P1.21
IMPLEMENTATION OF AN URBAN CANOPY PARAMETERIZATION IN MM5
FOR MESO-GAMMA-SCALE AIR QUALITY MODELING APPLICATIONS
Tanya L. Otteu and Avraham Lacser2
NOAA Air Resources Laboratory, Research Triangle Park, North Carolina
10n assignment to the National Exposure Research Laboratory, US EPA, Research Triangle Park, NC.
2 On leave from Israel Institute for Biological Research, Ness Ziona, Israel.
1.	introduction
The U.S. Environmental Protection Agency
{U.S. EPA) is extending its Models-3/Community
Multiscale Air Quality (CMAQ) Modeling System to
provide detailed gridded air quality concentration
fields and sub-grid variability characterization at
neighborhood scales and in urban areas. CMAQ
is an advanced air quality modeling system that
embodies a "one-atmosphere," multiple-pollutant
philosophy (Byun and Ching, 1999). There are
three primary models within Modeis-3/CMAQ:
meteorology, emissions, and chemistry. The
meteorology model used with CMAQ in this
application is the Pennsylvania State
University/National Center for Atmospheric
Research (PSU/NCAR) Mesoscale Model (MM5;
Grell etal. 1994). For fine-scale urban simulations
(~1-km grid spacing), MM5 has been modified to
include an urban canopy parameterization that
accounts for drag exerted by the urban structures,
the enhancement of turbulent kinetic energy
(especially near the top of the buildings), and the
energy budget at the street and roof levels. This
refinement of MM5 is targeted to provide CMAQ
with the means to capture the details of pollutant
spatial distributions at these scales.
One of the goals of this research is to
demonstrate the capability of MM5 to simulate the
effects of urban areas at the meso-gamma scale.
This paper describes the suggested modifications
to MM5 and presents preliminary results of using
the urban canopy parameterization.
2.	Urban Canopy Parameterization
To consider urban effects in a numerical
model with horizontal grid spacing on the order of
1 km, some parameterization is suggested to
account for effects of the urban canopy (e.g.,
buildings) on the flow and on the heat budget.
This urban canopy parameterization is applied to
grid cells in MM5 that have a non-zero fraction of
urban land use.
2.1 Momentum
The horizontal components of the momentum
equations were modified to account for the area
average effect of the sub-grid urban elements
following Brown (2000). The modifications were
implemented in MM5 via the TKE-based Gayno-
Seaman PBL parameterization scheme (e.g.,
Shafran etal. 2000). The momentum equations
accounting for the urban elements are:
fpFu -0.5UCaA(z)U {J2 +V2f5
fpFv-05fU!bCdA(z)V (J2 +V2f*
= Fm£ + 0.5UCdA(z)(u2 +V2 + W2f
where F are the general forcing terms in each
equation; U, V, and W are the wind components;
and TKE is the turbulent kinetic energy. In this
formulation, it is assumed that the buildings affect
the flow, but do not take up any volume within the
grid cell. Cd is a drag coefficient (assumed to be
constant and set to 1). The urban fraction of the
grid cell is described by A(z) is the canopy
area density, or the surface area of the obstacle
(e.g., building) perpendicular to the wind, per unit
volume of the urban canopy, expressed in nrf1.
There are several approaches to describe A(z)
(e.g., Uno etal. 1989, Brown 2000) where the
function reaches its maximum at the ground level,
but vanishes at the top of the obstacles (so the
drag term vanishes also at that level). The integral
of A(z) from the ground level to the top of the
buildings is "kf, which corresponds to the ratio of
the frontal area to the total surface area of the
+ Corresponding author address: Tanya L. Otte, U.S.
EPA/NERL/AMD, Mail Drop 80, Research Triangle
Park, NC 27711; e-mail: tlott0@hpcc.epa.gov

