Simulating Urban Flow and Dispersion in Beijing by ...

ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 30, NO. 6, 2013, 1663–1678

Simulating Urban Flow and Dispersion in Beijing by

Coupling a CFD Model with the WRF Model

MIAO Yucong1 (缪育聪), LIU Shuhua∗1 (刘树华), CHEN Bicheng1 (陈笔澄),ZHANG Bihui1,2 (张碧辉), WANG Shu1 (王姝), and LI Shuyan3 (李书严)

1Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871

2National Meteorological Center, China Meteorological Administration, Beijing 100081

3Beijing Regional Climate Center, Beijing Meteorological Bureau, Beijing 100089

(Received 20 September 2012; revised 23 February 2013; accepted 27 February 2013)

ABSTRACT

The airflow and dispersion of a pollutant in a complex urban area of Beijing, China, were numericallyexamined by coupling a Computational Fluid Dynamics (CFD) model with a mesoscale weather model.The models used were Open Source Field Operation and Manipulation (OpenFOAM) software package andWeather Research and Forecasting (WRF) model. OpenFOAM was firstly validated against wind-tunnelexperiment data. Then, the WRF model was integrated for 42 h starting from 0800 LST 08 September2009, and the coupled model was used to compute the flow fields at 1000 LST and 1400 LST 09 September2009. During the WRF-simulated period, a high pressure system was dominant over the Beijing area. TheWRF-simulated local circulations were characterized by mountain valley winds, which matched well withobservations. Results from the coupled model simulation demonstrated that the airflows around actualbuildings were quite different from the ambient wind on the boundary provided by the WRF model, and thepollutant dispersion pattern was complicated under the influence of buildings. A higher concentration levelof the pollutant near the surface was found in both the step-down and step-up notches, but the reason forthis higher level in each configurations was different: in the former, it was caused by weaker vertical flow,while in the latter it was caused by a downward-shifted vortex. Overall, the results of this study suggest thatthe coupled WRF–OpenFOAM model is an important tool that can be used for studying and predictingurban flow and dispersions in densely built-up areas.

Key words: WRF model, CFD model, OpenFOAM, dispersion

Citation: Miao, Y. C., S. H. Liu, B. C. Chen, B. H. Zhang, S. Wang, and S. Y. Li, 2013: Simulating urbanflow and dispersion in Beijing by coupling a CFD model with the WRF model. Adv. Atmos. Sci., 30(6),1663–1678, doi: 10.1007/s00376-013-2234-9.

1. Introduction

Patterns of pollution in urban areas are quite com-plex to study because of the influence of buildings onairflow and dispersion. Also, urban areas can sig-nificantly affect local meteorological conditions andair quality. Traditionally, the effects of urbanizationand local wind fields have been studied thorough fieldobservations. Examples include the urban bound-ary layer field campaign in Marseille (Mestayer et al.,2005), the Basel Urban Boundary Layer Experiment(BUBBLE) (Rotach et al., 2005), and the Urban 2000

(Allwine et al., 2002). Meanwhile, wind tunnel andwater tank experiments have also been extensivelyused to help understand the complex flow and disper-sion patterns around buildings (Pavageau and Schatz-mann, 1999; Baik et al., 2000; Uehara et al., 2000;Chang and Meroney, 2003; Liu et al., 2003).

In addition to field measurements and physical ex-periments, numerical models have recently been morewidely used to study the meteorological conditions anddispersion processes in complex urban areas. For ex-ample, Miao et al. (2009) parameterized the effect ofbuildings by using a drag coefficient and a canopy drag

∗Corresponding author: LIU Shuhua, [email protected]

© China National Committee for International Association of Meteorology and Atmospheric Sciences (IAMAS), Institute of AtmosphericPhysics (IAP) and Science Press and Springer-Verlag Berlin Heidelberg 2013

1664 URBAN FLOW AND DISPERSION SIMULATED BY WRF-OPENFOAM VOL. 30

length scale to improve the simulation of an urbanboundary layer model. Salamanca et al. (2011) exam-ined the Weather Research and Forecasting (WRF)model with different urban parameterization schemesand high resolution urban canopy parameters over thecity of Houston. Furthermore, Miao et al. (2009) usedthe WRF model to study the urban heat island inBeijing, while Miao and Chen (2008) stated that thestructure of horizontal convective cells can be mod-eled and depicted by using the WRF model with 500m horizontal grid intervals in Beijing areas.

With the rapid development of computing power,Computational Fluid Dynamics (CFD) models arenow also an option for simulating and predicting flowand pollutant dispersion around actual buildings in anurban area, with high horizontal and vertical resolu-tions. Chan and Leach (2007), for example, devel-oped a CFD model to simulate air flow and disper-sion in urban areas. At around the same time, DiSabatino et al. (2007) used the commercial CFD soft-ware FLUENT and the Atmospheric Dispersion Mod-elling System (ADMS)-Urban within idealized urbangeometries, while Gromke et al. (2008) used a CFDmodel to study airflow and dispersion of vehicle ex-hausts in urban street canyons with avenue-like treeplanting.

