To ease the adoption of AERMOD into Australia and New Zealand, several technical aspects that are important to the use of AERMOD are discussed in this presentation. These technical aspects include mixing height calculation techniques, surface parameter sensitivities and considerations, urban option applicability, and selection of terrain.

1. CASANZ2015 Conference, Melbourne, 20-23 September 2015 1
TECHNICAL CONSIDERATIONS OF ADOPTING AERMOD INTO
AUSTRALIA AND NEW ZEALAND
Tiffany Gardner, Brian Holland, Weiping Dai (PhD, PE, CM), Qiguo Jing (PhD)
Trinity Consultants, Inc.
Dallas, Texas, 75251 USA
Abstract
The AERMOD model has been the preferred near-field dispersion model by
the United States Environmental Protection Agency (US EPA) for air quality
impact assessment since 2006. US EPA also continues to update and improve
the model. The latest update to AERMOD was released in 2014, with another
update expected mid-2015. AERMOD is a steady-state Gaussian dispersion
model that represents the current state-of-science, including advanced
planetary boundary layer (PBL) parameterizations. Due to this advanced
science, good match between modelled and observed results, and reasonable
computational demands, more and more regulatory agencies across the globe
have started promulgating AERMOD for these assessments including EPA
Victoria (EPA Vic). EPA Vic has adopted AERMOD in place of AUSPLUME
as of January 2014. AUSPLUME is also a Gaussian model, but is limited by
the inability to model complex terrain and the use of older PBL
parameterizations (model last updated in 2004) than AERMOD. To ease the
adoption of AERMOD into Australia and New Zealand, several technical
aspects will be discussed in this paper that are important to the use of the
model. These technical aspects will include mixing height calculation
techniques, land use input sensitivities, urban option applicability, and terrain
data selection. By discussing these technical aspects of AERMOD, including
how they are handled in the model and the sensitivity of results to changes in
the parameters, existing and future AERMOD users in Australia and New
Zealand will be provided with information and tips that they may employ
moving forward when using the AERMOD model for their own environmental
impact assessments.
Keywords: AERMOD; AUSPLUME; Victoria; AERMET
1. Introduction
The AERMOD model has been the preferred near-
field dispersion model by the United States
Environmental Protection Agency (US EPA) for air
quality impact assessment since 2006 (US EPA
2005). US EPA also continues to update and
improve the model. The latest update to AERMOD,
executable 15181, was released in mid-2015.
AERMOD is a steady-state Gaussian dispersion
model that represents the current state-of-science,
including advanced planetary boundary layer (PBL)
parameterizations. Due to this advanced science,
good match between modelled and observed
results, and reasonable computational
requirements, more and more regulatory agencies
across the globe have started promulgating
AERMOD for these assessments including Victoria
EPA (EPA Vic). EPA Vic has adopted AERMOD in
place of AUSPLUME as of January 2014 (EPA
Victoria 2013). AUSPLUME is also a Gaussian
model, but is limited by the inability to model
complex terrain and the use of older PBL
parameterizations (updated in 2004) than
AERMOD. To ease the adoption of AERMOD into
Australia and New Zealand, several technical
aspects will be discussed in this paper that are
important to the use of the model. These technical
aspects include mixing height calculation
techniques, land use input sensitivities, and several
other aspects that AERMOD users should be
familiar with.
First, when using AERMOD, hourly convective
mixing heights, which are derived in part from upper
air observational data taken at or near sunrise, are
required by the model. Depending upon location,
however, this meteorological parameter can be
difficult to obtain. Some areas do not have nearby
upper air data available. Even areas with available
data can run into issues with AERMOD due to the
timing of the upper air observations. Typically,
observations are taken from radiosondes (weather
balloons) launched at 0000 and 1200 UTC. In
Western Australia, the time zone (UTC+8) puts the
0000 upper air sounding close enough to local

2. CASANZ2015 Conference, Melbourne, 20-23 September 2015 2
sunrise to generally be usable. For Eastern
Australia though, some of the upper air soundings
are launched a couple hours after local sunrise at
2300 UTC while others are launched a few hours
before local sunrise around 1700 UTC. While it may
be possible to use these upper air data in Eastern
Australia as they are launched a few hours before
or after local sunrise, modellers run the risk of not
being conservative enough in doing so because
these data could feed the “sunrise time” mixing
height information into the model when in reality the
data are coming from a time significantly before or
after sunrise. As a result, in Eastern Australia and
other locations, such as New Zealand and the
United Kingdom where the radiosonde launch times
are not close to the sunrise time as required by
AERMOD, various alternative techniques can be
used to best estimate the mixing heights for use in
AERMOD. These techniques are discussed and
evaluated.
