4.4
INTRODUCTION OF URBAN CANOPY PARAMETERIZATION INTO MM5 TO
SIMULATE URBAN METEOROLOGY AT NEIGHBORHOOD SCALE
Sylvain Dupont1 U), Jason Ching \ and Steve Burian2
1NOAA Air Resources Laboratory (On assignment to NERL, U.S. EPA)
department of Civil & Environmental Engineering, University of Utah
1. INTRODUCTION
Since most of the primary atmospheric
pollutants are emitted inside the roughness sub-
layer (RSL) and consequently the first chemical
reactions and dispersion occur in this layer, it is
necessary to generate detailed meteorological
fields inside the RSL to perform air quality
modeling at high spatial resolutions. At
neighborhood scale (on order of 1-km horizontal
grid spacing), the meteorological fields are
strongly influenced by the presence of the
vegetation and building morphology of varying
complexity, which requires developing more
detailed treatment of the influence of canopy
structures in the models and using additional
morphological databases as input. The
assumptions of the roughness approach, used by
most of the mesoscale models, are unsatisfactory
at this scale. Hence, a detailed urban and rural
canopy parameterization (Dupont et al., 2003c),
called DA-SM2-U, has been developed inside the
Penn State/NCAR Mesoscale Model (MM5) to
simulate the meteorological fields within and
above the urban and rural canopies. DA-SM2-U
uses the drag-force approach to represent the
dynamic and turbulent effects of the buildings and
vegetation, and a modified version of the soil
model SM2-U (Dupont et al., 2003a and b), called
SM2-U(3D), to represent the thermodynamic
effects of the canopy elements. A first evaluation
of DA-SM2-U on the city of Philadelphia (USA)
(Dupont et al., 2003c) with a simple urban
morphology representation has shown that the
model is capable of simulating the important
features observed in the urban and rural areas.
The improvement of the urban canopy
representation in mesoscale models requires the
knowledge of more parameters. These parameters
can be divided into three categories: i) the
empirical parameters which are deduced from
calibration of the models; ii) the "material
Corresponding author address: U.S. EPA, E243-
03, NERL/AMD/AMDB/, Research Triangle Park,
NC 27711, USA; e-mail: dupont@hpcc.epa.gov.
parameters" which correspond to the physical
properties of the surface materials of the canopy
elements, they can be easily found in the literature
from tables; and iii) the morphological parameters
which depend on the structure and on the 3D
arrangement of the canopy elements (buildings,
vegetation, etc). The morphological parameters
are variable from one city to another, and need to
be averaged on few 100-m2 with a vertical
resolution of a couple meters to be used at
neighborhood scales. Thus, these parameters
may be the most difficult parameters to estimate.
Here, the DA-SM2-U version of MM5 is applied
to Houston, Texas (USA), in order to study the
influence of the morphological parameter
resolution on the meteorological fields to know if a
detailed resolution of these parameters is required
or not for simulating at neighborhood scales. To
provide the most accurate representation of these
morphological parameters for the entire MM5
computational domain, a Houston GIS Urban
Database has been created. This paper gives a
brief description of the DA-SM2-U model and of
the procedures used to create the morphological
parameters on Houston. The first results of the
influence study of the representation of the city of
Houston on the structure of the urban boundary
layer are presented.
2. THE DA-SM2-U VERSION OF MM5
The DA-SM2-U version of MM5 is able to
simulate meteorological fields within and above
the rural and urban canopy at small mesoscales
by using the drag-force approach coupled with the
thermodynamic canopy model SM2-U(3D). The
drag-force approach transmits directly to the
atmosphere the dynamic, thermodynamic and
turbulent effects of the canopy elements
(vegetation and buildings) by changing the
conservation equations of the mesoscale model.
The lower level of the computational domain
corresponds to the real level of the ground, and
additional vertical layers are included within the
canopy to allow more detailed meteorological
fields within the RSL (see Figure 1). Inside the
canopy, the effects of buildings and vegetation are
represented by adding i) in the dynamic equation a

