An Inter-comparison of Three Urban Wind Models Using Oklahoma City Joint Urban 2003 Wind Field Measurements

8/2/2019 An Inter-comparison of Three Urban Wind Models Using Oklahoma City Joint Urban 2003 Wind Field Measurements

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An inter-comparison of three urban wind models using Oklahoma City Joint

Urban 2003 wind field measurements

Marina Neophytou a,n, Akshay Gowardhan b, Michael Brown b

a Environmental Fluid Mechanics Laboratory, Department of Civil and Environmental Engineering, University of Cyprus, Cyprusb Los Alamos National Laboratory, New Mexico, USA

a r t i c l e i n f o

Available online 22 March 2011

Keywords:

Fast-response models

Model evaluation

Large eddy simulation

Computational fluid dynamics

Empirical-diagnostic model

Field measurements

a b s t r a c t

Three wind models are compared to near-surface time-averaged wind measurements obtained in

downtown Oklahoma City during the Joint Urban 2003 Field Campaign. The models cover severallevels of computational approximation and include in increasing order of computational demand: a

mass-consistent empirical-diagnostic code, a Reynolds-averaged NavierStokes (RANS) computational

fluid dynamics (CFD) model, and a Large Eddy Simulation (LES) CFD code. The models were run with

identical inlet and boundary conditions using the same grid resolution; the choice of the specific

computational set-up reflects demands for fast-response models, although it may be a sub-optimal

choice for the more complex models. A qualitative comparison of the model-computed flow fields with

the Joint Urban 2003 wind measurements shows that all three models compare favorably to the near-

surface wind measurements in many locations, although there are often instances of winds being

calculated poorly in specific locations. The CFD models, however, had clearly superior looking flow

fields, whereas the empirical-diagnostic code produced fields that were less smoothly varying. The

inter-comparison exercise was supported by point-by-point quantitative comparisons of the wind

speed and wind direction and with statistical measures. The RANS-CFD code, for example, was within

50% of the measured wind speed 62% of the time as compared to 53% for the LES model and 49% for the

empirical-diagnostic code. For wind direction, the RANS-CFD code was within 301 of the measured

wind direction 58% of the time as compared to 50% for the LES code and 43% for the empiricaldiagnostic code. It is noticeable that throughout the various IOP cases examined, and under the specific

computational set-up used in the simulations for fast-response needs, there is no clear superiority of

one model over another. In addition, for the LES model, which in theory should provide the most

realistic representation of the flow field, it appears that further to the sub-optimal computational set-

up, as well as the uncertainty and natural variability persistent in the real world, has resulted in

diminished performance.

& 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Simulating urban wind flow for fast-response applications

poses significant challenges for computational modeling; on one

hand both the inhomogeneity of the urban geometry and thecomplexity of the resulting flows urge for more sophisticated

models, on the other, however, more advanced or detailed models

are associated with substantial increase in computational time

that nearly removes the model application from the context of

fast response. Expected or accepted run-up times range from a

few seconds to tens of minutes.

The complexity of the flows that develop in cities, around

and in-between buildings lies in the three-dimensionality of the

resulting flows (Boris et al., 2001). For example, vertically rotating

vortices can develop between buildings in so-called street can-

yons (e.g. DePaul and Sheih, 1986; Oke, 1988), horizontally

rotating vortices may exist near the canyon-intersection interface

(e.g., Hoydysh and Dabberdt, 1988; Pol and Brown, 2008),updrafts often occur on the backsides of tall buildings (e.g., Heist

et al., 2004), and strong downdrafts on the front faces of tall

buildings that stick up above others can lead to divergence at the

surface and street-level winds in all directions (e.g. Hanna et al.,

2006; Nelson et al., 2007; Princevac et al., 2010). In more open

areas, the prevailing wind can penetrate down into the city and

result in strong channel flow, bifurcating at 4-way intersections,

T-intersections, and along side streets (e.g., Belcher, 2005). The

channel winds interact, compete, and merge with the building-

induced vortices, updrafts and downdrafts and may produce highly

spatially variable winds at street level. As the prevailing wind

directions shifts, the vertically rotating vortices can turn into spiral

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Journal of Wind Engineeringand Industrial Aerodynamics

0167-6105/$ - see front matter & 2011 Elsevier Ltd. All rights reserved.

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n Corresponding author.

E-mail address: [email protected] (M. Neophytou).

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vortices (e.g., Nakamura and Oke, 1988; Klein and Clark, 2007),

downdrafts can dissipate in intensity as the prevailing wind hits tall

buildings at oblique angles, and channel flow reduces in strength in

some streets and increases in others, resulting in complicated non-

linear responses to the changing wind direction. These complex

building-induced 3D flow patterns can significantly alter the trans-

port and dispersion of plumes, resulting in displaced plume cen-

trelines (e.g., Theurer et al., 1996; MacDonald and Ejim, 2002),

significant upwind transport (e.g., Hanna et al., 2006) and reducedstreet-level concentrations in some cases due to enhanced venting

(e.g., Heist et al., 2004, 2009) and increased street-level concentra-

tions in other cases due to trapping and sheltering effects

(e.g., Chang and Meroney, 2003).

Accurate transport and dispersion prediction at the city scale

requires models that account for these effects on the wind field.