In addition, there are several studies that haveused CFD models with boundary conditions given bymesoscale models to investigate flow and dispersionin built-up areas. Brown et al. (2000) employed twoCFD models to simulate the microscale wind field ofSalt Lake City, Utah. Li et al. (2007) coupled theRegional Atmospheric Modeling System (RAMS) andFLUENT to simulate the wind field within an ideal-ized city block. Baik et al. (2009) used a CFD modelcoupled with a mesoscale model to simulate flow andpollutant dispersion in an urban area of Seoul, Ko-rea. And finally, Tewari et al. (2010) demonstratedthat by using the output of the WRF model as theinitial and boundary conditions, the prediction abilityof a CFD model employed over an urban area can besignificantly improved.

In this paper, we report the coupling of a CFDmodel to a mesoscale model, and then the results ofusing the coupled model to simulate airflow and pol-lutant dispersion in a built-up area of Beijing, China.The CFD model used is the free, open-source CFDpackage called OpenFOAM, while the mesoscale modelused was the WRF model. Our aim was to examineif OpenFOAM, which is designed for general-purposeuse, can be coupled with the WRF model to be suc-cessfully used as a practical tool for studying flow dy-namics and air quality in urban areas.

The remainder of the paper is organized as follows.

In section 2, the mesoscale model and CFD model aredescribed. In section 3, the results of validating theCFD model against wind-tunnel experimental data arereported. The results of the WRF–CFD numerical ex-periments are then presented in section 4, and thenfinally a discussion and conclusions are given in sec-tion 5.

2. Model descriptions

2.1 Mesoscale model

The mesoscale model used for the study was theWRF model. This model is a community model sys-tem, with a dual use for forecasting and research, thedetails of which can be found at http://www.ucar.edu/wrf/users. Specifically, we used the Advanced Re-search WRF Version 3.3, which was released in April2011.

2.2 CFD model

For the CFD model, the Open Source Field Oper-ation and Manipulation (OpenFOAM) software pack-age was used. OpenFOAM as the name suggests,is a free, open-source CFD toolbox. The core tech-nology of OpenFOAM is a flexible collection of effi-cient C++ modules. It comes with a growing set ofsolvers applicable to a wide range of physical prob-lems. Further details on OpenFOAM can be foundat http://www.openfoam.com. Specifically, we usedOpenFOAM version 2.1.0, released in December 2011.The computational grid of OpenFOAM can be struc-tured or unstructured; for simplicity, all the computa-tional grids used in this study were structured.

The buoyantBoussinesqSimpleFoam solver, one ofthe stander solvers of the OpenFOAM toolbox, wasused to solve the Reynolds-averaged Navier-Stokesequations with the k − ε turbulence model, by us-ing the SIMPLE algorithm (Ferziger and Peric, 2001).The mass equation, momentum equation, tempera-ture equation and the mass transport equation are ex-pressed by

∂uj

∂xj= 0 , (1)

∂ui

∂t+

∂

∂xj(ujui)− ∂

∂xj

[νeff

(∂ui

∂xj+

∂uj

∂xi

)]= −

∂p∗

∂xi+ gi[1− β(T − To)] , (2)

∂T

∂t+

∂

∂xj(ujT )− ∂

∂xk

(Keff

∂T

∂xk

)= 0 , (3)

∂C

∂t+

∂

∂xj(ujC)− νt

∂2C

∂x2k

= S , (4)

NO. 6 MIAO ET AL. 1665

where ui is the ith velocity component, T is the tem-perature, C is the pollutant concentration, and S isthe source term of the pollutant. In Eq. (2), νeff is theeffective kinematic viscosity, which is expressed as

νeff = νo + νt, (5)

where νo is the molecular viscosity, and νt is the tur-bulence viscosity. Furthermore, p∗ is a modified meankinematic pressure (Pope, 2001). To is the referencetemperature, and β is the coefficient of expansion withtemperature of the fluid. In Eq. (3), Keff is the heattransfer coefficient. For simplicity, the turbulence vis-cosity was used as the turbulent mass diffusivity inEq. (4), which means that the turbulent Schmidt num-ber was specified as 1.0 in this study.

