AN EVALUATION OF AUSPLUME-PRIME WITH NEUTRAL AND STABLE
FLOW WIND TUNNEL BUILDING EFFECTED DISPERSION DATA
T John Taylor
Engineering Air Science Pty Ltd., The Gap, 4061, Qld, Australia.
Abstract
This paper compares AUSPLUME PRIME with wind tunnel measurements
of building effected dispersion in neutral and stable flow conditions. While
AUSPLUME PRIME performed reasonably well, its performance in stable
conditions was inferior to that for neutral conditions. Significant under
prediction was evident immediately downwind of the recirculation region for
a roof top source with the building at an oblique angle (45°) to the flow and
also at large distances downwind (40 to 50 H b ) under stable flow conditions.
The results would indicate there is potential for larger discrepancy further
downwind. Better agreement was observed between wind tunnel and
AUSPLUME predictions when dispersion was not adjusted for roughness.
Keywords: Building Effects, AUSPLUME, PRIME, Wind Tunnel.
1. Introduction
With the increasing dense urban landscape of the
modern era, commercial offices and residential
neighbour hoods are moving closer to industrial
and manufacturing facilities.
Often office
environments and smaller industrial facilities
develop in close proximity or even within the same
complex. Residential developments are also
encroaching on the fence lines of what were once
well
buffered
industrial,
manufacturing or
agricultural facilities.
The quality of the ambient air can become a critical
issue for those occupying the offices or residential
facilities. While the majority of problems typically
arise through perceptible odour or dust impacts,
unseen emissions of chemical vapours and
particulate may have potential to cause more
serious health related impacts.
Standard practice for emissions to air from
industrial activities is the use of a stack to release
the pollutants at an elevated height. Emissions are
typically contained or captured and exhausted into
the environment through a vent or stack, often at or
just above the roof height of the industrial plant or
building within which the pollutants are generated.
Other facilities may also rely on natural ventilation
through openings such as windows doors and roof
vents to provide adequate extraction of process
emissions from the work environment.
To quantify potential air quality impacts on the
ambient environment regulatory atmospheric
dispersion models are typically employed. While
there are many atmospheric dispersion models
used in different jurisdictions across the world, in
Australia the relatively simple AUSPLUME model,
CASANZ 2011 Conference - Auckland - 31 July - 2 August 2011
Paper 151
developed by the Victorian EPA, is often the most
frequently used for these supposably more simple
assessments.
AUSPLUME is a steady state analytically based
dispersion model primarily validated for near-field
analysis of ambient air quality impacts. The more
complex 3-dimensional models CALPUFF and
TAPM may also be applied in a similar manner to
AUSPLUME, and would typically provide similar
predictions in the immediate vicinity of the source.
With many industrially based air pollutant sources
arising from operations within buildings and sheds,
a critical aspect of any approach to represent the
behaviour and potential impacts of the emissions is
the treatment of the building, and the effect it has
on plume transport and dispersion within the
atmospheric boundary layer. Many regulatory
atmospheric dispersion models now incorporate
buildings’ modules, specifically developed to
improve representation of building effects on flow
and dispersion within the regulatory context.
AUSPLUME, CALPUFF and TAPM, the three
regulatory atmospheric dispersion models typically
used in Australia and New Zealand, incorporate the
Plume RIse Model Enhancement (PRIME)
algorithm developed under sponsorship from the
Electric
Power
Research
Institute (EPRI)
(Schulman et al. 2000). The approach of PRIME is
similar to that of the UK developed ADMS Build
module with the development of both tools relying
heavily on the results of fluid modelling studies of
flow and dispersion around surface mounted
obstacles performed primarily within wind tunnels.
Apart from a number of simulations conducted in
the Monash University Environmental Wind Tunnel
using an inverted modelling technique (Melbourne
& Taylor 1994), the majority of fluid simulations
were conducted under neutral atmospheric flow
conditions.