Over the past several decades considerable progress has been

seen in the application of computational fluid dynamics (CFD)

models. Some of the first CFD simulations were performed around

a few idealized buildings (e.g., Patankar, 1975; Murakami et al.,

1992). With the enhancement in computer power, researchers

are currently performing simulations in real cities that include

hundreds to thousands of building obstacles (e.g., see Neophytou

and Britter, 2005; Pullen et al., 2005; Hanna et al., 2006).

A number of teams have successfully applied Reynolds-averaged

NavierStokes (RANS) codes and large-eddy simulation (LES)

codes to neighborhood- and city-scale transport and dispersion

problems (e.g., Coirier et al., 2006a, b). In addition, progress has

been made in highly parameterized (non-NavierStokes) numer-

ical wind field models for use in emergency response systems, or

even for assessments and planning where many cases must be

run over a short time period (Boris et al., 2001). These codes are

based on potential flow or diagnosticempirical approaches

(e.g. Rockle, 1990; Kaplan and Dinar, 1996). These types of models

attempt to approximate the impacts of large numbers of buildings

on the wind flow, providing a semi-realistic 3D flow field around

buildings, but at a fraction of the run time as compared to CFD

codes. The substantial decrease of run time makes such models

particularly useful; however, it is extremely important to deter-

mine: (i) what kind of wind flow features are missed using such

simplifications and assumptions and consequently (ii) how much

these predictions from simpler models differ from predictions of

higher-complexity models.

This paper reports results from an inter-comparison between

three urban wind models, each of different level\of complexity,

using data from time-averaged wind field measurements obtai-

ned in downtown Oklahoma City during the Joint Urban 2003

Field Campaign; the exercise is performed for 3 specific test cases

(corresponding to three different Intensive Observation Periods)

using a specific computational set-up reflecting fast-response

standards. The paper is structured as follows: a brief summary

of the Joint Urban 2003 field campaign including the specific

Intensive Observation Periods (IOP) that were used in our ana-lyses is given in Section 2. The models in increasing order of

computational cost and complexity include: (i) a mass-consistent

empirical-diagnostic code, (ii) a RANS-CFD code, and (iii) a LES-CFD

code with a more detailed description of the models presented

in Section 3. It is important to note that the specific computa-

tional set-up used in this exercise was selected to reflect realistic

running conditions for fast-response applications but may be sub-

optimal for the more complex RANS and LES models. Finally, a

systematic comparison of the models with the wind field data is

presented and discussed in Section 4 while Section 5 concludes

with the overall findings of this work considering carefully the

difficulty in arguing for clear superiority of one model over

another based on a certain number of cases, as well as suggestions

for further work.

2. Oklahoma City Joint Urban 2003 (JU2003)

Wind measurements obtained from the Joint Urban 2003

(JU03) field experiment were used to evaluate the wind models.

The JU03 experiment was held in Oklahoma City in July 2003 with

the goal of providing information useful for testing and evaluation

of the next generation of urban transport and dispersion models.

The experiment consisted of a large number of tracer releases, a

network of concentration samplers and meteorological sensorsplaced in and around the city. An overview of the field campaign

is provided in Allwine et al. (2004).

Although the Central Business District (CBD) in Oklahoma

City is only a little over a kilometer on a side (Fig. 1), a number

of relatively tall buildings exist there, including the Bank One

Building (152 m), the FNC Building (123 m), the Oklahoma Tower

(117 m), the Kerr-McGee Building (115 m), and City Place

(107 m). The plan area fraction in the CBD is 0.4 and the average

building height in the southern half of the CBD is 27 m, while in

the northern half it is 65 m ( Burian et al., 2005). There are some

trees in the CBD along streets and in the Botanical Gardens Park in

the southwest sector of the CBD. During the July experimental

period, temperatures were generally warm, winds were strong,

and the prevailing wind direction consistently had a southerly

component.

About one hundred and fifty 2D and 3D sonic anemometers

were placed throughout the city on towers, tripods, light poles,

and building rooftops and nine sodars were placed in and around

the city to provide upper-air information. Although a majority of

the wind instrumentation was operating during the entire period

of the experiment, there were intensive operating periods (IOPs)

where more equipment was placed in the field. Within the central

business district (CBD), the instrument distribution below roof-

level included twenty four anemometers in the eastwest running

street canyons (e.g., Park Avenue) and twenty four anemometers

in northsouth running street channels (most in intersections,

Depicted domain in comparative

results (Figures 2,3, and4)

350m

Fig. 1. A Google Earth aerial view of the entire Central Business District in

Oklahoma City. The extent of the image corresponds to the size of the modeling

domain used for this study.

M. Neophytou et al. / J. Wind Eng. Ind. Aerodyn. 99 (2011) 357368358
http://dx.doi.org/10.1016/j.jweia.2011.01.010


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however) during the IOPs. For this model evaluation study, we have

used thirty-minute average sonic measurements made near street

level on tripods (22.5 m agl), towers (2.53.5 m) and on light

poles (7.58 m agl) from IOPs 2, 8, and 9. Note that in several

locations two anemometers were placed at the same x, y location,

but a meter or so apart vertically. In the vector plots to follow, this

explains what appears to be two vectors at the same location in

several cases.