Second, when using AERMET to process
meteorological data for input into AERMOD, three
land use parameters must be identified: surface
roughness, albedo and Bowen ratio. To define
these three micrometeorological parameters in the
area surrounding a facility, a detailed land use
analysis is required. While guidance is provided by
regulatory agencies, such as the US EPA and EPA
Vic, for determining these land use values, caution
must be exercised when doing so as variations can
lead to significant changes in AERMOD results. To
better understand the sensitivity of AERMOD
results to changes in these three land use inputs, a
comparison of AERMOD modelling results using
different land use settings was performed and is
presented in this paper.
In addition to these considerations, the applicability
of AERMOD’s urban option and issues related to
terrain data selection will be discussed. By
discussing these technical aspects of AERMOD,
including how they are handled in the model and
the sensitivity of results to changes in the
parameters, existing and future AERMOD users in
Australia and New Zealand will be provided with
information and tips that they may employ moving
forward when using the AERMOD model for their
own environmental impact assessments.
2. Mixing Height Techniques
2.1. Background
When processing meteorological data into an
AERMOD-ready format, both surface and upper air
data are required. While surface data is obtained
through weather stations on the ground, most upper
air observations are obtained when a radiosonde (an
instrument package suspended below a weather
balloon) is launched. These launches occur daily in
about 800 locations worldwide around 0000 and
1200 UTC, 365 days a year. As the weather balloon
ascends through the atmosphere, sensors on the
radiosonde measure profiles of pressure,
temperature, and relative humidity, and the wind
speed and direction are also recorded by tracking
the position of the radiosonde. These parameters
are used in AERMET, the meteorological pre-
processor to AERMOD, to determine atmospheric
turbulence and mixing height, which in turn affects
the computed pollutant concentrations in AERMOD.
In AERMOD, the preferred upper air sounding used
is one just before or at sunrise. For locations in
North America, this generally means the 1200 UTC
sounding (~0500 to 0800 local time). In other parts
of the world, this means the 0000 UTC sounding.
When AERMOD is used for a location in parts of
the world where 0000 and 1200 UTC are not near
sunrise, like New Zealand, and the United
Kingdom, or in locations like Eastern Australia
where using the upper air data may not be the most
conservative modelling approach, the question of
how to accurately and appropriately determine for
the local mixing height arises. There is currently no
regulatory standard method used to address this
issue, but various techniques have been developed
and are in widespread use.
One such method, based in part on the work of
Holtslag and Van Ulden (1983) is described below.
This technique, which utilizes the fact that hourly
mixing height data can be used by AERMET in
place of upper air data, has been utilized for
regulatory modelling applications in countries
around the world for more than fifteen years, with
broad acceptance. This technique can be used if
upper air data are unavailable, or when local times
that correspond to radiosonde ascents do not occur
near sunrise. This technique relies on calculation
of mixing heights using semi-empirical models to
estimate the surface similarity parameters of friction
velocity, sensible heat flux, temperature scale, and
Monin-Obukhov length via the routinely collected
surface meteorological variables of cloud cover,
ceiling height, wind speed, and temperature, as well
as estimates of surface roughness.
2.2. Technique to Estimate Mixing Depths
from Surface Observations
2.2.1. Daytime Mixing Depth Estimate
Daytime refers to the period from one hour after
sunrise to one hour before sunset. The daytime
mixing depth is estimated using sensible heat flux,
friction velocity, and Monin-Obukhov length. The
Monin-Obukhov length is used to determine
whether daytime mixing depth estimates will be
calculated using a neutral or unstable mixing depth
equation. If the absolute value of the Monin-
Obukhov length is greater than 100 metres, the

3. CASANZ2015 Conference, Melbourne, 20-23 September 2015 3
neutral mixing depth equation is used; otherwise,
the unstable mixing depth equation is used. To
determine the Monin-Obukhov length and then
estimate the daytime mixing depth, the sensible
heat flux and friction velocity will need to be
estimated.