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friction force induced by horizontal surfaces of
buildings, and a pressure and viscous drag force
induced by the presence of buildings and
vegetation, ii) in the temperature equation the
sensible heat fluxes due to buildings and
vegetation, and the anthropogenic heat flux
parameterized following Taha (1999), iii) in the
specific humidity equation the humidity sources
coming from the evapotranspiration of the
vegetation and the evaporation of the water
intercepted by buildings, and iv) in the turbulent
kinetic energy equation a shear production terms
induced by horizontal surfaces of buildings,
turbulent kinetic energy sources induced by the
presence of buildings and vegetation, and buoyant
production terms from the sensible heat fluxes
emitted by buildings and vegetation. The
turbulence length scale has been also modified
inside the urban canopy, as proposed by Martilli et
al. (2002), by adding a second length scale to
consider the vortices induced by the presence of
buildings; this modification has been also
extended to the vegetation. All of these new terms
are volumetric: the volume of buildings is
considered in each cell whereas the volume of the
vegetation is neglected. The turbulent transport in
the vertical is also modified to consider the real
volume of air in the cell.
The SM2-U(3D) model estimates the heat and
humidity fluxes emitted by the canopy elements at
different levels within the canopy, it corresponds to
an extended version of the soil model SM2-U
(Dupont et al., 2003a) to the drag-force approach.
DA-SM2-U is thus a multi-layer canopy and soil
model with few layers of a couple meters within
the canopy depending on the mesh of the
mesoscale model domain, and three layers within
the ground: a surface soil layer for the natural
surfaces, a root zone layer, and a deep soil layer.
This simple discretization of the ground allows the
model to estimate the soil humidity available for
the evapotranspiration with a good compromise
between the computational time and the accuracy
of the water budget evaluation. The sub-grid
variability is introduced in DA-SM2-U by
considering eight surface types in each canopy
grid cell. The total heat flux for a canopy grid cell is
thus determined by the average of individual heat
fluxes calculated for each surface type, weighted
by the fraction areas within the cell.
3. HOUSTON APPLICATION
Our study focuses here on simulating the
meteorology on Houston-Galveston area, Texas,
during the August 25 - September 1, 2000, period
which includes a portion of the Texas 2000 Air
Quality Study field program, characterized by high
temperatures and dry conditions favorable for the
production of ozone. MM5 Version 3 Release 4
has been run by Nielson-Gammon (2001) for this
period in a one-way nested including four nested
MM5 computational domains of 108-, 36-, 12-, and
4-km horizontal grid spacing. This last 4-km
simulation is used here as boundary conditions by
the DA-SM2-U version of MM5 to simulate
meteorological fields at 1-km horizontal grid
spacing (Figure 2).
To provide the most accurate representation of
the morphological parameters for the entire MM5
computational domain on Houston, a GIS Urban
Database has been created. This database
includes multiple surface topography and surface
cover digital datasets including land use, bare
earth elevation, full-feature digital elevation model,
roadway locations, and others.
The land use/land cover (LULC) dataset
selected to provide base level information in the
MM5 computational domain is the standard LULC
dataset available from the U.S. Geological Survey
(USGS). The USGS LULC dataset was compared
against the National Land Cover Dataset (NLCD)
and a site-specific dataset produced by the City of
Houston (COH). Both the NLCD and COH
datasets were based on more recent information
(1990s versus 1970s), but the COH dataset did
not cover the entire MM5 modeling domain and
the NLCD dataset did not have sufficient urban
land use types to attain the level of detail desired.
Therefore, the USGS was selected for this study.
High-resolution aerial photos from 2000 were used
to modify the land use type to correspond to more
recent conditions for 1653 km2 section of central
Harris County (Harris A in Figure 2), which
includes the downtown core area and the ship
channel industrial district.
The final USGS LULC dataset was classified
according to the level II classification scheme
described by Anderson et al. (1976), including 38
categories with 7 urban categories. Comparison of
the original USGS LULC dataset with the modified
dataset indicates that the amount of urban land
use increases substantially, while the amount of
cropland and pasture decreases about the same
amount because the area has urbanized
significantly during the 20+ years since the USGS
dataset was completed. The LULC dataset
provided a means to (1) estimate several surface
properties including fractions of land cover types
with each 1-km2 grid cell and (2) extrapolate
parameters from areas with sufficient data to
accurately compute the values to areas within the