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buildings. In general A(z) is a function of the
location within the domain as it depends on
building morphology. Ratti etal. (2001) suggests
a method of obtaining and building height (H)
from digital imagery. A(z) can then be estimated
from lf and H. For simplicity, we initially assumed
a linear functional form for <4(z),
To solve the modified momentum equations
(the added new term) we followed the analytical
solution suggested by Byun and Arya (1986). The
TKE equation was solved explicitly. To take
proper account of the influence of A(z), the
vertical resolution in MM5 was increased in the
domain where the urban canopy was applied such
that several prognostic layers are below H.
2.2 Energy Budget
To account for the impact of urban settings on
the energy budget, the anthropogenic heat flux is
included in the heat equation and not in the
surface energy budget (e.g., Chin etal. 2000).
The heat equation also includes the heat
contribution of the rooftops and city canyons. The
surface energy balance will include in the urban
part the effect of the net radiation reaching the
ground in the city canyons. The energy budget
has not yet been modified for the urban canopy.
3. Preliminary Runs
The unmodified MM5 was run in a one-way
nested mode for several days in July 1995 during
which there was a high-ozone episode in the
Northeastern U.S. that coincided with a
photochemical field study (e.g., Seaman and
Michelson 2000). The MM5 domains included a
five-domain configuration (108, 36,12, 4, and 1.33
km horizontal grid spacing) with 30 vertical layers
(about 12 layers in the PBL, and lowest level at
19 m) and physics options appropriate for each
resolution. As expected, the initial 1,33-km runs
with the urban canopy showed that "buildings"
below the lowest prognostic level did not have an
impact on the flow. Thus, the 1,33-km domain
was run with the urban canopy parameterization
on a 40-layer configuration to include smaller
obstacles and better simulate the increase in TKE
near the tops of the buildings. The 40-layer run
included 10 new layers in the lowest 100 m
(lowest level at 2 m). Initially, the urban canopy
assumed H = 40 m and kf= 0.4 for urban areas.
In this application, MM5 is using the 24-category
USGS land use database that has only one urban
category (Figure 1); ideally, urban areas should be
represented by several sub-categories. The 12-
and 4-km runs provide a baseline for comparing
the 1,33-km simulation with and without the urban
canopy to observational data.
Figure 1. The 1,33-km MM5 domain covering the
Philadelphia metropolitan area, including parts of
Pennsylvania, New Jersey, Delaware, and
Maryland. Grid cells with "urban" land use are in
black; all other land use categories are in gray.
Figure 2 shows the difference between TKE
fields from two 40-layer, 1,33-km MM5 runs with
and without the initial urban canopy
parameterization (urban canopy minus standard).
The TKE fields are from the eighth horizontal level
above the surface (-35 m), selected to be within
the canopy and near the tops of the buildings (H).
The frame is valid 1500 UTC 14 July 1995. As
intended, the TKE increased with the urban
canopy parameterization specifically over the
urban areas (cf. Figures 1 and 2). Also as
expected, there is a net decrease in wind speed
within the urban canopy (Figure 3), and the impact
of the urban area is felt in the surrounding areas.
Figure 4 shows the vertical profiles of the TKE
and wind speed differences (from the same two
runs as in Figures 2 and 3) in the lowest 300 m
AGL as a function of time at a point within the
urban domain. In both sets of profiles, the
absolute maximum difference occurs near the tops
of the buildings (H), and the impact of the change
decreases through the canopy and toward the
surface. This tendency In the TKE and wind
speed fields is similar to wind tunnel

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measurements of a simulated urban area (e.g.,
Kastner-Klein and Rotach, 2001).
Figure 5 shows the wind speed profiles from
the model simulations with and without the urban
Figure 2. Difference of TKE field, 8 levels above
the surface (within the urban canopy and near the
top of the buildings) between 1.33-km runs with
and without the urban canopy parameterization.
Valid 1500 UTC 14 July 1995, 15 hours into the
simulation. Positive TKE differences with the
urban canopy parameterization are in white;
negative differences are in gray. Field values
range from -0.30 to 4.75 m2 s .

Figure 3. Same as Figure 2, but for wind speed.
Decreases in wind speed with the urban canopy
parameterization are in dark gray (absolute
decrease greater than 1.5 m s'1) and white (zero to
-1.5 ms"1); increases are shown in light gray.
Field values range from -5.50 to 0.89 m s"1.
canopy parameterization for the same grid point
shown in Figure 4. As suggested by Figure 4, the
urban canopy parameterization acts to reduce the
wind speed in the urban areas, and the maximum
reduction in wind speed is focused near the tops
of the buildings, H.
4. Future Work
The urban canopy parameterization will soon
be upgraded to include the influences of the
buildings and street canyons on the energy
budget. In addition, detailed comparisons against
observational data will be performed to assess the
viability of the urban canopy parameterization for
air quality modeling applications. Ultimately, the
urban canopy parameterization will be expanded
to use more detailed land use databases than
currently available in MM5 (e.g., the EPA's
National Land-Cover Date, Vogelman and
Wickham 2000) so that the urban areas can be
properly sub-categorized. In addition, the scheme
will be modified to use better-resolved urban
morphology to identify urban terrain zones.
300
250
200
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100
50

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0.5
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1.5
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Figure 4. Differences in TKE (top panel) and wind
speed (bottom panel) between runs with and