The sensible heat flux, QH, is a critical parameter
required to estimate the buoyant production of
turbulent energy and resulting daytime mixing
depth. The convergence or divergence of sensible
heat flux produces warming or cooling of the air in
the boundary layer. The vertical exchange of heat
occurs primarily through turbulent motions or mixing
in the boundary layer. This process influences the
vertical profile of air temperature and resulting
atmospheric stability. Since no method exists for
directly measuring the sensible heat flux, it is
determined from the surface energy balance
expression that may be found in Appendix A. The
equation is solved using cloud cover data and
temperature values to parameterize Q* and solve
for QH, as proposed by Holtslag and Van Ulden
(1983). The latent heat flux and soil heat flux are
parameterized using the soil moisture availability
parameter and techniques proposed by Holtslag
and Van Ulden (1983). The soil moisture availability
parameter is assumed to be 0.5, which is the
midpoint in the range between saturated (1) and
arid (0). The anthropogenic heat flux is not
accounted for.
There are two additional methods to solve the
surface energy balance equation for QH using Q*:
(1) using a net radiometer to collect Q*
measurements, or (2) using a pyranometer to
collect incoming solar radiation measurements and
parameterize Q*.
After the sensible heat flux has been estimated, the
friction velocity needs to be estimated. There are
two separate equations used to estimate friction
velocity in neutral versus unstable conditions, which
may be found in Appendix A.
Once the sensible heat flux and friction velocity are
estimated, the Monin-Obukhov length can be
determined using the equation below:
𝐿 =
−𝑢∗
3
𝑇𝜌𝐶 𝑝
𝑘𝑔𝑄 𝐻
Using the Monin-Obukhov length, the estimation of
the daytime mixing depth is possible. For unstable
conditions (when |L| < 100), the daytime mixing
depth (Zi) is calculated using the sensible heat flux
and friction velocity as proposed by Farmer (1991).
The integrated sensible heat flux is calculated by
summing the values for each hour (h) after sunrise:
𝑍𝑖 = √ 𝑍 𝑛
2
+ 1400 ∑ 𝑄 𝐻
ℎ
0
Where: Zn =
𝑢∗𝑛
4𝑓
f = Coriolis Parameter
If the absolute value of the calculated Monin-
Obukhov length is greater than 100 metres, the
following expression is used to determine the
neutral mixing depth:
𝑍 𝑛 =
𝑢∗𝑛
4𝑓
2.2.2. Night time Mixing Depth Estimate
Night time refers to the period from one hour before
sunset to one hour after sunrise. Night time mixing
depths are estimated using friction velocity,
sensible heat flux, and Monin-Obukhov length.
During stable conditions, the temperature scale 𝜃∗
is used to calculate the stable friction velocity,
sensible heat flux, and Monin-Obukhov length,
which are subsequently used to determine night
time mixing depth. If the absolute value of the
Monin-Obukhov length is greater than 100 metres,
the neutral mixing depth equation is used.
First, two estimations are made for the temperature
scale. The first estimate is based upon the method
proposed by Holtslag and Van Ulden (1983) and
the second is based upon the temperature profile
equation. These equations may be found in
Appendix B. The smaller of the two temperature
profile estimates is used for subsequent
calculations.
After the temperature scale estimates are made,
the friction velocity is calculated which is then used
to estimate the sensible heat flux (see Appendix B).
The Monin-Obukhov length is then determined
using:
𝐿 =
𝑇𝑢∗
2
𝑘𝑔𝜃
The night time mixing depth is estimated using the
sensible heat flux and friction velocity during stable
conditions (|L| < 100) as proposed by Farmer
(1991). The depth of the turbulent layer (ZS) is
defined as:
𝑍𝑆 =
21500𝑢∗
2
√|𝑄 𝐻|
Where: 𝑍 𝑛 =
𝑢∗
4𝑓

4. CASANZ2015 Conference, Melbourne, 20-23 September 2015 4
If the absolute value of the calculated Monin-
Obukhov length is greater than 100 metres, then
the following expression defines the neutral mixing
depth:
𝑍 𝑛 =
𝑢∗
4𝑓
After the daytime and night time estimations for all
parameters above are made, a file should be
generated using the data as required by our
proprietary mixing height tool (a CD-144 file) and
then a computer utility called ADMS is run to
generate an .ADM file from the data. The .ADM file
is then input into AERMET to run the ADMS job type
and upon completion, the .SFC (surface) and .PFL
(upper air) files will be created that may be used in
AERMOD.