Towers and remote sensing sodars located upwind (i.e., south)of the CBD were used to create inflow wind profiles. The closest

upwind location was about 200 m due south of our modeling

domain. A Dugway Proving Ground (DPG) propeller anemometer

was located at that location on a tower 35 m above the ground

and 25 m above the roof of a post office building. The anem-

ometer was not operating, however, during IOP 2. Eight sonic

anemometers from Indiana University (IU) were located between

2 and 80 m above ground level on several towers about 5 km

south of our domain. For IOP2, only two sonics provided informa-

tion, however. A sodar from the Pacific Northwest National

Laboratory (PNNL) located about 1 km south-southwest of the

CBD was used for the winds above 100 m. The Argonne National

Laboratory (ANL) sodar located in the Botanical Gardens in the

southwest corner of our domain also provided reasonable inflow

information if the winds were from the southwest, but not if they

were from the southeast (probably due to larger buildings being

directly southeast of the sodar). In addition, we used two other

ANL sodars that were downwind of the city to cross correlate with

the upwind measurements.

3. Description of computational models and model set-up

In this study, we have used three different urban wind models,

each representing a different level of complexity: an empirical-

diagnostic code, a simple Reynolds-averaged NavierStokes (RANS)

code, and a large eddy simulation (LES) codeall implemented as an

option in the Quick Urban & Industrial Complex (QUIC) dispersion

modeling system (Nelson and Brown, 2006).

3.1. Empirical-diagnostic wind solver

The QUIC-URB (Updated Rockle-style Building-aware) wind

solver produces a mass-consistent flow field and uses empirical

parameterizations to produce a velocity field that maintains

important features of the time-averaged flow around buildings.

The wind solver is based on Rockle (1990) in which various

empirical relationships based on the building height, width, and

length and the spacing between buildings are used to initialize

the velocity fields in the regions around buildings (e.g., upwind

rotor, downwind cavity and wake, street canyon vortex, and

rooftop vortex). This initial flow field is then forced to satisfy

mass conservation. More information about the QUIC-URB windsolver and modifications made to the code for high density urban

areas can be found in Pardyjak and Brown (2003), Singh et al.

(2008), Brown et al. (2009), and Gowardhan et al. (2010). Version

5.4 of the QUIC-URB wind solver has been evaluated in this study.

3.2. RANS computational fluid dynamics model

The Q-CFD(RANS) solver is based on the 3D Reynolds-Aver-

aged NavierStokes (RANS) equations for incompressible flow

using a zero equation (algebraic) turbulence model based on

Prandtls mixing length theory (Gowardan et al., in press). The

selection of zero-equation turbulence model was made so as to

reduce the run time of the CFD simulation, and therefore making

it more closely adapted for a fast-response application (Chen and

Xu, 1998). Computational time using a zero-equation model can

be reduced by 28 times from that using a more complex

turbulence modelkeeping all other computational settings such

as discretization schemes and mesh size the same (REF). It is

accepted however, that more complex turbulence models could

also be considered, however, given that there has been no

evidence of clear superiority or appropriateness of one model

over another for all wind flow applications in real urban geome-

tries (REF), using a zero-equation model was considered accep-table in the context of this exercise.

The governing RANS equations are solved explicitly in time

until steady state is reached using a projection method. At each

time step of the projection method, the divergence-free condition

is not strictly satisfied to machine precision levels, but rather

when steady state is reached incompressibility is recovered. This

makes the method comparable to the artificial compressibility

method (Chorin, 1967). The RANS equations are solved on a

staggered mesh using a finite volume discretization scheme that

is second-order accurate in space (central difference) and time

(AdamsBashforth). The law-of-the-wall was imposed at all of the

solid surfaces. The pressure Poisson equation was solved using

the successive over-relaxation method (SOR). A free slip condition

was imposed at the top boundary and the side boundaries, while

an outflow boundary condition is used at the outlet. More

information on the numerical scheme and parameterizations

can be found in Gowardhan et al. (in press).

3.3. LES computational fluid dynamics model

In large eddy simulation (LES) application of a spatial filter to

the NavierStokes equations is generally used to partition the

solution space into resolved and subgrid parts. The large and

energetic scales of turbulence are calculated explicitly, while the

small scales are represented by a model. The QUIC-LES code uses the

Smagorinsky subgrid-scale eddy viscosity model. The NavierStokes

equations are discretized using the second-order QUICK scheme

in space and a second-order AdamsBashforth scheme in time ona staggered grid using a finite volume technique. The algorithm is

based on the fractional step method (Kim and Moin, 1985). The

Poisson equation for pressure p is solved by a multigrid method.

Inflow velocity profile is specified at the inlet and outflow boun-

dary condition is specified at the outlet, while the boundary

condition at the top of the domain is free slip. At all solid surfaces,

the local profile of the tangential velocity component is taken to

be logarithmic and the normal velocity component is zero. The

LES code used in this exercise is based on the development

by Gowardhan et al. (2007) and has been validated for various

turbulent flow problems.