Generally, studies investigating flow around surface
mounted obstacles, and primarily performed to
develop understanding of building effects on
atmospheric flow and dispersion, have ignored the
effect that stable stratification may have. With a
lack of experimental data relating to stably stratified
conditions, buildings module algorithms have
considered prediction schemes derived from
neutral studies as general and equally applicable
for both neutral and stably stratified flow conditions.
To address this issue an experimental investigation
into the effects of stable stratification on flow and
dispersion around a surface mounted obstacle was
undertaken. Experiments focusing on the region
beyond the near wake, a region of very limited
previous investigation, were conducted within the
meteorological
wind
tunnel
within
the
Environmental Flow Research Centre (EnFlo) at
the University Of Surrey, UK, (Maré 2003).
The current investigation utilises the results of
these wind tunnel studies to assess the
performance of PRIME within the AUSPLUME
dispersion model with respect to predicted ground
level concentrations (GLC’s) for pollutant releases
from building height and above, under neutral and
stably stratified flow conditions.
2. Background
2.1. Dispersion Model
AUSPLUME is a steady-state Gaussian plume
dispersion model designed to predict ground level
concentrations or dry deposition of pollutants
released into the atmosphere. Developed by the
Victorian Environmental Protection Authority, it has
a mathematical basis derived from the “Plume
Calculation Procedures“ (EPAV 1985), which is an
extension of the US EPA Industrial Source
Complex (ISC) model (Bowers et al. 1979). The
model’s primary purpose is for the regulatory
assessment of the impact of emissions from
‘individual’ industrial sources.
The latest versions of AUSPLUME include the
PRIME algorithms as their most sophisticated
method of accommodating the effect of a building in
the vicinity of a point source, adjusting the plume
dispersion for the effect buildings may have on the
flow and turbulence. The PRIME algorithms have
been incorporated into many dispersion models
including the US models ISC, AERMOD and
CALPUFF, and the CSIRO model TAPM, with
numerous validation studies performed using
laboratory and field based data sets. The data set
CASANZ 2011 Conference - Auckland - 31 July - 2 August 2011
Paper 151
used in this analysis is independent of the previous
validation studies.
PRIME uses the Building Profile Input Program
(BPIP) to analyse point source and building
configurations and provide effective building
parameters for each 10 degree segment of the
compass. Dispersion is then determined based on
the effective building parameters determined for the
appropriate 10 degree segment for the wind
direction of concern.
The PRIME model breaks the region downwind of
the building into two primary regions:


Near Wake, being the high turbulence
recirculation region in the immediate lee of the
building defined by the location of streamline
reattachment. In this region PRIME assumes
uniform concentrations based on the plume
fraction entrained into the near wake region;
Far Wake, being the region of enhanced
turbulence downwind of the near wake. A two
source model is used whereby a virtual ground
level source is used to represent the plume
originating from the near wake, and an elevated
source represents the remaining plume.
2.2. Wind Tunnel Experiments
Wind tunnel experiments investigating the effect of
stable stratification on flow and dispersion around a
cube were conducted in the meteorological wind
tunnel at the Environmental Flow Research Centre
(EnFlo), University of Surrey (Maré 2003). The
tunnel has a working section 20 m long by 3.5 m
wide and 1.5 m high. Barrier walls, vorticity
generators and surface roughness elements are
used to create drag and generate a model
simulation of the natural atmospheric boundary
layer under neutral conditions as in a boundary
layer wind tunnel. To create stratified boundary
layer flow, a density profile is generated through the
use of heating tubes, controlling the inlet air
temperature, and floor cooling panels assisting to
develop and maintain a stratified flow along the
length of the wind tunnel. Flow and dispersion data
is provided in three boundary layer flows of differing
stability, one neutral and two stable flows,
considering dispersion in ‘undisturbed’ and building
effected conditions for two source heights:
 Building height (H b ), and
 1.5 H b .
Two
building
scenario
configurations
considered:
are
 Upwind face normal (90°) to the flow; and
 Cube at 45° oblique angle to the flow.
The experiments utilised a streamlined horizontal
non-buoyant source with the exit velocity matched
to that of the local freestream flow to limit any effect
the source configuration may have on the flow and
dispersion.
