的2m气温、2m露点温度、10 m风速、湿度廓线与模拟区域内19个气象站及2个探空站数据进行比较.结果表明,无论采用哪种地形数据,四种边界层参数化方案中,YSU方案模拟的2m气温误差最小,ACM2方案模拟的2m露点温度和10m风速误差最小;采用SRTM3数据后,四种边界层参数化方案模拟的2 m气温平均均方根误差(root mean squared error,RMSE)分别降低了3.79％(YSU方案)、2.48％(ACM2方案)、3.8％(MYN方案)、0.87％(BL方案);对2 m露点温度模拟,除MYN方案模拟平均RMSE降低了0.59％外,其他三种方案模拟误差分别增加了1.39％(YSU方案)、0.49％(BL方案)、0.89％(ACM2方案);而对10m风速的模拟结果,除ACM2方案模拟平均RMSE降低了2.28％外,其他三种方案模拟误差分别增加了0.22％(YSU方案)、2.32％(MYN方案)、2.45％(BL方案);对2个探空站点湿度廓线的模拟显示,各边界层方案均能模拟出水汽的垂直变化趋势,但模拟效果总体表现为偏湿,采用SRTM3地形数据之后,ACM2方案模拟部分时刻的低层水汽廓线有所改善.     

边界层参数化方案对台风SANBA初生阶段影响的数值模拟研究

台风的形成和发展与边界层中低涡扰动的形成和发展密切相关。利用中尺度WRF模式,采用数值敏感性试验的方法来研究不同边界层方案对台风SANBA初生阶段的移动路径、强度及物理量场分布等方面的影响,结果表明:在台风形成初期,不同边界层方案对模拟的SANBA台风的强度和路径具有明显影响;未引入Bogus方案时,采用QNSE和ACM2边界层参数化方案的模拟结果与最佳路径资料最为接近。采用Bogus方法在初始场中引入热带扰动有关信息后,五种边界层参数化方案模拟的台风路径均有显著改进,其中QNSE和ACM2方案对台风路径和强度模拟结果的改进效果较其他几种边界层参数化方案的模拟结果改进小,说明这两种边界层参数化方案对SANBA台风发展初期边界层过程的处理较完善,能够较好地处理边界层过程和边界层热带扰动的形成和发展;对比分析表明由于不同边界层参数化方案对边界层过程处理的差异,导致SANBA台风发展到强盛阶段时近地层的有关物理量及自由大气中的物理场分布出现一定的差异,即使模拟效果较好的QNSE与ACM2边界层参数化方案模拟的台风垂直结构之间也存在一定的差异。数值试验也表明MYJ参数化方案对此次台风的模拟效果最差,在进行相关研究时应慎用该边界层参数化方案。     

边界层参数化方案在梅雨暴雨模拟中的应用比较

利用新一代中尺度数值模式WRF中不同的边界层参数化方案,对2009年7月24日发生在江南地区的一次暴雨过程进行了模拟分析。结果表明,边界层参数化对于10km精度的模式模拟,具有重要影响;不管哪种边界层参数化方案,由于都已经考虑比较全面的边界层物理过程,因此模拟结果都比较接近实况;边界层对模拟的影响主要在低层,对暴雨强度有影响,对暴雨落区也有影响;不同的参数化方案之间,边界层结构就会有所不同,使得边界层内动量、热量、水汽以及能量的垂直输送有差异,从而对模拟结果会产生影响。就此个例而言,MYNN Level-3方案具有最佳的模拟效果。     

MM4模式积云参数化方案的改进和检验Ⅱ：改进模式的检验试验

为了检验用Fritsch-Chappell积云参数化方案改进的MM4模式^[1]对梅雨锋暴雨系统的模拟能力，初步确定该模型的稳定性，可靠性，用不同的侧边界条件，地形条件，行星边界层参数化方法进行了模拟试验，并将模拟结果与采用Kuo-Anthes积云参数化方案的模式模拟结果分别进行了比较，结果表明，改进模式对高度场和降水的预报均有改善，并能预报出一些细致特征，此外，改进模式对侧边界条件和地形极为敏感，而对行星边界层参数化方法的敏感性较弱，因此在模式侧边界条件选取和地形处理方面应十分谨慎。     