3.4. Model set-up

The modeling domain covered most all of the Oklahoma City

central business district (CBD) and was 1.2 km 1.2 km in size for

all model calculations (see Fig. 1); the horizontal grid size was

set to 5 m and the vertical grid size was 3 m resulting in a

236 242 64 grid cell domain (3.66 million cells in total) for all

models, with a blockage ratio of 15%. Such a computational set-up

(e.g. of this mesh and cell size) is considered typicalreflect-

ing realistic scenarios of fast-response applications (Boris et al.,

2001; Patnaik and Boris, 2007). It should be noted that this may

not necessarily be optimal for a standard application of CFD, e.g.

LES or RANS, as the focus of this study is rather the application of

these models in a realistic scenario of a fast-response situation for

a corresponding typical computational set-up for such situation

(Boris et al., 2001; Patnaik and Boris, 2007). Sensitivity studies

M. Neophytou et al. / J. Wind Eng. Ind. Aerodyn. 99 (2011) 357368 359


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were not performed due to the extremely high computational

cost. The 3D building data were obtained from the Defense Threat

Reduction Agency and the University of Oklahoma. Parking

garages were set as solid buildings and although there were some

trees in the downtown area, they were ignored in the simulations.

The inflow wind speed profile was created by fitting a log-law

to thirty-minute average wind measurements from the IU sonics,

the DPG prop-vane and the PNNL sodar measurements (see

Table 1). The specification of the inflow wind direction profile

was somewhat more difficult as the different instrumentation

often showed up to 301 differences in wind direction, with little

consistent bias between instrument location and time periods. To

determine the inflow wind direction profile, we plotted thirty-

minute average measurements from the IU sonics, the DPG prop-

vane, the PNNL sodar, and the three ANL sodars and specified the

wind direction based on where the majority of measurements fell,

ignoring outliers (similar to a modal average). Note that the

inflow profile for the LES calculation did not vary with time, so

that the inflow turbulence will be underestimated (e.g. Xie and

Castro, 2006, 2009; DeCroix and Brown, 2002). The QUIC-LES code

was run for 1800 s (physical time) for the flow to develop and the

results were then averaged over the next 1800 s (actual duration

of an IOP) so that it can be compared with the field data.

In order to test the models under different inflow conditions,

simulations were performed for three distinct time periods when

the ambient winds were from the south-southwest, the south, and

the south-southeast. These time periods corresponded to IOP-2,

Release 1 (10:0010:30 CST, July 2), IOP-8, Release 2 (00:0000:30

CST, July 25), and IOP-9, Release 2 (01:0001:30 CST, July 27). All

simulations were performed assuming neutral stability. Within the

urban canopy this assumption is likely valid, but above the canopy

there may be a stable layer during IOP8 and an unstable layer during

IOP2. Winds were fairly brisk during all three IOPs; hence, it is

likely that the stability was not too far from neutral. Additionally,

numerous studies have shown that a several hundred meter well-

mixed neutrally stratified layer often exists above larger-sized city

centers due to enhanced thermal and mechanical mixing effects. Thedowntown district of Oklahoma City is rather small, however, and

thus it is not clear if the well-mixed layer is deep or shallow.

4. Results and discussion

4.1. Overview

The results in this section are focused on the central part of the

domain, which corresponds to the core of the Central Business

District with dimensions roughly 400 m 500 m. This area con-

tains a good number of the near-surface wind measurements in

the most built-up region of the city. Additionally, from the

computational modeling point of view, it avoids regions nearer

the edges of the computational domain, which may be influenced

by the application of specific boundary conditions. Fig. 1 shows an

aerial view of the computational domain. An important feature of

the flow field determining pollutant transport is the resulting

wind flow directions, which subsequently determine flow struc-

tures (e.g. channeling, recirculation, etc). Therefore, we have focused

on several different types of flow regimes within the CBD: (i) flow

channeling regions, (ii) street intersections, (iii) sheltered regions,

and (iv) open areas. In the subsequent discussion and the modelinter-comparison, emphasis is placed on the ability of the model to

capture such flow features discerned from the wind measurements.

4.2. Comparative qualitative results

4.2.1. IOP2southwesterly inflow

For the IOP2 southwesterly inflow case (2151), the three

models agree qualitatively with measurements in several loca-

tions, and disagree in several other places (Fig. 2). Qualitatively

the flows created by the Q-CFD(RANS) and Q-LES codes look

smoother and are less noisy than the Q-URB solution. All three

models capture extremely well the west to east channeling along

Sheridan just north of the Convention Center, as well as the

change in direction of the flow from west-northwesterly to

southeasterly as one goes from the western side of the Broad-

way-Main St. intersection to the northern side. As one travels up

Broadway just past the Bank One Building, the Q-LES and

Q-CFD(RANS) models show much stronger south-to-north chan-

neling flow as compared to Q-URB. It appears that the CFD models

overestimate the magnitude in this region, while the empirical

Q-URB model underestimates it (this is discussed further in

subsequent sections). All three models show a downwind cavity

on the north side of the Bank One building (just to the west of

the Broadway-Park Ave. intersection), however the CFD codes

better represent the strength and direction of the single wind

measurement there.