Furthermore, the standard k− ε turbulence modelis given by

∂k

∂t+

∂

∂xj(ujk) =

∂

∂xj

[(νo +

νt

σk

)∂k

∂xj

]+

G− ε , (6)

∂ε

∂t+

∂

∂xj(ujε) =

∂

∂xj

[(νo +

νt

σε

)∂ε

∂xj

]+

c1ε

kG− c2

ε2

k, (7)

where k is the turbulent kinetic energy and its dissi-pation rate is ε, and σk, σε, c1 and c2 are empiricalconstants. G is the production of kinetic energy, whichis expressed as

G = νt

(∂ui

∂xj+

∂uj

∂xi

)∂ui

∂xj. (8)

3. OpenFOAM validation

The wind-tunnel data used for validating Open-FOAM were obtained from http://www.mi.uni-hamburg.de/Data-Sets.432.0.html. The experimentwas carried out in the wind tunnel at the Meteorolog-ical Institute of the University of Hamburg. Details ofthe experiment data and facility can be found at theaforementioned website.

The aim was to simulate the wind flow around asingle rectangular building at a scale of 1:200 in thewind tunnel. The width, length and height of thebuilding were 100 mm, 150 mm and 125 mm, respec-tively. The CFD computational domain was 2000 mm×1450 mm × 500 mm, which was also scaled downto match the wind tunnel experiment. The horizontaland vertical planes of the CFD domain are shown inFig. 1. The horizontal grid interval was 10 mm at the

Fig. 1. The horizontal and vertical plane of the CFDdomain: (a) horizontal plane of z = 35 mm; (b) verticalplane of y = 625 mm.

center of the computational domain where the build-ing was located, and expanded gradually to 50 mm atthe boundary. The dimensions of the X and Y direc-tions were 97 and 90. The vertical grid contained 61levels, and the vertical grid spacing of the lowest 51levels, which contained the building, was 5 mm, withthe vertical grid spacing of the remaining levels being25 mm. The wind was parallel to the X direction. Theinitial profiles of the X direction’s horizontal velocity,turbulent kinetic energy and rate of dissipation werespecified by the wind tunnel experiment data at theboundary.

The simulated and observed wind fields for twosmall regions indicated by the rectangles in Fig. 1 areshown in Fig. 2. It was found that OpenFOAM sim-ulated the wind field around a single building well.The simulations of the horizontal and vertical planesmatched well with observations, except that the sim-ulated lee-side vortex was longer than observed. Therelated coefficient (R) of the u, v and w componentsand mean error (ME) of wind speeds in the two regionsare given in Table 1. The ME was calculated by

ME =1n

n∑

i=1

|vs,i − vo,i| , (9)

where vs,i is the simulated value and vo,i is theobservation on the same grid. The mean errorsof the horizontal and vertical planes were 0.71 and0.69 m s−1, and all the related coefficients of the windcomponents were higher than 0.85.

From this preliminary validation, it was concluded


Fig. 2. Simulated and observed wind fields in the two small rectangular regions indicated in Fig. 1:(a) simulation of the small rectangular region in Fig. 1a; (b) observation of the small rectangularregion in Fig. 1a; (c) simulation of the small rectangular region in Fig. 1b; (d) observation of thesmall rectangular region in Fig. 1b.

Table 1. The related coefficient (R) and mean error (ME)of simulations against observations in the two small rect-angular regions of Fig. 1.

Plane R of u R of v/w ME (m s−1)

Horizontal 0.85 0.87 0.71Vertical 0.87 0.88 0.69

that OpenFOAM performs well in simulating the windfield around a single building.

4. WRF–OpenFOAM numerical experimentresults

Here, we report results from OpenFOAM numeri-cal experiments within an actual built-up area of Bei-jing, which were performed to study the flow anddispersion patterns around complex buildings. Theboundary conditions were given by the WRF model.

4.1 WRF model simulation

4.1.1 Case setupThe simulation case selected for the study was a

high-pressure event over the Beijing area, and the lo-cal circulations, such as mountain valley winds, werewell established. Four one-way nested computationaldomains were set, with horizontal grid spacing of 45,15, 5 and 1 km, and the horizontal grid dimensionswere 100 × 100, 97 × 97, 121 × 121 and 186 × 181,respectively (Fig. 3). The vertical grid contained 38full sigma levels from the surface to the 50-hPa level,and the lowest 11 levels were below 450 m, which wereused to provide the boundary conditions for the CFD

Fig. 3. Nested computational domains in the WRFmode simulation.

model described below. The innermost domain cov-ered the Beijing metropolitan area and its adjacentareas (Fig. 4). The National Centers for Environ-mental Prediction (NCEP) operational Global Final(FNL) Analyses datasets with 1◦ resolution were usedto set the initial and boundary conditions, and a 42-h simulation (0800 LST 08 September to 0200 LST10 September 2009) was conducted. The first 16hours were used as spin-up time for the case, and weused the Mellor-Yamada-Janjic PBL scheme (Janjic,1990, 1994), which predicts turbulent kinetic energy.Other physical parameterizations included the WRFsingle-moment three-class simple ice scheme, the Dud-


Fig. 4. The land use category of the innermost domain of the WRF model. Crossesshow the locations of the meteorological observatories.

hia (1989) shortwave radio scheme, the Rapid Radia-tive Transfer Mode longwave scheme, and the NoahLand Surface Model (Chen and Dudhia, 2001). Theland use dataset selected was that based on Moderate-resolution Imaging Spectroradiometer (MODIS), andthe category of the innermost domain’s land use isshown in Fig. 4. Finally, we did not use an urbancanopy parameterization scheme (Kusaka et al., 2001;Martilli et al., 2002; Kusaka and Kimura, 2004) withinthe WRF model because there were no available datato adapt the urban canopy parameters of those ur-ban schemes to be suitable for Beijing. Urban canopyparameterization schemes will be considered in futurework.