苏通长江公路大桥设计风速的计算与分析

苏通长江大桥是世界第一跨度斜拉桥, 设计风速对其设计、建设、运营安全至关重要。为了合理选用大桥的设计风速, 在大桥桥位长江江面、江岸、南通气象站、常熟气象站建立风速同步观测站, 在桥位南岸建立80 m高的风梯度观测塔, 2000年3月1日至2003年2月28日开展地面及梯度风同步观测, 获取大桥设计风速计算所需的基础资料。在分析桥位风况与当地气象站异同及桥位风速随高度变化规律的基础上, 将气象站长年风速数据客观外延至桥位, 采用极值频率分布拟合方法, 分析计算得到大桥建设所需的设计基本风速和基准风速。分析表明:江面风速明显大于气象站, 也大于江岸风速, 计算值大于理论推算值。结果为大桥抗风设计提供了依据。     

A simple and efficient procedure for the numerical simulation of wind fields in complex terrain

In spite of recent progress in the prognostic numerical simulation of the atmospheric boundary layer, the explicit simulation of turbulent flows in actual complex terrain is generally still very complicated and time consuming for many environmental applications. In an attempt to develop simpler and more efficient application oriented techniques, although less refined, we propose a multi-step procedure for simulating wind fields. Once obtained the necessary meteorological input, the mass-consistent modelling technique is used to perform high-resolution mean wind flow simulations taking into account recent developments in the atmospheric boundary-layer theory. Besides, a procedure based on a generalisation of the local logarithmic law-of-the-wall over complex terrain is used to estimate the effective parameters characterising the simulated wind profiles. Turbulence intensities and spectral properties are then calculated through the estimated effective parameters, in particular through the effective friction velocity parameter. Finally, time series of the instantaneous velocity field are simulated by the Monte Carlo technique. Two applications of the proposed approach are discussed briefly: the first one is related to a coastal area in southern Italy (the Messina Straits), where the construction of the world’s longest central span bridge is being planned; the second one corresponds to the flow in a mountainous area in northern Italy (the Albenga Airport).     

利用多普勒雷达速度资料检验三维中小尺度模式流场

数值模式调试时,需要合理快速地将观测资料用于模式的流场检验,为此建立一种新方法:将模式输出的三维风场通过正演给出模拟雷达显示,使模拟风场可以有效地和多普勒雷达的速度产品进行比较.在强降水、冰雹和大风等的个例实验中,应用上述方法对双参三维冰雹云模式风场处理,模式给出的模拟雷达结果在主要对流特征上与雷达卖测一致.试验表明,该方法能直观对比多普勒雷达风场资料,有效检验模式中间过程流场,在模式研究调试中起到辅助作用.     

Non-Gaussian Lagrangian Stochastic Model for Wind Field Simulation in the Surface Layer

Wind field simulation in the surface layer is often used to manage natural resources in terms of air quality,gene flow(through pollen drift),and plant disease transmission(spore dispersion).Although Lagrangian stochastic(LS)models describe stochastic wind behaviors,such models assume that wind velocities follow Gaussian distributions.However,measured surface-layer wind velocities show a strong skewness and kurtosis.This paper presents an improved model,a non-Gaussian LS model,which incorporates controllable non-Gaussian random variables to simulate the targeted non-Gaussian velocity distribution with more accurate skewness and kurtosis.Wind velocity statistics generated by the non-Gaussian model are evaluated by using the field data from the Cooperative Atmospheric Surface Exchange Study,October 1999 experimental dataset and comparing the data with statistics from the original Gaussian model.Results show that the non-Gaussian model improves the wind trajectory simulation by stably producing precise skewness and kurtosis in simulated wind velocities without sacrificing other features of the traditional Gaussian LS model,such as the accuracy in the mean and variance of simulated velocities.This improvement also leads to better accuracy in friction velocity(i.e.,a coupling of three-dimensional velocities).The model can also accommodate various non-Gaussian wind fields and a wide range of skewness–kurtosis combinations.Moreover,improved skewness and kurtosis in the simulated velocity will result in a significantly different dispersion for wind/particle simulations.Thus,the non-Gaussian model is worth applying to wind field simulation in the surface layer.     