There are some distinct differences between the models

near the RobinsonSheridan intersection in the SW sector of

the domain. First, in the southwestern corner of Fig. 2 plots, in

the open region immediately west of the Convention Center, the

Q-LES model has produced near zero winds, the Q-URB model

shows the flow in this region being virtually identical to the

ambient SW inflow, and Q-CFD(RANS) shows flow in the same SW

direction, but it has been significantly retarded. Although there is

only one measurement in this region it appears that the Q-URB

and Q-CFD(RANS) codes have best matched the flow direction,

and Q-CFD(RANS) has best matched the wind speed. The stagna-

tion region produced by the Q-LES model appears to have resulted

from strong southerly backflow on the front faces of tall buildings

a few blocks north that are just outside Fig. 2 western plot

boundary.

Just to the north near the RobinsonMain St. intersection,the Q-CFD(RANS) model appears to be doing the best, capturing

the westerly flow along Main St. and the southerly flow along

Robinson. The Q-LES model also has the westerly flow penetrating

through the intersection and matching the measurement along

Main St., however, it also shows weak northerly winds on

Robinson at the measurement location, not the relatively strong

southerly winds measured there. Q-URB, on the other hand, is not

able to propagate the westerly flow through the Robinson-Main

St. intersection, but does correctly produce the southerly winds to

the immediate north of the intersection on Robinson.

Further north along Robinson at the Park Ave. cross street, the

Q-CFD(RANS) model is seen to compute the merger of the south-

erly flow along Robinson and the westerly flow along Park Avenue

exceptionally well. The Q-LES model also does well, but appears

Table 1

The inflow wind profiles used to describe the different IOP cases in the numerical

models.

Test case: IOP2

Wind inlet profile (logarithmic) Uref5 m/s, zref50 m, wind angle 215;

Surface roughness z00.6 m

Test case: IOP8

Wind inlet profile (logarithmic) Uref8.5 m/s, zref80 m, wind angle 165;

Surface roughness z01 m

Test Case: IOP9

Wind inlet profile (logarithmic) Uref7 m/s, zref50 m, wind angle 180;

Surface roughness z01 m



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to overestimate the strength of the westerly flow to the west of

the intersection and underestimate the strength of the southerly

flow to the south of the intersection. Q-URB actually matchesthe wind directions fairly well point by point in the Robinson

Park Ave. intersection, but the flow field in this region is

unsatisfactorily noisy.

Within the western half of the Park Ave. street canyon

between Robinson and Broadway, the Q-URB model has the flow

going down the street the right way (westerly) at about the right

magnitude, but individual flow vectors can be off by ninety

degrees. Both the Q-CFD(RANS) and Q-LES models capture the

flow direction well on the western half of Park Ave., but the

strength of the flow is overestimated. The Q-CFD(RANS) code is

able to resolve the easterly reverse flow at the eastern end of the

street canyon, whereas the Q-LES model incorrectly shows the

westerly flow extending through the entire length of the street

canyon.

The Q-LES model appears to perform better on the northern

side of the domain. The backflow produced by Q-LES along McGee

Ave. matches the measurements at McGee & Broadway and atMcGee & Robinson (note that there are tall buildings immediately

north beyond the plot boundary). Both Q-URB and Q-CFD(RANS)

are off by 901 at these two locations. It appears that the models

slightly underestimated the spatial extent of the backflow as

only a few grid cells away the model-computed flow is in the

right direction. Another big difference between Q-URB and the

two CFD codes is apparent in the northeast quadrant downwind

of the wide building running along Kerr Avenue between Broad-

way and Gaylord. Here, both CFD models show a rather short

downwind cavity region, whereas Q-URB shows relatively strong

backflow. Similarly, the long rectangular building directly south

parallel to Gaylord shows southerly flow on the downwind side

near the back wall, whereas Q-URB shows a thin region of

northerly flow.

Fig. 2. Measured (in black) and predicted (in gray) velocity field in the Central Business Area using QUIC-URB (a), Q-CFD(RANS) (b) and Q-LES (c) for IOP2 (wind direction:

2151).



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4.2.2. IOP8south-southeasterly inflow

For the IOP8 south-southwesterly inflow case (1651) depicted

in Fig. 3, the three models do fairly well reproducing the flow

along Sheridan near the Convention Center. The flow is straigh-

tened to a pure southerly flow east of the Convention Center on

Gaylord Blvd., is close to background flow direction to the west of

the Convention Center on Robinson, and shows the easterly

backflow in the Park Ave.Sheridan intersection immediately

north of the Convention Center. The strength of the backflow offof the building immediately north of the Convention Center

(x750, y 575) is weakest for Q-URB and strongest for Q-LES.

All three models show southerly channeling along sections of

Broadway, but there are differences in the strength of the flow,

where the southerly flow is interrupted, and whether the south-

erly flow covers the entire width of the street. The Q-LES model,

for example, shows westerly flow penetrating through the Main

St.Broadway intersection (in contradiction to the measurements

along Main St. near the intersection), while the Q-CFD(RANS)

shows the entire intersection consisting of southerly flow and

Q-URB has the western half of the street with southerly flow and

the eastern half being northerly. All of the models have some level

of disagreement with the measurements along Main St., in some

cases being 901801 out of phase.