4.1.2 Simulation results

Figure 5 shows the WRF-simulated sea level pres-sure field in the outermost domain at 1400 LST 09September 2009, as well as observed results for thesame time. At 1400 LST, the observed sea level pres-sure field demonstrated a high pressure system overthe Beijing area. This high pressure system was sim-ulated well by the WRF model, despite the high pres-sure system being somewhat smaller than observed.

Figure 6 exhibits the evolution of the simulatedwind vector field at 2 m in the innermost WRF do-main, and the location of the CFD model computa-tional domain is indicated by a square in the middle

Fig. 5. The sea level pressure field at 1400 LST 09 September 2009: (a) observation; (b) simulation of theoutermost domain of the WRF model.


Fig. 6. Simulated 2-m wind vector fields in the innermost WRF domain on 09 September 2009: (a) 0200 LST;(b) 0600 LST; (c) 1000 LST; (d) 1400 LST; (e) 1800 LST; (f) 2200 LST. The location of the CFD model domainis indicated by an open square in each panel.

of the WRF domain. At 0200 LST, 0600 LST and 1000LST, the cold air flowed down from the mountains inthe north and west of the Beijing area, and a north-west wind blew in the CFD model domain. At 1400LST, the valley winds appeared as the land surface washeated by the sun, and the CFD model domain expe-rienced southwest winds. At 2200 LST, the wind di-rection turned to the northwest again. The simulatedmountain valley wind circulations (Fig. 6) are typicalof local circulations observed when the synoptic windis weak in the Beijing area on summer days.

In order to evaluate the accuracy of the WRFmodel, the observations of 32 meteorological observa-tories (Fig. 4) located in the Beijing area were used toexamine the WRF output. Figure 7 shows the timeseries of observed and WRF-simulated 2-m temper-ature and 10-m wind speed. The observation is themean value of the 32 stations, and the simulation isthe average of the values taken from the grids nearestto the stations. The simulated temperature was a lit-tle higher than that observed at night, and lower thanthat at noon. However, despite this deficiency, the di-urnal temperature cycle was simulated well. Althoughthe simulated wind speed was higher than observed,

the evolution of the wind speed represented a typicaldiurnal cycle of mountain-valley breezes well: the sim-ulated wind speed was at its maximum at 0000 LSTwhen the down slope wind was fully developed, anddecreased to a minimum at about 1100 LST when thewind began to change from down-slope wind to val-ley wind, and then increased to reach another peakat about 1700 LST when the valley wind was fullydeveloped. The difference between the simulated andobserved wind speeds may be due to the effect of thebuildings around the meteorological observatories inthe urban areas, acting to reduce wind speeds.

The frequency distribution of the wind direction ofthe 32 stations is given in Fig. 8. It can be seen thatthe dominant wind direction of the simulation and ob-servation at 0400 LST and 1000 LST was north, cor-responding to mountain wind at that time. Further-more, both simulated and observed dominant direc-tions were south at 1600 LST, when the valley windwas well developed. However, a difference betweenthe simulated and observed dominant directions wasfound at 2200 LST in that the simulation was mostlynorth, while the observation was south. Apparently,the simulated valley wind weakened too quickly. Fur-


0 2 : 0 0 0 6 : 0 0 1 0 : 0 0 1 4 : 0 0 1 8 : 0 0 2 2 : 0 001 02 03 04 0T emperat uer2 m(o C) ( a ) O b e s r v a t i o nS i m u l a t i o n0 2 : 0 0 0 6 : 0 0 1 0 : 0 0 1 4 : 0 0 1 8 : 0 0 2 2 : 0 0024

6S e p t e m b e r 9 t h ( h o u r )Wi nd speed10 m( m/ s) ( b ) O b e s r v a t i o nS i m u l a t i o n

Fig. 7. Time series of observed and WRF-simulated (a) 2-m temperature and (b)10-m wind speed. The observation is the mean value of 32 stations around Beijing,while the simulation is the average of the values taken at the grids nearest to thestations.