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MM5 modeling domain that did not have sufficient
data to compute parameter values.
The base earth elevation and the elevation of
the top of canopy elements (vegetation, buildings
and other structures) were determined from the
airborne LIDAR (Light Detection and Ranging)
dataset obtained from TerraPoint LLC. LIDAR
technology produces x, y, z representation of
topography via airborne lasers. Data products are
created as even distribution of data points in
evenly-spaced grids. The LIDAR dataset covered
the Harris County area (Harris A + B in Figure 2)
with 1-m and 5-m horizontal grid cell spacing and
a horizontal accuracy of 15 to 20 cm RMSE and a
vertical accuracy of 5 to 10 cm RMSE. The canopy
data product was derived by subtracting the bare-
earth elevation dataset (Digital Terrain M - DTM)
from the non-ground data layer using the ArcView
map calculator. To distinguish the buildings from
the vegetation in the canopy data product, a
building footprint dataset has been created. A
building footprint dataset was obtained from the
COH and updated using the same high-resolution
aerial photos used to update the land use. Primary
updates required were the addition of buildings in
areas where the aerial photo indicated recent
development or redevelopment, and the deletion
of buildings in areas where the aerial photo
indicated that buildings did not exist. The original
COH building dataset within the 1653-km2 Harris A
area included 523,920 building footprints while the
modified building dataset contains 664,861
building footprints. The following parameters used
by DA-SM2-U have been calculated using the
integrated building, vegetation, and land use
datasets at 1-km2 horizontal resolution and 1-m
vertical resolution for 3D parameters for the entire
Harris A area:
•	Mean building height
•	Building plan area density,
•	Vegetation plan area density
•	Building rooftop area density
•	Vegetation top area density
•	Building frontal area density
•	Vegetation frontal area density
•	Wall-to-plan area ratio
•	Building height-to-width ratio
Following parameter computation in the Harris
A region the values were correlated to the
underlying land use type using area-weighted
averages. The average values for each parameter
for each land use type were then extrapolated to
each 1-km2 grid cell in the MM5 modeling domain
using an area-weighting scheme based on land
use fraction with the grid cell.
In addition to the parameters listed above,
numerous other parameters describing building
and vegetation morphology and surface cover
properties were also computed. See Burian et al.
(2003) for a summary of the parameter values and
a detailed description of the calculation methods.
4 Influence of the city representation
As shown in the last section, the process of
morphological parameters is complex and costly, it
is thus necessary to know if a detailed resolution
of these parameters is required or not for
simulating at neighborhood scales. Hence, two
simulations are performed on one day (August
31); they are referenced as "detailed city" and
"average city" cases. For the average city case,
the morphological parameters of the entire
computational domain are deduced from their
average values per land use type estimated in
Harris A, whereas for the detailed city case, the
morphological parameters in Harris A are
calculated individually for each grid cell without
considering the land use information. Thus, the
morphological parameters between the two cases
are different only in Harris A.
The Figure 3 compares the average surface
temperature (Ts) between the detailed and
average city cases at 4 p.m. and 12 a.m. At 4
p.m., Ts is the highest on the South and West
sides of the city, corresponding to dry soil with
small vegetation. The cooler surface of the city
can be explain by the shadowing effect and by the
presence of high vegetation, especially in the
residential areas. Ts of the detail city is much
more spatially heterogeneous (spatial variation:
~10 K) than the one of the average city (~3 K)
because the average representation of the city
smoothes the morphological parameter values
(not shown here). As expected the surface
temperature differences between the two cases
occur only on the urban part (Harris A). At
midnight, Ts of the city is higher than the one of
the rural areas (urban heat island), the urban
surfaces releasing the heat stored during the day.
The urban surface temperature differences
between the two cases represent only few
degrees, and the same degree of Ts spatial
heterogeneity is observed.
Hence, the detailed representation of a city in
atmospheric models increases the city