The channeling of the flow on Broadway north of the

BroadwayMain St. intersection, the reverse flow in the lee ofthe Bank One Building, and the penetration of the flow into Park

Ave. seems to be best captured by the Q-CFD(RANS) model,

although the Q-LES does a fair job as well. Q-URB completely

misses the reverse flow on the downwind side of the Bank One

Building, but other than that appears to get the wind directions

more or less correct in this region. On the western side of

Park Ave there is clearly divergent flow resulting from

Fig. 3. Measured (in black) and predicted (in gray) velocity field in the Central Business Area using QUIC-URB (a), Q-CFD(RANS) (b) and Q-LES (c) for IOP8 (wind direction:

1651).



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south-southeasterly flow impinging on the front face of the

relatively tall City Place building. The CFD models capture this

feature best, although Q-URB also captures some aspects of

the flow.

The flow computed by all three models near the Robinson

Main St. intersection is in good agreement with the two measure-

ments there. Further north, at the RobinsonPark Ave. intersec-

tion, the southerly flow is also well predicted by all three models

and in the case of the two CFD models the easterly outflow fromPark Ave as well. All three models, however, incorrectly show a

southerly flow through the RobinsonKerr intersection, whereas

the wind measurement indicates an easterly flow there.

The southerly flow measured near the BroadwayKerr intersec-

tion is captured by the Q-URB and Q-LES models, but the Q-

CFD(RANS) code shows easterly flow there. Further north, at the

BroadwayMcGee intersection, the Q-LES model is in best

agreement to the weak westerly flow measured there, while

Q-URB and Q-CFD(RANS) are both off by 901 with a strong

southerly flow.

4.2.3. IOP9southerly inflow

This case with winds coming from 1801 proved to be fairly

difficult for Q-URB, while the Q-CFD(RANS) code appears to have

done fairly well and the Q-LES somewhere in between ( Fig. 4). Atthe BroadwaySheridan intersection just north of the Convention

Center, the Q-URB model has likely overestimated the length of

the downwind cavity reverse flow region (however, the measure-

ments are inconclusive as one of the measurements at this

location shows weak reverse flow, while the other does not).

Q-LES shows a combination of westerly channel flow, backflow off

the tall building to the north (x 750, y 575), and weak reverse

Fig. 4. Measured (in black) and predicted (in gray) velocity field in the Central Business Area using QUIC-URB (a), Q-CFD(RANS) (b) and Q-LES (c) for IOP9

(wind direction:1801).



8/12

flow in the lee of the Convention Center in reasonable agreement

with the measurements. Q-CFD(RANS) shows similar features as

the Q-LES calculation, except that the westerly channeling is

stronger and no evidence of reverse flow is apparent immediately

downwind (north of) the Convention Center.

Both Q-URB and Q-LES incorrectly predict southerly flow

through the BroadwayMain St. intersection, while Q-CFD(RANS)

correctly computes the easterly flow seen there (resulting from

strong street-level backflow off the front face of the tall Bank Onebuilding). All three models are in agreement with the strong

southerly channeling north of the BroadwayMain intersection.

Both Q-CFD(RANS) and Q-LES predict backflow on the lee (north)

side of the Bank One building in agreement with measurements,

but the flow field circulation is very different in each case. Q-URB

computes some backflow in this region, but the locations of the

backflow do not agree with the measurements.

Both Q-LES and Q-CFD(RANS) capture the penetration of the

winds from Broadway into Park Ave as depicted in the measure-

ments, while Q-URB does not. Q-CFD(RANS) is the only model

that shows the end vortex in this region in agreement with the

easterly flow on the north side of the street and westerly flow on

the southern side. Both Q-LES and Q-CFD(RANS) agree with the

measurements showing westerly flow in the center of the street

canyon and both have produced a counter rotating vorticity in the

street canyon near the RobinsonPark Ave intersection (although

it is not clear from the measurements if such a vortex exists

there). Overall, Q-URB performed poorly in Park Ave.

The southerly winds along Robinson, however, were well

predicted by Q-URB, as well as Q-LES. Q-CFD(RANS) did well also,

but incorrectly computed a westerly wind at the RobinsonMain

St. intersection, whereas the measurement revealed a southerly

wind. All three models incorrectly predicted a southerly wind at

the RobinsonMcGee intersection, whereas the actual wind was

easterly. The direction of the wind one block to the east, also on

McGee, was correctly computed by Q-CFD(RANS), but not by

Q-URB and Q-LES. It is obvious that Q-URB has severely over-

estimated the southerly channel flow along Broadway resulting in

the incorrect wind flow at the McGeeBroadway intersection.

Overall, it could be noted that the models have reasonable

agreement with measurements in some locations and significant

differences in others. Although an attempt has been made to

group the zones of weak agreement and provide possible reasons

for the discrepancies, due to the relatively limited number of

points, areas and cases we avoided to deduce a more generalized

conclusion on such issue of stronger/weaker agreement and their

possible related explanation. For instance, the inherent transient

effects of urban aerodynamics are predominant in certain loca-

tions than others and that could explain perhaps some discre-

pancies especially for RANS.