0 9 0 1 8 0 2 7 0 3 6 002 04 06 08 01 0 0F requency(%) ( a ) 0 4 : 0 0O b e s r v a t i o nS i m u l a t i o n0 9 0 1 8 0 2 7 0 3 6 002 04 06 08 01 0 0 ( b ) 1 0 : 0 0O b e s r v a t i o nS i m u l a t i o n

0 9 0 1 8 0 2 7 0 3 6 002 04 06 08 01 0 0W i n d d i r e c t i o n 1 0 m ( o )F requency(%) ( c ) 1 6 : 0 0O b e s r v a t i o nS i m u l a t i o n

0 9 0 1 8 0 2 7 0 3 6 002 04 06 08 01 0 0W i n d d i r e c t i o n 1 0 m ( o )

( d ) 2 2 : 0 0O b e s r v a t i o nS i m u l a t i o nFig. 8. The frequency distribution of simulated and observed wind direction on 09 September 2009:(a) 0400 LST; (b) 1000 LST; (c) 1600 LST; (d) 2200 LST.

thermore, the hit rate (HR) of the 2-m temperature,10-m wind speed and 10-m wind direction was 0.57,0.32 and 0.42, respectively. The criteria for the HRcalculation were model–observation agreement within2◦C for temperature, 1 m s−1 for wind speed, and 30◦

for wind direction.In short, the WRF model simulated the mountain-

valley circulation in September 2009 well. The de-ficiencies between the simulated results and observa-

tions may have been due to the difference between thehorizontal grid interval of the WRF grids and the re-gion representative of the meteorological observatory.Furthermore, the buildings in the urban areas mayhave enhanced the difference between the simulatedand observed wind speed. Therefore, it is necessaryto use urban canopy parameterization schemes in fu-ture studies to successfully parameterize the effects ofbuildings on wind speed.


Fig. 9. Google Earth image and horizontal plane of the CFD domain: (a) Google Earth imageof the Zhongguancun area; (b) the horizontal plane of the CFD domain at z = 1.5 m. The redrectangle in (a) indicates the CFD domain; the red dashed line L1 in (b) indicates the HaidianDajie main street, and L2 represents the Haidian Zhongjie main street; the blue crosses labeled S1and S2 in (b) indicate the locations of point sources; the solid line rectangles R1 and R2 indicatethe regions shown in Figs. 12 and 13, respectively; the dashed line rectangle R3 indicates the regionshown in Fig. 14.

4.2 Coupling method

Figure 6 shows the location of the CFD model do-main, which is in the middle of Beijing, and withinwhich there is an area called Zhongguancun, a tech-nology hub in the Haidian district of the city. Figure9a shows a Google Earth image of the CFD computa-tional domain. The actual buildings and their differ-ent shapes were simplified to cuboids for the purposesof this study, and 91 simplified cuboid buildings werebuilt up in the CFD computational domain. The aver-age height of the buildings was 37.9 m, with the tallestbuilding being 114 m and the shortest 6 m. The hori-zontal grid dimensions and spacing of the CFD modeldomain were 101×101 and 10 m, respectively. The ver-tical grid contained 91 levels from the surface to 450m, and the vertical grid spacing of the lowest 51 levels,which contained the buildings, was 3 m, while that ofthe topmost 20 levels was 10 m. The grid interval ofthe remaining 20 levels was 5 m.

It is important to note that the grid spacing anddomain size of the WRF and CFD models were quitedifferent. In the innermost computational domain ofthe WRF model, the horizontal grid spacing was 1 km,which was equal to the CFD model’s horizontal do-main size. Therefore, only one grid cell that was near-est to the location of the CFD domain from the WRFsimulation was actually used as the boundary condi-tions for the CFD model simulation. A one-way nest-

ing method was employed to couple the CFD modelto the WRF model.

In this study, the WRF-simulated results were usedto provide the boundary conditions for the CFD modelto numerically calculate the steady state of airflow,including the velocity components, temperature, tur-bulence kinetic energy (TKE) and the atmosphericboundary layer height. The momentum diffusion co-efficient was indirectly calculated by the Grisogonoscheme (Grisogono and Oerlemans, 2002; Jericevic etal., 2010), and the TKE dissipation rate was calculatedby

ε = Cµk2/νt , (10)

where Cµ is an empirical constant.

4.3 WRF–OpenFOAM simulation

Since the buoyantBoussinesqSimpleFoam solveruses the SIMPLE algorithm to calculate the steady-state turbulent flow, in this study we used it to sim-ulate wind field at two moments with different windspeeds and directions: 1000 LST and 1400 LST 09September 2009. Some elements extracted from theWRF-simulated results used to initiate the CFD modelat these two moments in time are shown in Fig. 10.As mentioned in section 4.1.2, the CFD domain expe-rienced northwest winds at 1000 LST and southwestwinds at 1400 LST.