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heterogeneities which accentuates the spatial
heterogeneities of the surface temperatures,
especially during the day. The surface
temperature heterogeneity accentuates the mixing
inside the planetary boundary layer (PBL) by
increasing the values of the turbulent kinetic
energy (not shown here), and thus increases the
PBL height (Figure 4). The differences of PBL
height between the two cases can reach 300 m.
Disclaimer. This paper has been reviewed in
accordance with United States Environmental Protection
Agency's peer and administrative review policies and
approved for presentation and publication.
4. References
Anderson, J. R., E. E. Hardy, J. T. Roach and
R. E. Witmer, 1976: A land use and land cover
classification system for use with remote sensor
data, USGS Professional Paper 967, U.S.
Geological Survey.
Burian, S. J., W. S. Han, S. P. Velugubantla,
and S. R. K. Maddula, 2003: Development of
gridded fields of urban canopy parameters for
Models-3/CMAQ/MM5, report Department of Civil
& Environmental Engineering, University of Utah.
Dupont, S., E. Guilloteau, P. G. Mestayer, E.
Berthier and H. Andrieu, 2003a: Parameterization
of the urban water budget by using SM2-U model,
J. Appl. Meteor, (submitted).
Dupont, S., I. Calmet, and P.G. Mestayer,
2003b: Parameterization of the urban energy
budget by using the SM2-U model for the urban
boundary layer simulation, Bound.-Layer Meteor,
(submitted)
Dupont, S., T. L. Otte, and J. K. S. Ching,
2003c: Simulation of meteorological fields within
and above urban and rural canopies with a
mesoscale model (MM5), Bound.-Layer Meteor,
(submitted)
Martilli, A., A. Clappier, and M. W. Rotach,
2002: An Urban Surface Exchange
Parameterization for Mesoscale Models. Bound.-
Layer Meteor., 104, 261-304.
Nielson-Gammon, J. W., 2001: Initial Modeling
of the August 2000 Houston-Galveston Ozone
Episode, report to the Technical Analysis Division,
Texas Natural Resource Conservation Commision
December 19, 2001.
Taha, H., 1999: Modifying a mesoscale model
to better incorporate urban heat storage: A bulk
parameterization approach, J. Appl. Meteor. 38,
466-473.
Roughness
approach
Drag-Force
approach
Sensible

l^atent

Stomge

Anthropogenic
heat flux

heatflux

heat flux

heatflux
Precipitation
u tr ir
natural
I
aved
Infiltration
Draining
network
2 soil lay
3' soil lay
L'raining
Draining outside
the system
Return towards
equilibrium
Figure 1. Scheme of the new MM5 canopy parameterization, DA-SM2-U, using the drag-force approach
with the soil model SM2-U(3D), compared with the roughness approach.

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10 20 30 40 50 60 70 80 90 100 110 120 130 140
P.! ex ico
Harris A
10 20 30 40 50 60 70 80 90 100 110 120 130 140
MM5 1-km horizontal grid spacing domain
Morphological parameter
domain
Figure 2. MM5 1-km horizontal grid spacing computation domain on Houston-Galveston area.
Detailed city	4 p.m. Average city	4 p.m.
10 20 30 40 50 60 70 80 90 100 110 10 20 30 40 50 60 70
Detailed city
12 a.m. Average city
90 100 110
12 a.m.
10 20 30 40 5 0 60 7 0 80 90 100 110 ,10 20 30 40 50 60 70
90 100 110
Figure 3. Average surface temperature (K) at 4 p.m. and 12 a.m. for the detailed and average city cases.
Detailed city
4 p.m. Average city
4 p.m.
10 20 30 40 5 0 60 7 0 80 90 100 110 ,10 20 30 40 50 60 70
90 100 110
Figure 4. Planetary boundary layer height (m) at 4 p.m. for the detailed and average city cases.

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TECHNICAL REPORT DATA
1. Report No.
2,
3. 1
4, Title and Subtitle
"Introduction of Urban Canopy Parameterization into MM5 to simulate urban
meteorology at neighborhood scale
5.	Report Date
6.	Performing Organization Code
7. Author(s)
Sylvain Dupont, Jason Ching, and Steve Burian
8. Performing Organization Report No.
9.Performing Organization Name and Address
Atmospheric Modeling Division
National Environmental Research Laboratory
Research Triangle Park, North Carolina 27711
10.	Program Element No.
11.	Contract/Grant No.
] 2-Sponsoring Agency Name and Address
U. S. Environmental Protection Agency
Office of Research and Development
National Exposure Research Laboratory
Research Triangle Park, North Carolina 27711
•
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