Using this method, locations where the 0000 and
1200 UTC soundings do not align with sunrise, such
as New Zealand, or where using the existing upper
air data may not be the most conservative approach
as is the case in Eastern Australia, will be able to
generate and use mixing heights and atmospheric
turbulence parameters in AERMOD that are
representative of the site location. As was
mentioned above, it should be noted that for Western
Australia, the time zone (UTC+8) puts the 0000 UTC
upper air sounding close enough to local sunrise to
generally be usable instead of this method.
3. Surface Parameter Considerations
To define turbulence in AERMOD, especially in the
absence of direct on-site measurements, the
surface roughness, Bowen ratio, albedo, wind
speed and direction, and temperature are used in
AERMET. Unlike wind speed, wind direction, and
temperature, the other three surface
micrometeorological parameters can be difficult to
quantify. Guidance is provided by regulatory
agencies, such as the US EPA and Victoria EPA,
for determining these land use values. However,
caution must be exercised when doing so as
variations of these values in AERMET can lead to
significant changes in AERMOD results.
3.1. Surface Roughness
The surface roughness length is a measure of how
smooth or rough a surface is, with lower values
corresponding to smoother surfaces (e.g., open
water) and higher values corresponding to rougher
surfaces (e.g., high intensity residential areas).
When determining the surface roughness around the
meteorological surface station being used in
AERMOD, the US EPA (US EPA 2008) and Victoria
EPA (EPA Victoria 2013) requires that modelers
consider land-use types within a 1 km radius. To
ease the process of determining surface roughness,
a surface roughness length table (see Appendix C
for an example) may be used, which contains
predetermined values for land use types. However,
in order to accurately estimate the surface
roughness, the circular area centered on the site
location should be broken down into up to 12 sectors
(30° each) and an inverse-distance weighted
average should be used when multiple land use
types are present within that 1 km radius. In addition
to varying by direction, the surface roughness can
vary seasonally, so it is important exercise caution
when determining these values, taking into account
the time of year and land use. AERMET allows for
seasonal or even monthly variation in land use
parameters to account for this.
3.2. Albedo
The albedo is the measure of a surface’s ability to
reflect incoming solar radiation with values ranging
from 0 to 1, where light-colored and reflective
surfaces (e.g., snow) will have higher albedo values
because more light is reflected and dark surfaces
(e.g., forest) will have lower albedo values. To
accurately account for albedo in AERMOD, the US
EPA (US EPA 2008) and Victoria EPA (EPA Victoria
2013) require modelers to consider land-use types
within a 10km by 10km area around the
meteorological station site. A simple average of all
land use types within the area may be used instead
of determining a value per sector, and the values for
albedo may be found in the seasonal and land use
variability tables available from US EPA and other
sources (similar to the Surface Roughness table in
Appendix C).
3.3. Bowen Ratio
The Bowen ratio, which ranges from 0.1 to 10,
represents the ratio of sensible heating (in which
solar radiation increases temperature) to latent
heating (in which solar energy is used in evaporating
water). Higher Bowen ratios represent arid regions
whereas low Bowen ratios represent moist regions.
Like the albedo, land use types within a 10km by
10km area around the meteorological station site
should be considered (EPA Victoria 2013; US EPA
2008), as should variations by season, and a simple
average of all land use types within the area may be
used.
3.4. Considerations
In order to provide appropriate surface roughness,
albedo, and Bowen ratio values for the surrounding
area to AERMET, a detailed land use analysis is
required. It is important to point out that according to
the guidance of US EPA, the area over which these
values are obtained and averaged should be
centered upon the meteorological station site, so it is
important to make sure the land use coverage is
similar to that around the actual site being modelled.

5. CASANZ2015 Conference, Melbourne, 20-23 September 2015 5
AERMET and AERMOD do not currently offer the
ability to adjust meteorological data to account for
land use differences between a meteorological
station and source location.
Land use maps and aerial photographs are essential
resources to determine the types, amounts, and
relative locations of vegetation, urban, and other
land uses and covers. Additionally, the US EPA
AERSURFACE utility may be used as an aid in
determining realistic and reproducible surface
characteristic values for input to AERMET.