− 6 − 3 0 3 601 0 02 0 03 0 04 0 0Z( m) ( a ) 1 0 : 0 0UV

0 0 . 2 0 . 4 0 . 601 0 02 0 03 0 04 0 0 ( b ) 1 0 : 0 0 1 0 2 0 3 0 4 001 0 02 0 03 0 04 0 0 ( c ) 1 0 : 0 00 2 4 601 0 02 0 03 0 04 0 0

W i n d s p e e d ( m / s )Z( m) ( d ) 1 4 : 0 0UV

0 0 . 5 1 1 . 5 201 0 02 0 03 0 04 0 0T K E ( m 2 / s 2 )( e ) 1 4 : 0 0 1 0 2 0 3 0 4 001 0 02 0 03 0 04 0 0

T ( o C ) ( f ) 1 4 : 0 0Fig. 10. Some elements extracted from the WRF output at the location of the CFD domain: (a) wind profile at1000 LST; (b) TKE profile at 1000 LST; (c) temperature profile at 1000 LST; (d) wind profile at 1400 LST; (e)TKE profile at 1400 LST; (f) temperature profile at 1400 LST.

4.3.1 Flow analysis

The simulated wind vector fields for the two mo-ments in time at z = 1.5 m and 19.5 m are given inFig. 11. Complex urban flows were apparent aroundthe actual building clusters. At 1000 LST, the am-bient wind direction given by the WRF model wasnorthwest, and the speeds at the heights of 1.5 m and19.5 m were 1.4 and 2.4 m s−1, respectively. Here, theterm “ambient wind” means the WRF-simulated windused as the boundary conditions for the CFD modeldomain (Baik et al., 2009), including wind speed anddirection. However, under the influence of buildings onflow, the modeled wind direction mostly did not fol-low the ambient wind direction provided by the WRFmodel. Furthermore, because of the effect of buildingclusters acting to reduce wind speed, the simulatedwind speed was lower than the ambient wind speed inmost simulated regions. At z = 19.5 m, the flows werestronger than that at the height of 1.5 m, and in re-gions where buildings were uncommon at this height,the northwestly flow was dominant. At 1400 LST,when the valley wind appeared, the ambient wind di-rection given by the WRF model turned southwest,and the wind speed was higher than that at 1000 LST.

The simulated wind vector fields for the two smallregions at z = 1.5 m indicated by the rectangles R1and R2 in Fig. 9b are enlarged in Figs. 12 and 13 to

show some characteristics of urban flows. A typi-cal channeling flow pattern can be seen in Fig. 12.In the inlet of the channel-type street canyon, whichis formed by buildings, airflows converge, strengthen,pass through, and leave. The flow in the middle of thechannel region was stronger than the ambient wind,with the speed as high as 2.8 m s−1, twice as strong asthe ambient wind at 1000 LST. As mentioned above,the wind speed at 1400 LST was stronger, and thewind direction turned from north to south. These pat-terns are also apparent in Fig. 12.

Figure 13 exhibits the wind field around four build-ings. The heights of buildings A, B, C and D indi-cated in the figure are 60 m (HA), 39 m (HB), 51 m(HC) and 54 m (HD), respectively. The ratio of HA

and HB is about 1.5, while HC/HD approximates to 1.At 1000 LST, when the wind direction was northwest,a step-down notch (Assimakopoulos et al., 2003) wasformed by buildings A and B, while a step-up notchwas formed by these two buildings when the wind di-rection changed at 1400 LST. Although the distancebetween buildings C and D was 50 m, a little widerthan the distance between buildings A and B, whichwas 40 m, it was reasonable to investigate the interest-ing variations of airflow in the step-down and step-upnotch configurations by comparing the canyon flow inthe nearby symmetric notch formed by buildings Cand D, which almost had the same height.


Fig. 11. Simulated wind vector fields: (a) wind field at z = 1.5 m, 1000 LST; (b) wind field atz = 19.5 m, 1000 LST; (c) wind field at z = 1.5 m, 1400 LST; (d) wind field at z = 19.5 m, 1400LST.

At 1000 LST, a single vortex was established in thevertical plane of the symmetric notch of buildings Cand D (Fig. 13c), and a typical lee-side cavity (Kaplanand Dinar, 1996) was seen on the lee side of down-wind building D. The flow pattern was very similar inthis symmetric notch at 1400 LST, except for the windspeed and direction (Fig. 13f). However, At 1000 LST,the flow pattern in the step-down notch was quite dif-ferent from that in the symmetric notch; the flow wasweaker in the step-down notch, and the updraft tookthe place of the vortex in the vertical plane of thestep-down notch (Fig. 13b), corresponding to the con-vergence in the middle of buildings A and B at thehorizontal plane near the surface (Fig. 13a).