3. Model Configuration
3.1. Wind Tunnel
The modelling of wind effects on square form
structures, including effects on dispersion, is
relatively straight forward, as the location of flow
separation is distinctly defined by the sharp edges
of the obstacle. However, the limited size of a wind
tunnel can restrict the downwind and crosswind
distance over which reliable measurements can be
obtained, as well as restricting larger eddy sizes,
limiting representation of meander behaviour.
Flow conditioning devices are used to enhance
generation of a scaled representation of the
atmospheric boundary layer flow. Typically, a
region of reasonably horizontally homogeneous
flow is developed through the middle and later
sections of the wind tunnel working section in which
the simulations are performed. A summary of the
wind tunnel boundary layer and model parameters
is provided in Table 1. A cube of dimensions 100
mm was used as the bluff body obstacle,
representing a building within the experiments.
Table 1: Model and flow parameters summarising
the wind tunnel model scenarios.
Wind Tunnel Model
Neutra
Stable 1.3
Stable 1.1
l
H b (m)
z o (m)
z o /H b
δ (m)
Max x/H b
Max x (m)
U b (m/s)
u* (m/s)
L MO (m)
0.1
0.001
0.01
1.06
50
5
1.45
0.14
∞
0.1
0.001
0.01
0.6
50
5
0.82
0.04
0.133
0.1
0.001
0.01
0.6
50
5
0.69
0.03
0.081
3.2. AUSPLUME
Regulatory
dispersion
models
including
AUSPLUME are developed to emulate field
configurations of actual atmospheric flow and
dispersion. They cannot be directly applied to the
smaller scale model configuration of wind tunnel
experiments of atmospheric flow and dispersion.
Hence, the experiments require scaling to a size
representative of possible field scenarios. The
scaling can be somewhat arbitrary, within the limits
of what would be considered a realistic real-world
scenario. Consideration should also be given to the
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Paper 151
eddy size within the wind tunnel flow and that
appropriate to the simulated atmospheric
conditions. The length scale of the wind tunnel
experiments was adjusted by a scaling factor of
300 to represent a realistic field scenario. This
provided for a cubic building of 30 m dimensions.
The building and flow parameters used in the
AUSPLUME configuration are summarised in Table
2. As the model plume within the wind tunnel
experiments was released horizontally, with
normalised momentum and no buoyancy, the wind
speeds used for the AUSPLUME configuration can
also be somewhat arbitrary, particularly as the
concentration results are normalised by the
corresponding model velocities to enable direct
comparison. The model was configured assuming
the meteorological measurements were obtained at
the building height (30 m). As AUSPLUME, like
most regulatory dispersion models, cannot
represent horizontal emissions, a vertical stack with
a small exit velocity was employed to limit
momentum plume rise to virtually zero. Buoyancy
induced rise was also minimised by the use of a
release temperature equivalent to that of the
ambient flow.
Table 2: Summary of the AUSPLUME
configuration used to represent the wind tunnel
simulation on a scale of 300:1.
Scale
300
H b (m)
z o (m)
z o /H b
δ (m)
Max x/H b
Max x (m)
U b (m/s)
P-G Cat.
AUSPLUME Model
Stable
Neutral
Stable 1.1
1.3
30
0.3
0.01
318
50
1500
5
D
30
0.3
0.01
180
50
1500
3.1
E
30
0.3
0.01
180
50
1500
1.6
F
AUSPLUME is an older generation model with
dispersion primarily based on stability class (A to F)
and a power law velocity profile used to estimate
stack height wind speed when it is different from
that of the meteorological data. A wind speed at the
building height (H b = 30 m) was provided in the
meteorological input file in this investigation, and
thus a correction was only applied for the higher
stack simulations with H s = 1.5H b .
AUSPLUME was initially run in a ‘default’ regulatory
mode to provide a comparison of ground level
concentration predictions for a typical operational
configuration. In addition to investigating the
performance of PRIME, predictions for isolated
stacks, without the effect of buildings, were also
compared. Subsequent comparisons concentrated
on improved similarity in predicted ground level
concentration profiles for the isolated stack
scenario, and the corresponding effect this had
when building effects were considered.