AERSURFACE requires a land use dataset based
on the format of the US 1992 National Land Cover
Database.
3.5. Sensitivity Analysis in AERMOD
A simple comparison of AERMOD results with
varying land use inputs was performed to illustrate
the effects of land use on the model. Four uniform
land uses were considered: grassland, desert
shrubland, open water, and urban. A one-year model
run was conducted using two sources: a 25 m stack
and a ground-level area source. Maximum ground-
level concentrations from 1-hour and 24-hour
averaging periods were examined. The results are
shown in Figure 1, with concentrations normalized
based on the grassland case results to allow easier
comparison of the variations between land use
types.
1-Hour Averaging Period
Land Use
Type
Normalized Concentration
25m Stack Ground-level
Grassland 1.00 1.00
Desert 1.08 1.06
Water 1.32 1.19
Urban 1.32 0.99
24-Hour Averaging Period
Land Use
Type
Normalized Concentration
25m Stack Ground-level
Grassland 1.00 1.00
Desert 1.56 1.14
Water 0.51 0.79
Urban 2.17 1.23
Figure 1. Maximum ground-level concentrations from 1-
hour and 24-hour averaging periods normalized based on
the grassland case results.
As is shown in the results in Figure 1, varying the
land use inputs can have an impact on AERMOD
results. While the impacts of varying land use inputs
on the 1-hour averaging period concentrations in this
analysis are visible, the impacts on the 24-hour
averaging period results are much more significant.
In the 24-hour averaging period results, the land use
effect has more impact on the stack than it does on
the ground-level source. This is likely due to the fact
that higher Bowen ratio in the desert and urban
cases, and a higher surface roughness in the urban
case, help to mix the plume down to the surface
sooner, whereas the low surface roughness and low
Bowen ratio in the water case means that the plume
does not get mixed down quickly. Even for the
ground-level source though, the 24-hour averaging
period normalized concentrations show significant
impacts due to varying the land use parameters. For
example, the concentration for the urban land use
case is about 56% higher than the water
concentration for the ground-level source.
All in all, this analysis shows the impacts that varying
land use inputs can have on AERMOD results. As
such, it is important to ensure the most
representative land use inputs are used when
performing a land use analysis.
4. Urban Option Applicability and
Considerations
When processing meteorological data in AERMET
and using the surface roughness, Bowen ratio, and
albedo, the surrounding land use types are taken
into account as described above. However, if a
facility is located within the influence of a large city,
an additional portion of the AERMOD model
algorithm may be needed to account for the urban
heat island effect.
In cities, surfaces such as concrete and asphalt
absorb and store radiation to a greater degree than
typical rural surfaces. This effect, combined with
anthropogenic waste heat and reduced wind speeds
due to large buildings, can cause an increase in
surface temperature in urban areas relative to rural
areas, particularly at night. The warm night time
temperatures within the city create enhanced
turbulence relative to that which is expected in an
adjacent rural, stable boundary layer. The result is
an urban heat island; a city or metropolitan area that
is significantly warmer than its surrounding rural
areas. This effect extends beyond what is captured
by the surface roughness, Bowen ratio, and albedo
parameters, and thus must be accounted for
separately by a model.
In AERMOD, users may turn on the Urban Option
(URBANOPT) to account for the urban heat island
effect (US EPA 2004). By doing this, AERMOD
assumes higher surface temperatures in urban
areas compared to rural night time conditions, and
will make adjustments to the convective velocity
scale, heat flux, and temperature gradient to

6. CASANZ2015 Conference, Melbourne, 20-23 September 2015 6
compute an adjusted urban mixing height. The
magnitude of the urban heat island effect in
AERMOD is driven by the urban-rural temperature
difference that develops at night, so this adjustment
of the mixing height will be based on temperature
difference, roughness, and population.
By default, the Urban Option is turned off in
AERMOD because it is only applicable for use in
large cities. Because many smaller cities do not
experience this urban heat island effect, before
turning it on in AERMOD, a local regulatory agency
should be consulted. If permission is granted by the
regulatory agency to use this option, US EPA
guidance is available to help determine which
sources should be modelled as urban and which
should be modelled as rural (US EPA 2009). This
approach is consistent with the fact that the urban
heat island is not a localized effect, but more
regional.