4.3. Comparative quantitative results

A quantitative comparison has been made by examining

various wind scatter plots produced by the three models. An

important feature that is examined here is the wind direction, in

addition to wind speed, as this determines substantially the flow

pattern as well as features produced within the urban area, which

thereby determine the transport and dispersion. Figs. 5 and 6

show the scatter plots for the wind speed and wind direction for

each of the 3 IOP cases IOP2, IOP8, IOP9, respectively and

depict the bounds for the 710%, 25%, 50% and 100% percentage

error for wind speed as well the bounds within 7151, 301, 451and 901 for wind direction. From these plots, the percentage of the

model predictions within the specified error bounds was calcu-

lated. The results from each IOP case and each model are listed in

Table 2, while Table 3 lists the average results for all three IOP

cases for each model and Table 4 lists the correlation coefficients

(between observed and predicted) derived from each scatter plot

(each case and model).

As indicated in Tables 2 and 3, the RANS code was within 50%

of the measured wind speed 62% of the time (on average

considering all three IOP cases, ranging from 57% in IOP9 to 71%

IOP2) as compared to 53% for the LES model (on average

considering all three IOP cases, ranging from 41% in IOP2 to 60%in IOP8) and 49% for the empirical-diagnostic code (on average

considering all three IOP cases, ranging from 47% in IOP2 to 51%

IOP8). Considering all the cases individually, there does not seem

any one proving consistently more difficult than others for the

models to capture. In turn, considering the level of quantitative

differences in the computed errors, no model seems to be

performing clearly better than other in predicting the wind speed.

Similarly, for wind direction, the RANS-CFD code was within 301of the measured wind direction 58% of the time (on average

considering all IOP cases, ranging from 54% in IOP8 to 63% in

IOP2) as compared to 50% for the LES code (on average consider-

ing all IOP cases, ranging from 50% in IOP9 to 57% in IOP2 and

IOP8) and 43% for the empirical diagnostic code (see Tables 2

and 3). If stricter bounds are considered, for example wind speed

errors within 25%, the RANS-CFD was within this error threshold

33% of the time, the LES model 29% and the empirical-diagnostic

code 28%, considering all three IOP cases on average. For the average

wind direction error, the model predictions were within 151 of the

measured wind direction 39% of the time for the RANS-CFD code, as

compared to 34% for the LES code and 27% for the empirical

diagnostic code. For cases where the plume is being channeled down

a street one direction or 1801 in the other direction, wind errors at

street-level of up to7601 can in many cases actually be considered a

success for street-level plume transport and dispersion since the

plume will be transported in the right direction within a street

canyon if the winds are within this error bound. That is, for

successful plume model transport one may consider whether the

model computations have the same easterly or westerly component

as the wind measurements in an eastwest running street or the

same northerly or southerly component in a northsouth running

street. It is interesting to note that although some of the vector plots

may show a fairly good agreement (in the flow patterns), it appears

that a relatively good agreement in vector plots does not necessarily

mean a good scatter plot for wind speeds. For example, although

the values of the correlation coefficients for wind direction may

reach as high as 0.860 (that is for Q-URB in the IOP 2 case), for

exactly the same case, the same model predictions produce a

correlation coefficient for the wind speeds as low as 0.068

(see Table 4). Given the range of correlation coefficient values across

the various cases, it appears that wind directions are overall better

captured than the wind speeds. All the correlation coefficients

between the observed and simulated values for each presented

scatter plot (of Figs. 5 and 6) are listed in Table 4.It is important to consider that given the natural variability

and uncertainties in the field, these levels of quantitative differ-

ences in the various computed statistical measures do not support

clearly a better performance of one model over others. It is also

worth noting the computational times required to run the

corresponding code. For example, the computational time for

the simulations using the Q-URB code for the three IOP cases were

of the order of 1 min, for the Q-CFD(RANS) of the order of 30-min

and for the Q-LES 30 h using a standard PC for Q-URB and Q-

CFD(RANS) and a parallel cluster of 8 nodes for the Q-LES.

Although the accuracy of Q-URB may not appear as good as

that of the Q-CFD(RANS) and Q-LES codes, it is only by a rela-

tively small percentage fraction when compared to the two

CFD codes.



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Moreover, accounting for the inherent uncertainty and natural

variability in the wind field, it is difficult to argue in some cases

(especially when differences in percentage fraction of successes

are of the order of a few percentage points) for clear superiority of

one model over another. Especially, with regard to evaluationof urban wind models, it is necessary to specify the purpose of

model application and thereby judge its appropriateness through

specific performance evaluation criteria and metrics (COST732,

2007). For example, given that the empirical diagnostic code runs

23 orders of magnitude faster and its performance in the

quantitative comparisons, the empirical diagnostic code appears

to be a viable option for applications where fast turnaround time

is required or where many cases must be run.

It is important to note that for the purpose of consistent

comparisons, the LES code is most likely run under sub-optimal

conditions, e.g., the grid cell size near the walls should be smaller

and the inlet boundary conditions should be fully turbulent and

match the atmospheric conditions during the experiment. It is the

case, however, that conditions and parameters representative of

the field and necessary as input to the LES code are often not

available from the available field measurements and in some

cases the needed grid resolution is not operationally plausible.