4. Centreline GLC comparison
The wind tunnel measurements (Maré, 2003)
investigated a number of aspects of the dispersion
downwind of the idealised model building, however
the most significant relevant to regulatory
applications is typically the potential maximum
ground level concentrations that occur on the
downwind centreline. It is these predictions that
are likely to provide the impacts on which
regulatory decisions are made. Concentrations are
presented in a non-dimensional format as detailed
in Equation 1 below:
C  U b  H b2

Q
Equation 1
Here C is the concentration, Q the emission rate
and U b and H b the mean velocity and height at the
top of the building respectively. The downwind
distance x, is also normalised by H b.
4.1. Default configuration
The results for a comparison using the default
‘regulatory’ configuration of AUSPLUME, based on
the measured wind tunnel boundary layer
parameters as summarised in Table 2, are
presented in Figure 1. A summary of a statistical
analysis is presented in Table A- 1. From visual
inspection of Figure 1 it is immediately evident that
the main features representing plume behaviour,
such as the location of initial plume touchdown and
maximum concentration and the general decay of
the concentration with downwind distance, are in
best agreement for the neutral flow condition. As
the stability of the boundary layer increases, the
similarity in these main features between the wind
tunnel results and the predictions of AUSPLUME is
observed to reduce significantly.
Under stable flow conditions and for the isolated
stacks in particular, the location of maximum
concentration is observed significantly closer to the
source for the AUSPLUME predictions than the
wind tunnel results would indicate. The stable flow
wind tunnel measurements would indicate the
maximum GLC has not been achieved for either
isolated stack or for the building configuration with
the higher stack when the cube is at 90° to the wind
direction. The measured concentrations would also
suggest the region over which the peak
concentration extends is increased, and the rate of
reduction reduced under the stable flow conditions,
leading to higher concentrations observed further
CASANZ 2011 Conference - Auckland - 31 July - 2 August 2011
Paper 151
downwind (40 – 50 H b ) in the wind tunnel
measurements than predicted by AUSPLUME.
The other significant variation is the over prediction
of GLC’s by AUSPLUME PRIME closer to the
buildings (x < 15H b ) when the flow is at 90° to the
building. The effect is more pronounced under the
stable flow conditions, with concentrations over
estimated by up to a factor of 3, or more for the
stable flow configuration with H s = 1.5H b . There is
also a general under prediction downwind of the
near wake (3H b <x<15H b ) when the flow is at an
oblique angle of 45° to the building, with the PRIME
model reducing the concentration beyond the near
wake more rapidly than observed in the wind tunnel
when H s = H b , particularly for the stable flows.
When the stack is higher (H s = 1.5H b ), PRIME over
predicts the rate of entrainment to the ground, and
thus the GLC for the more stable flow configuration.
4.1.1. Modified dispersion configuration
With concern the higher observed than predicted
concentrations for the building effected stable flow
scenarios at the greater downwind distances may
arise due to the poor agreement of the base
dispersion (isolated stack) case, the standard
roughness adjustment to dispersion within
AUSPLUME was selectively decommissioned. This
improved the AUSPLUME representation of the
wind tunnel results, particularly for the stable
scenarios. The adjustments that gave best
agreement between the wind tunnel dispersion
measurements and the AUSPLUME predictions for
the isolated stack scenarios are summarised in
Table 3. The adjustment for surface roughness
reduced the magnitude of the dispersion parameter
used within AUSPLUME.
Graphical results comparing the wind tunnel
measurements with the predictions of AUSPLUME
with modified dispersion are presented in Figure 2.
Corresponding statistical parameters are presented
in Table A- 2. The improved representation for the
isolated stack scenarios under stable flow and also
for the neutral case with H s = 1.5H b are evident.
However, the features discussed above in relation
to the higher observed concentrations than
predicted at the larger downwind distances under
the building affected stable flow scenarios are still
evident, and even more pronounced the Category
E stability. It is also evident that AUSPLUME
PRIME is in better agreement with the wind tunnel
measurements for building effected dispersion
under neutral flow conditions than stable flow
conditions. This was particularly the case when the
building was at an oblique angle of 45° to the flow,
with PRIME having a tendency to under estimate
GLC’s, particularly at distances further downwind.