5. Terrain Selection
5.1. How Terrain is Handled in AERMOD
In many older Gaussian models, such as the ISTSC3
model and AUSPLUME, a pollutant plume can either
rise above a terrain feature or travel around the
terrain feature; not both (Ministry for the
Environment 2004). This results in a sharp
discontinuity in behaviour – a miniscule increase in
stack height could completely change the terrain
response of the plume. AERMOD, however, utilizes
a terrain algorithm that enables a portion of the
plume to travel over the terrain while the remainder
travels around the terrain, eliminating the
discontinuity. Using a dividing streamline height,
which is calculated based on stability, wind speed,
and plume height, AERMOD is able to account for
this not-purely-Gaussian behaviour of a plume (see
Figure 2).
Figure 2. To determine the flow of the plume when terrain
is present, AERMOD uses the dividing streamline height
to calculate the weighted sum of the horizontal plume state
(e.g., portion that wraps around the terrain) and the terrain
responding plume state (e.g., portion that rises above the
terrain). (US EPA 2004)
As is illustrated in Figure 2, the portion of the plume
that is below the dividing streamline height wraps
around the terrain feature, while the portion of the
plume that is above the dividing streamline height
rises up and over the terrain feature.
5.2. Terrain Data Selection
The terrain files accepted by AERMAP, the terrain
pre-processor of AERMOD, are Digital Elevation
Model (.DEM) data and National Elevation Dataset
(NED) GeoTIFF files. AERMAP tends to be “US-
specific” in terms of the terrain data formats it
processes, so CISRO and the Australian
Government Bureau of Meteorology (BoM) are
currently undertaking the One-second DEM Project,
during which they will be developing one-second (30
metre resolution) DEM for Australia based on SRTM
data (EPA Vic 2013a). SRTM data include the
heights of obstacles (e.g., buildings; trees), however,
because SRTM data is based on reflective surfaces,
there are gaps in the data. EPA Vic states though
that gap filled and filtered topography data with
vegetation and obstacles removed is available from
Geo Science in Australia in high resolution (EPA Vic
2013a).
Using the terrain data files, AERMAP imports model
object elevations into AERMOD using the UTM
coordinate system. It is important to note that if a
new model object is added after AERMAP has
already been run, it is necessary to rerun AERMAP
so the elevation for the new model object is also
imported.
5.3. 10% Slope Rule
AERMOD requires that the DEM or NED data files
that are imported into the model encompass every
model object and also satisfy the 10% slope rule. In
other words, if a 10% slope is drawn from the most
distant receptors, then the DEM or NED terrain data
files should include every terrain feature that rises
above this slope.
Estimating the number of DEM or NED files that are
necessary to include in the terrain analysis
performed by AERMAP is not straight forward
because there is no standard distance for which
terrain data should be provided; it varies case by
case. Because of this, many modellers simply obtain
terrain data that surrounds the extents of their
receptors. In areas with significant topography, this
will not be enough to compute the correct critical
scale height required by AERMOD though, which is
used to calculate the critical dividing streamline
height. As a conservative estimate, it is good
practice to estimate on the higher end to ensure the
correct number are included instead of

7. CASANZ2015 Conference, Melbourne, 20-23 September 2015 7
underestimating the number of DEM or NED data
files required.
6. Conclusion
With the recent promulgation of AERMOD in Victoria
and the potential future promulgation of the model in
other Australian states and New Zealand, certain
aspects of AERMOD should be considered as they
differ from the previously promulgated model,
AUSPLUME. The topics covered in this paper bring
light to and discuss a few of those aspects and
provide suggestions and considerations on how to
handle them when setting up a model run in
AERMOD.
References
Environmental Protection Authority Victoria, 2013a,
‘Construction of Input Meteorological Data Files for
EPA Victoria’s Regulatory Air Pollution Model
(AERMOD)’, 1550.
Environmental Protection Authority Victoria, 2013b,
‘Guidance Notes for Using the Regulatory Air
Pollution Model AERMOD in Victoria’, 1551.
Farmer, S.P.G 1991, ‘Outline of Smith and Blackall’s
(1979) methods of estimating boundary layer
depth,’ Private communication to M.D. Miller.