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without the urban canopy parameterization at a
point in the center of the urban area in the 1.33-km
domain. These vertical profiles are for the lowest
300 m AGL in each simulation. The curves are
valid at 17 (a), 18 (y), and 19 (v) UTC
14 July 1995. Values greater than zero represent
a net increase in the field using the urban canopy
parameterization.
300
250
200
100
m/s
Figure 5. Wind speed profiles from the two model
simulations shown in Figure 4. The profiles from
the simulation with the urban canopy
parameterization are shown in dashed lines; the
standard parameterization is shown in solid lines.
The profiles represent 18 (v) and 19(c) UTC
14 July 1995.
Acknowledgments. We thank Drs. M. Brown
(LANL) and D. W. Byun (NOAA/ARL) for their
valuable suggestions. We also thank Dr. J. Pleim
(NOAA/ARL) for his thorough review.
Disclaimer. The information in this manuscript has
been funded by the United States Environmental
Protection Agency. It has been subjected to
Agency review and approved for publication.
Mention of trade names or commercial products
does not constitute endorsement or
recommendation for use.
5. References
Brown, M. J., 2000: Urban parameterizations for
mesoscale meteorological models. Mesoscale
Atmospheric Dispersion. Ed., Z. Boybeyi.
Byun, D. W., and S. P. S. Arya, 1986: A study of
mixed layer momentum evolution. Atmos.
Environ., 20, 715-728.
Byun, D. W., and J. K. S. Ching, 1999: Science
algorithms of the EPA Models3 Community
Multiscale Air Quality (CMAQ) Modeling
System. EPA/600/R-99/Q30.
Chin, H. S., M. L. Leach, and M. J. Brown, 2000: A
sensitivity study of the urban effect on a
regional scale model: an idealized case. Third
Symp. on the Urban Environment, August
2000, Davis, CA.
Grell, G., J. Dudhia, and D. R. Stauffer, 1994: A
description of the Fifth-Generation Penn
State/NCAR Mesoscale Model (MM5).
NCAR/TN398+STR, 138 pp.
Kastner-Klein, P., and M. W. Rotach, 2001:
Parameterization of wind and turbulent shear
stress profiles in the urban roughness layer. .
Preprints, Third Int. Conf. on Urban Air
Quality, March 2001, Loutraki, Greece.
Ratti, C., S. Di Sabatino, R. E. Britter, M. J. Brown,
F. Caton and S. Burian, 2001: Analysis of 3-D
urban databases with respect to pollution
dispersion for a number of European and
American cities. Preprints, Third Int. Conf. on
Urban Air Quality, March 2001, Loutraki,
Greece.
Seaman, N. L., and S. A. Michelson, 2000:
Mesoscale meteorological structure of a high-
ozone episode during the 1995 NARSTO-
Northeast study. J. Appl. Meteor., 39, 384-
398.
Shafran, P. C„ N. L. Seaman, and G. A. Gayno,
2000: Evaluation of numerical predictions of
boundary layer structure during the Lake
Michigan Ozone Study. J. Appl. Meteor., 39,
412-426.
Uno, I., H. Ueda, and S. Wakamatsu, 1989:
Numerical modeling of the nocturnal urban
boundary layer. Bound-Layer Meteor,, 49,
77-98.
Vogelman, J. E., and J. D. Wickham, 2000:
Implementation strategy for production of
National Land-Cover Data from the Landsat-7
thematic mapper satellite. EPA/600/R-00/051.

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TECHNICAL REPORT DATA
5. report no.
2 .
3
4, TITLE AND SUBTITLE
'' Implementation of an urban canopy parameterization in MM5
for meso-gamma-scale air quality modeling applications
5.REPORT DATE
6.PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
Tanya L. Ottc-' and Avraham Lacscr*
8.PERFORMING ORGANIZATION REPORT NO.
I 9. PERFORMING ORGANIZATION NAME AND ADDRESS
'Same as Block 12
2Xsrael Institute for Biological Research, Nsss £io ns,
¦ Israel
10.PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
12. SPONSORING AGENCY NAME AND ADDRESS
U.S. Environmental Protection Agency
. Office of Research and Development
National Exposure Research Laboratory
Research Triangle Park, NC 27711
13.TYPE OF REPORT AND PERIOD COVERED
Proceedings, FY-01
14. SPONSORING AGENCY CODE
EPA/600/9
-15. SUPPLEMENTARY NOTES
16. ABSTRACT
The United Slates Environmental Protection Agency (U.S. EPA) is extending its Models-3/Community Multiscaie Air Quality (CMAQ) Modeling System to provide detailed
gridded air quality concentration fields and sub-grid variability characterization at neighborhood scales and in urban areas. CMAQ is an advanced air quality modeling system
that embodies a "one-atmosphere," multiple-pollutant philosophy. The meteorological model used with CMAQ in this application is the Pennsylvania State University/National
Center for Atmospheric Research (PSU/NCAR) Mesoscale Model (MM5). For fine-scale urban simulations (~S-km grid spacing), MM5 is being modified to include an urban
canopy parameterization that accounts for the drag exerted by the urban structures on the flow, the enhancement of turbulent kinetic energy especially near the top of the
buildings, and the energy budget at the street and roof levels. This refinement is targeted to provide CMAQ with the means to capture the details of pollutant spatial distributions
at these scaies.One of the goals of this research is to demonstrate the capability ofMMS to simulate Ihe effects of urban areas at the meso-gamma scale. Preliminary experiments
with the "off-the-shelf' MM5 were performed with various physics options in the coarseT domains (e.g., 36, 12, and 4 km) to provide a baseline for comparing with the 1.33-km
simulations. This paper will show sensitivities of the predicted meteorological variables to grid sizes and illustrate the utility of the urban canopy parameterization through
comparisons with measuremer.ts.Ultirr.ately, the urban canopy parameterization will be expanded to use more detailed land use databases than currently available in MM5, and it
will include better-resolved urban morphology to identify urban terrain zones.
17. KEY WORDS AND DOCUMENT ANALYSIS
a. DESCRIPTORS
b.IDENTIFIERS/ OPEN ENDED
TERMS
c.COSATI



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