Table 3: Summary of AUSPLUME dispersion
parameter adjustments used to improve
agreement with the wind tunnel isolated stack
scenarios.
Neutral
Vertical dispersion adjustment only
Stable 1.3 No dispersion roughness adjustments
Stable 1.1 Horizontal dispersion adjustment only
H s = 1.5 H b
Hs = Hb
0.08
0.07
0.045
Neutral WT
Neutral AUSPLUME
Stable 1.3 WT
Category E AUSPLUME
Stable 1.1 WT
Category F AUSPLUME
0.04
0.035
0.06
0.03
0.05
0.04


0.025
0.02
0.03
0.015
0.02
0.01
0.01
0.005
0
0
0
10
20
30
40
50
60
0
10
20
x/H b
0.9
0.8
0.7
30
40
50
60
x/H b
0.14
Neutral WT
Neutral AUSPLUME
Stable 1.3 WT
Category E AUSPLUME
Stable 1.1 WT
Category F AUSPLUME
0.12
Neutral WT
Neutral AUSPLUME
Stable 1.3 WT
Category E AUSPLUME
Stable 1.1 WT
Category F AUSPLUME
0.1
0.6
0.08


0.5
0.4
0.06
0.3
0.04
0.2
0.02
0.1
0
0
0
10
20
30
40
50
60
0
10
20
x/H b
0.7
0.6
30
40
50
60
x/H b
0.25
Neutral WT
Neutral AUSPLUME
Stable 1.3 WT
Category E AUSPLUME
Stable 1.1 WT
Category F AUSPLUME
0.2
Neutral WT
Neutral AUSPLUME
Stable 1.3 WT
Category E AUSPLUME
Stable 1.1 WT
Category F AUSPLUME
0.5
0.15


0.4
0.3
0.1
0.2
0.05
0.1
0
0
0
10
20
30
40
50
60
x/H b
0
10
20
30
40
50
60
x/H b
Figure 1: Downwind centreline non-dimensional ground level concentrations measured in the wind tunnel and
predicted by AUSPLUME in ‘default’ configuration. H s = H b - left, H s = 1.5H b - right, isolated stack - top, cube at
90° - centre, cube 45° - bottom.
5. Conclusions
AUSPLUME PRIME predicted GLC’s downwind of
a building are compared to wind tunnel results for
neutral and stable flow conditions. The results
demonstrate AUSPLUME PRIME provides more
accurate predictions of downwind GLC’s under
neutral flow conditions than stable conditions. For
stable conditions AUSPLUME PRIME had a
CASANZ 2011 Conference - Auckland - 31 July - 2 August 2011
Paper 151
tendency to under predict GLC’s, particularly for
wind directions oblique to the building and for
distances greater than 30 to 40 building heights
downwind. While generally AUSPLUME PRIME
performed well, there is some concern of its ability
to represent dispersion from the more elevated
stack with respect to the location of the maximum
GLC’s. Higher impacts occurred at distances from
the source under stable conditions, than otherwise
predicted by AUSPLUME. Building module
improvement could be achieved through greater
consideration of flow angle relative to the building
along with improved understanding of the effect of
Hs = Hb
0.12
0.1
stability on flow and dispersion around buildings,
particularly at extended distances downwind where
building effects under neutral conditions have
typically dissipated.