At 1400 LST, the flow in the vertical plane of thestep-up notch was stronger than that in the symmetric

notch (Fig. 13e), caused by the blockage of flow in frontof the higher down-wind building A. Furthermore, thewhole vortex in the step-up notch shifted downward,corresponding with the strong divergence in the mid-dle of buildings A and B near the surface (Fig. 13d).

It is further noted that the flow was too strong tobe reasonable near the west boundary of the computa-tional domain (Fig. 11), where an unrealistic channel-ing flow pattern was caused by the westernmost build-ings and west boundary. Therefore, it is better to en-large the distance from the outermost buildings to theboundary to eliminate this unrealistic channeling flowpattern in future studies. However, aside from thiswestern region, the wind field simulated by the WRF–OpenFOAM coupled model was reasonable in otherregions of the computational domain.


Fig. 12. Simulated wind vector field of the rectangle R1 in Fig. 9b: (a)wind field at z = 1.5 m, 1000 LST; (b) wind field at z = 1.5 m, 1400LST.

Fig. 13. Simulated wind vector field of the rectangle R2 in Fig. 9b: (a, d) horizontal plane at z = 1.5 m; (b, e)vertical plane at x = 415 m; (c, f) vertical plane at x = 555 m; (a–c) show the wind field at 1000 LST; (d–f) showthe wind field at 1400 LST. The vertical plane at x = 415 m is indicated by the line cross buildings A and B in(a), and the vertical plane at x = 555 m is indicated by the other line in (a).


Table 2. Details of the dispersion numerical experiments.

Source Source location Start Emission rate Simulatedtype indicated in Fig. 9b time (ppb s−1) period (min)

Point S1 1000 LST 50 5Point S2 1000 LST 50 5Point S1 1400 LST 50 5Point S2 1400 LST 50 5Line L1 1000 LST 10 10Line L2 1000 LST 10 10Line L1+L2 1000 LST 10 10Line L1 1400 LST 10 10Line L2 1400 LST 10 10Line L1+L2 1400 LST 10 10

In short, the different flow patterns at each of thetwo moments in time arose from the different ambi-ent wind directions and speeds provided by the WRFmodel for the given intricate building configuration.

4.3.2 Pollutant dispersionWe assumed that the steady state of airflow com-

puted by OpenFOAM at one moment could be usedto present the airflow state in a short time period,such as 5 or 10 minutes. Furthermore, the transportequation was solved in the short interval by using the

steady velocity and turbulence field at 1000 LST and1400 LST. Two sets of pollutant dispersion numericalexperiments were performed, the details of which aregiven in Table 2.

4.3.2.1 Dispersion simulation in the step-down andstep-up notch

To investigate the dispersion pattern in the step-down and step-up notch, four numerical experimentsof point source dispersion were performed (Table 2).The point sources were passive pollutant sources at a

Fig. 14. Simulated concentration fields of the rectangle R3 in Fig. 9b: (a, e) S1 source dispersion at 1005 LST; (b, f)S2 source dispersion at 1005 LST; (c, g) S1 source dispersion at 1405 LST; (d, h) S2 source dispersion at 1405 LST;(a–d) show the horizontal plane at z = 1.5 m; (e, g) show the vertical plane at x = 415 m; (f, h) show the verticalplane at x = 555 m. The vertical plane at x = 415 m is indicated by the line crossing buildings A and B in (a), andthe vertical plane at x = 555 m is indicated by the other line in (a).


Fig. 15. Simulated concentration fields of line sources at z = 1.5 m: (a, d) L1 line source; (b, e) L2 line source;(c, f) L1 and L2 line sources; (a–c) show the concentration field at 1010 LST; (d–f) show the concentration fieldat 1410 LST.

height of 1.5 m, which were continuously emitted dur-ing the simulated period. Figure 14 shows the pollu-tant concentration fields of the rectangle R3 in Fig. 9bafter 5 minutes of dispersion simulation.

The concentration field in the step-down notch isshown for 1005 LST. Compared to the concentrationfield in the nearby symmetric notch (Fig. 14b), it canbe seen that the pollutant plume with concentrationhigher than 100 ppb extended wider in the horizon-tal plane near the surface in the step-down notch(Fig. 14a), which means the pollutant tended to ac-cumulate more in the step-down notch near the sur-face. In the vertical plane at the same moment in time,the pollutant in the symmetric notch accumulated atthe lower corner of the up-wind building C, and trans-ported along the up-wind building in the vertical di-rection to its top (Fig. 14f). This was caused by thecounterclockwise rotated vortex formed in the notchof buildings C and D (Fig. 13c). Meanwhile, there wasno vortex in the step-down notch, and a weak updrafttook its place there (Fig. 13b). Therefore, the pollu-tant accumulated more in the middle of the notch near

the surface (Fig. 14e).In contrast to the concentration fields at 1005 LST,

the pollutant concentration level was lower at 1405LST, corresponding to the stronger flow at that mo-ment. Although the vertical flow was stronger inthe step-up notch, the surface pollutant concentrationlevel was higher than that in the symmetric notch at1405 LST. This was because the vertical-dispersed dis-tance of the pollutant was shortened by the shifteddownward vortex formed by the step-up notch config-uration, and the pollutant was trapped in the circula-tion of the vortex, which was separated from the upperflow.