5. Concluding remarks

Three computational wind models of different level of com-

plexity were systematically tested and compared with wind

measurements from the Oklahoma City Joint Urban 2003 field

experiment. The comparative exercise included only near-street

level data collected within the Central Business District (between

2 and 7.5 m) using 30 min averages. The size of the input domain,

grid resolution, building dimensions, wind inflow profiles and

other relevant parameters within the different codes were

selected to reflect fast-response modeling needs and were matched

to allow for consistent comparisons.

Overall, the results show that qualitatively all three models

compare favorably to the near-surface wind measurements in

IOP2 QUIC-URB

IOP2 QUIC-RANS

IOP2 QUIC-LES

IOP9 QUIC-URBIOP8 QUIC-URB

IOP9 QUIC-RANSIOP8 QUIC-RANS

IOP9 QUIC-LESIOP8 QUIC-LES

Fig. 5. Scatter plots for wind speed for measurements and corresponding predictions in the Central Business Area using QUIC-URB (top), Q-CFD(RANS) (middle) and Q-LES

(bottom) for IOP2, IOP8, IOP9 (left, center, right, respectively). The plotted lines show the bound lines for percentage error within 710%, 725%, 750%, 7100%.



10/12

IOP2 QUIC-URB

IOP2 QUIC-RANS

IOP2 QUIC-LES

IOP9 QUIC-URBIOP8 QUIC-URB

IOP8 QUIC-RANS IOP9 QUIC-RANS

IOP9 QUIC-LESIOP8 QUIC-LES

Fig. 6. Scatter plots for wind velocity direction for measurements and corresponding predictions in the Central Business Area using QUIC-URB (top), Q-CFD(RANS) (middle)

and Q-LES (bottom) for IOP2, IOP8, IOP9 (left, center, right, respectively). The plotted lines show the bound lines for wind direction error within 7151, 7301, 7451 and

7901.

Table 2

Calculated percentage error for each model in each IOP case, for wind speed and wind direction; the symbols OBS and SIM represent the observed and simulated/predicted

values, respectively.

Wind speed error, Error9OBSSIM9

0:5 OBS SIM

Wind direction error, Error 9OBSSIM9

o100% o50% o25% o 10% o901 o451 o301 o151

IOP 2

Q-URB 71% 47% 27% 14% 77% 65% 48% 32%

Q-CFD 88% 71% 37% 14% 84% 75% 63% 38%

Q-LES 82% 41% 27% 10% 77% 68% 57% 38%

IOP 8

Q-URB 85% 51% 35% 13% 77% 52% 43% 30%

Q-CFD 85% 58% 33% 16% 86% 59% 54% 38%

Q-LES 87% 60% 18% 5% 77% 68% 57% 38%

IOP 9

Q-URB 79% 48% 23% 4% 77% 57% 39% 20%

Q-CFD 90% 57% 30% 13% 91% 68% 57% 41%

Q-LES 84% 57% 43% 23% 91% 70% 50% 34%



11/12

many locations, although there are several instances of winds

being calculated poorly in specific locations. This qualitative

assessment was bolstered by the point-by-point quantitative

comparisons of the wind speed and wind direction. The RANS-

CFD code, for example, was within 50% of the measured wind

speed 62% of the time as compared to 53% for the LES model and

49% for the empirical-diagnostic code. For wind direction, the

RANS-CFD code was within 301 of the measured wind direction

58% of the time as compared to 50% for the LES code and 43% for

the empirical diagnostic code. It should be pointed out that there

were noticable differences between the wind fields produced by

the LES and RANS-CFD codes in specific regions of the domain,

especially in intersections, in the strength of backflow resulting

from flow impingement on the front faces of buildings, and the

strength of channel flow. It is important to consider that given the

variability and uncertainties in the field, the level of quantitative

differences in the computed errors do not support a clearlysuperior performance of one model over others. However, it is

interesting to note that the simpler model seems to perform

almost nearly as well as the more computationally demanding

models. It should also be noted that the computational set-up

selected for all the models reflects typical realistic settings and

requirements for fast-response modeling and may not necessarily

reflect optimal settings and performance, particularly for RANS

and LES in more general applications. Moreover, accounting for

the inherent uncertainty and natural variability in the wind field,

it is difficult to argue in some cases for clear superiority of one

model over another. Given that the empirical diagnostic code is

23 orders of magnitude faster than the RANS and LES-CFD codes,

it appears that in this context it can be considered a practical

option for studies where time is of concern. Future work on the

comparison of model-computed and measured concentrations

from the Joint Urban 2003 tracer field study using the three

different types of wind models to drive a plume transport and

dispersion model will help to answer whether or not the differ-

ences in the wind fields translates into significant differences in

the concentration fields.

Acknowledgements

Dr. M. Neophytou wishes to acknowledge financial supportgranted by the United Nations Educational, Science and Cultural

Organization (UNESCO) for a 6-month exchange visit to US during

which this research has been conducted. Dr. Neophytou wishes

also to acknowledge the Systems Engineering and Integration

Group of Los Alamos National Laboratory (LANL) for hosting and

facilitating her 3-month visit at LANL.

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Q-CFD 0.644 0.618

Q-LES 0.653 0.755

IOP 9

Q-URB 0.356 0.402

Q-CFD 0.561 0.802

Q-LES 0.513 0.784



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An Inter-comparison of Three Urban Wind Models Using Oklahoma City Joint Urban 2003 Wind Field Measurements