Holtslag, A. A. M. and Van Ulden, A. P. 1983, ‘A
simple scheme for daytime estimates of the
surface fluxes from routine weather data’, J.
Climate Appli. Meteorol., 22: 517-529.
Ministry for the Environment, 2004, Manatu Mo Te
Taiao, New Zealand, 2004, Good Practice for
Atmospheric Dispersion Modeling.
US Environmental Protection Agency, 2008,
‘AERSURFACE User’s Guide’.
US Environmental Protection Agency, 2004,
‘AERMOD: Description of Model Formulation’.
US Environmental Protection Agency, 2005,
‘Revision to the Guideline on Air Quality Models:
Adoption of a Preferred General Purpose (Flat and
Complex Terrain) Dispersion Model and Other
Revisions; Final Rule’ 40 CFR Part 51.
US Environmental Protection Agency, 2009,
‘AERMOD Implementation Guide’.
Wang, I.T. and Chen, P.C. 1980, ‘Estimation of heat
and momentum fluxes near the ground’, Proc. 2nd
Joint Conf. on Applications on Air Pollution
Meteorology, AMS, 764-769.
Appendix A
The following equations are used in Section 2.2.1 to
estimate the sensible heat flux and friction velocity,
which are in turn used to estimate the Monin-
Obukhov length and mixing depth.
The sensible heat flux is determined from the
following surface energy balance expression:
Q* = QH + QE + QG - QA
Where: Q* = Net Radiation
QH = Sensible Heat Flux
QE = Latent Heat Flux
QG = Soil Heat Flux
QA = Anthropogenic Heat Flux
For friction velocity, the following equations are
used in neutral and unstable conditions,
respectively:
Neutral Conditions: 𝑢*n =
𝑘𝑢
ln(
𝑧
𝑧𝑜
)
Where: u*n = Neutral friction velocity (m/s)
k = von Karman’s constant (0.4)
u = wind speed (m/s)
z = wind measurement height (m)
zo = surface roughness length (m)
Unstable Conditions (Wang and Chen 1980):
u* =
𝑘𝑢
ln(
𝑧
𝑧𝑜
)
[1 + 𝑑1 ln(1 + 𝑑2𝑑3)]
Where: u* = Friction Velocity (m/s)
d1 = 0.128 + 0005 ln (z/zo) if (z/zo) <= 0.01
= 0.107 if (z/zo) > 0.01
d2 = 1.95 + 32.6 (z/zo)0.45
d3 = (
𝑄 𝐻
𝜌𝐶 𝑝
) (
𝑘𝑔𝑧
𝑇𝑢∗𝑛
3)
Where: QH = Sensible Heat Flux (W/m2)
𝜌 = Atmospheric Density (kg/m3)
Cp = Specific Heat at Constant Pressure
(J/K kg)
g = Acceleration due to Gravity (9.8m/s2)
T = Ambient Air Temperature (K)

8. CASANZ2015 Conference, Melbourne, 20-23 September 2015 8
Appendix B
The following equations are used in Section 2.2.2 to
estimate temperature scale and friction velocity.
The first estimate of temperature scale is based on
the method proposed by Holtslag and Van Ulden
(1983):
𝜃∗ = 0.09[1 − 0.5(
𝑇𝑂
10
)2
]
Where: TO = Total Opaque or Total Sky Cover in
tenths
The second estimate is based upon the temperature
profile equation:
𝜃∗ =
𝑇𝐶 𝑑𝑛 𝑢2
18.8𝑧𝑔
Where: 𝐶 𝑑𝑛 = 𝑘/ln(
𝑧
𝑧 𝑂
) (Neutral Drag Coefficient)
For the night time friction velocity, the following
calculation is used:
𝑢∗ = (
𝐶 𝑑𝑛 𝑢
2
) [ 1 + √1 − (
2𝑢0
√ 𝐶 𝑑𝑛 𝑢
)2 )
Where: uo = √
4.7𝑔𝑧𝜃∗
𝑇
The sensible heat flux is estimate using the friction
velocity and temperature scale for the turbulent heat
transfer using the following formula:
𝑄 𝐻 = −𝜌𝐶 𝑝 𝑢∗ 𝜃∗
Appendix C
The following chart shows an example of the Surface
Roughness Length chart that may be utilized when
defining the surface roughness for an area:
Table 1. Surface roughness length, in metres, by
land-use and season