4.3.2.2 Line-source dispersion simulation

Two main streets of Zhongguancun are indicatedby red dashed lines in Fig. 9b. In this part of the work,six numerical experiments with line sources were per-formed to study the dispersion pattern of vehicle pol-lutants (Table 2). The line sources were all set at aheight of 1.5 m and continually emitted during thesimulated period.


It was found that the typical channel flow pat-tern occurred along the route of the L2 line sourceat the two moments in time (Fig. 11), and most ofthe pollutant released from the L2 source was trans-ported by this channel flow and accumulated in thedownwind regions along the route of L2 (Figs. 15b ande). Meanwhile, the flow along the route of the east–west line source (L1) was more complicated (Fig. 11),with the pollutant accumulating in some regions wherethe wind speed was low or convergence happenedalong the route of L1, such as the linear region where100 m < x < 250 m (Figs. 15a and d).

It was northwestly ambient winds that were blow-ing at 1000 LST; however, the pollutant plume of theL2 line source with concentration higher than 10 ppbextended wider in the west than in the east, where thedirection of v component was opposite to the ambientwind’s v component under the influence of buildings.Despite the ambient wind direction being southwestat 1400 LST, the L2’s pollutant plume with concen-tration higher than 10 ppb also extended wider in thewest in the region where 600 m < y < 800 m (Fig. 15e).These features could not be explained by the ambientwind direction alone.

Unlike the pollutant plume of the south–north linesource, which was a sector of the horizontal plane,the east–west line source almost contaminated all thedownwind regions in the south–north direction.

When the pollutant was released from both twoline sources, the CFD domain was divided into foursub-regions by the routes of L1 and L2, and it is inter-esting that the lower left sub-region was the heaviestpollution region at 1010 LST, except for linear regionsalong the two line sources.

5. Discussion and conclusions

In this study, we first validated OpenFOAMagainst wind-tunnel experiment data, and the resultsshowed that it performs well in simulating the windfield around a single building. Then, the urban flowand pollutant dispersion in the Zhongguancun areaof Beijing was evaluated using the WRF–OpenFOAMcoupled model. It was found that the WRF modelcan simulate the mountain-valley circulation of Beijingwell, and that the high resolution flow field simulatedby OpenFOAM in the built-up area was reasonable inmost regions.

Although the mountain-valley circulation can besimulated well by the WRF model, the simulated windspeed near the surface was higher than observed overthe urban area. It was clear that this discrepancy wascaused by the lack of an urban canopy parameteri-zation scheme. Therefore, to better simulate urban

meteorological conditions, it is necessary to use urbancanopy parameterization schemes, whose parametersshould be adapted based on local building morpholog-ical data.

The flow and dispersion patterns of step-down andstep-up notches were also investigated by the coupledmodel. It was found that vertical flow was weakerin the step-down notch, where more of the pollutantwould accumulate near the surface. Despite a strongervortex being formed in the vertical plane of the step-upnotch, a higher surface pollutant concentration levelwas caused by the downward-shifted vortex, which wasable to shorten the vertical-dispersed distance of thepollutant and cap the pollutant near the surface.

The dispersion pattern of vehicle pollutants alongtwo main streets of Zhongguancun was also evaluated.The pollutant dispersion pattern of line sources un-der different ambient wind speeds and directions inthe built-up area was complicated, and could not sim-ply be obtained from the ambient wind of the WRFmodel alone. In Beijing, when the mountain-valley cir-culation is well established, the wind direction changesfrom north to south in the daytime, and then turnsnorth again at night. At that time, it seems, from theresults, that the pollutant from the east–west mainstreet would contaminate large downwind regions inthe south–north direction in a built-up area whosebuilding morphology and distribution are similar tothe Zhongguancun area, and that some regions withhigher concentration levels of the pollutant would beproduced by the pollutant released from the south–north main street.

Finally, it should be pointed out that there are noobservations of the actual built-up area used in thisstudy to evaluate the high-resolution simulation of theWRF–OpenFOAM coupled model. In other words,the work reported here is simply a set of reasonablenumerical experiments. More studies are thereforeneeded to validate the capability of this coupled model.However, even at this stage of the research, it is sug-gested that the coupled WRF–OpenFOAM model canbe used as an important tool to study and predict ur-ban flows and dispersions in densely built-up areas.

Acknowledgements. This work was supported by

the Public Welfare Special Fund Program (Meteorology)

of the Chinese Ministry of Finance under Grant No.

GYHY201106033.

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