ANALYSIS OF THE EFFECT OF 3DVAR AND ENSRF DIRECT ASSIMILATION OF RADAR DATA ON THE FORECAST OF A HEAVY RAINFALL EVENT

The present study designs experiments on the direct assimilation of radial velocity and reflectivity data collected by an S-band Doppler weather radar (CINRAD WSR-98D) at the Hefei Station and the reanalysis data produced by the United States National Centers for Environmental Prediction using the Weather Research and Forecasting (WRF) model, the WRF model with a three-dimensional variational (3DVAR) data assimilation system and the WRF model with an ensemble square root filter (EnSRF) data assimilation system. In addition, the present study analyzes a Meiyu front heavy rainfall process that occurred in the Yangtze -Huaihe River Basin from July 4 to July 5, 2003, through numerical simulation. The results show the following. (1) The assimilation of the radar radial velocity data can increase the perturbations in the low-altitude atmosphere over the heavy rainfall region, enhance the convective activities and reduce excessive simulated precipitation. (2) The 3DVAR assimilation method significantly adjusts the horizontal wind field. The assimilation of the reflectivity data improves the microphysical quantities and dynamic fields in the model. In addition, the assimilation of the radial velocity and reflectivity data can better adjust the wind fields and improve the intensity and location of the simulated radar echo bands. (3) The EnSRF assimilation method can assimilate more small-scale wind field information into the model. The assimilation of the reflectivity data alone can relatively accurately forecast the rainfall centers. In addition, the assimilation of the radial velocity and reflectivity data can improve the location of the simulated radar echo bands. (4) The use of the 3DVAR and EnSRF assimilation methods to assimilate the radar radial velocity and reflectivity data can improve the forecast of precipitation, rain-band areal coverage and the center location and intensity of precipitation.     

海洋对大气风应力强迫响应的数值模拟及其与El Ninño的关系

本文设计了一个一层的等效非线性浅水模式,用此模式进行了有关海洋对表面风应力强迫的试验,并用于解释El Ninño的形成过程。结果表明,当西太平洋东部的偏东风突然减弱,并转变为偏西风时,能引起太平洋东部海面高度及温度的升高。该非线性浅水模式及相应的线性浅水模式都较成功地模拟了中东太平洋大范围增暖的这类广义El Ninño事件^[1]。试验结果还表明,东部海域海面高度的升高及增暖强度与西部海域西风的强度、持续时间和扩展的范围有关,即海域西部西风愈强、持续时间愈久、扩大范围愈大,产生的El Ninño就愈强。并对用线性模式得到的结果同用非线性模式的结果进行比较,结果表明非线性平流项能影响波的传播速度,使得逆基本气流传播的波的传播速度减慢,而非线性平流项对形成El Ninño这种行星尺度的超低频事件所起的作用可能不大。     

Variation in wind velocity over water

Starting from the equations of motion and continuity, a theoretical model is deduced in this paper for the variation in wind velocity over water caused by abrupt changes in surface roughness and temperature when air flows from land to water, based on the consideration that the turbulent exchange coefficient varies with height and distance from the upwind edge. According to the computation of this model, the variation in wind velocity over water, as the drift of air is from land to water, occurs mainly in the first few kilometers from the upwind edge. The wind velocity over water increases to a maximum when the air over land is stable, it tends to moderate when neutral condition is reached, and least variation is shown in unstable condition. And when the air over land is unstable the wind velocity is less over water than over land in strong winds, but some-what greater in light winds.     

A regression-based statistical correction of mesoscale simulations for near-surface wind speed using remotely sensed surface observations

Wind speed is an important meteorological variable for various scientific communities. In this study, numerical mesoscale simulations were performed over the Republic of Korea in 2006, to produce wind information distributed homogeneously with space. Then, an attempt was made to statistically correct the simulated nearsurface wind speed using remotely sensed surface observations. The weak wind season (WWS, from May to October) and strong wind season (SWS, from November to April) were classified on the basis of the annual mean wind speed. Although the spatial features and monthly variation pattern of the near-surface wind speed were reasonably simulated in the Weather Research and Forecasting (WRF) model, the simulations overestimated the observed values. To correct the simulated wind speeds, a regression-based statistical algorithm with different constants and coefficients for WWS and SWS was developed using match-up datasets of wind observations and satellitederived variables (land surface temperature and normalized difference water index). The corrected wind speeds showed reasonable performance for both WWS and SWS with respect to observed values. The monthly variation in the corrected wind speeds over the Republic of Korea also matched better with observations throughout the year, within a monthly bias range of approximately ± 0.2 m s^?1. The proposed algorithm using remotely sensed surface observations may be useful for correcting simulated near-surface wind speeds and improving the accuracy of wind assessments over the Republic of Korea.