H s = 1.5 H b
0.045
Neutral WT
Neutral AUSPLUME
Stable 1.3 WT
Category E AUSPLUME
Stable 1.1 WT
Category F AUSPLUME
0.04
0.035
0.08
0.03
0.025


0.06
0.02
0.04
0.015
0.01
0.02
0.005
0
0
0
10
20
30
40
50
0
60
10
20
x/H b
0.9
0.8
0.7
30
40
50
60
x/H b
0.25
Neutral WT
Neutral AUSPLUME
Neutral WT
Neutral AUSPLUME
Stable 1.3 WT
Category E AUSPLUME
Stable 1.3 WT
Category E AUSPLUME
Stable 1.1 WT
Category F AUSPLUME
Stable 1.1 WT
Category F AUSPLUME
0.2
0.6
0.15


0.5
0.4
0.1
0.3
0.2
0.05
0.1
0
0
0
10
20
30
40
50
0
60
10
20
x/H b
0.7
0.6
30
40
50
60
x/Hb
0.25
Neutral WT
Neutral AUSPLUME
Neutral WT
Neutral AUSPLUME
Stable 1.3 WT
Category E AUSPLUME
Stable 1.3 WT
Category E AUSPLUME
Stable 1.1 WT
Category F AUSPLUME
Stable 1.1 WT
Category F AUSPLUME
0.2
0.5
0.15


0.4
0.3
0.1
0.2
0.05
0.1
0
0
0
10
20
30
40
50
60
x/H b
0
10
20
30
40
50
60
x/H b
Figure 2: Comparison of downwind centreline non-dimensional ground level concentrations measured in the wind
tunnel and predicted by AUSPLUME with modified dispersion. H s = H b - left, H s = 1.5H b - right, isolated stack top, cube at 90° - centre, cube 45° - bottom.
6. References
Bowers, J. F., Bjorkland, R. J., & Cheney, C. S.,
1979. "Industrial source complex model users
guide" report EPA-450/4-79-030. US-EPA.
EPAV, 1985. "Plume Calculation Procedures".
Publication 210. Melbourne: EPA Victoria.
CASANZ 2011 Conference - Auckland - 31 July - 2 August 2011
Paper 151
Maré, C., 2003. Effects of Stratification on Flow and
Dispersion Around Obstacles in Turbulent
Boundary Layers. PhD Thesis, University of
Surrey, School of Engineering.
Melbourne, W. H., & Taylor, T. J., 1994. Plume
Rise and Downwash Wind Tunnel Studies:
Combustion Turbine Unit 4 Sayreville. TR-235274
Draft
Final
Report,
Monash
University,
Department of Mechanical Engineering, Clayton.
Schulman, L. L., Strimaitis, D. G., & Scire, J. S. (2000). Developemnt and evaluation of the PRIME plume rise and building downwash model. Journal of Air & Waste Management Association , 8, 708‐717. CASANZ 2011 Conference - Auckland - 31 July - 2 August 2011
Paper 151
Appendix A
Table A- 1: Statistical summary of comparison of downwind centreline non-dimensional ground level
concentrations measured in the wind tunnel and predicted by AUSPLUME in ‘default’ configuration.
Undisturbed H b
Neutral
Normalised Mean
Stable 1.3
Undisturbed 1.5H b
Stable 1.1
Neutral
Stable 1.3
Stable 1.1
0.82
0.95
3.06
0.55
1.29
2.04
2.05E+02
1.34E+01
1.22E+02
8.62E+10
2.17E+08
5.38E+10
Fractional Bias (FB)
0.19
0.05
-1.02
0.57
-0.25
-0.69
F1.5
0.75
0.25
0.06
0.06
0.31
0.00
F3
0.88
0.81
0.25
0.75
0.75
0.31
NMSE
Cube 90 H b
Neutral
Stable 1.3
Cube 90 1.5H b
Stable 1.1
Neutral
Stable 1.3
Stable 1.1
Normalised Mean
1.42
1.37
2.42
0.96
1.21
2.67
NMSE
0.10
0.29
0.58
2.72
0.65
3.94
-0.35
-0.31
-0.83
0.04
-0.19
-0.91
F1.5
0.88
0.38
0.31
0.88
0.25
0.19
F3
1.00
0.94
0.75
0.88
0.81
0.38
Fractional Bias (FB)
Cube 45 H b
Neutral
Stable 1.3
Cube 45 1.5H b
Stable 1.1
Neutral
Stable 1.3
Stable 1.1
Normalised Mean
0.82
0.60
0.75
0.65
0.68
1.79
NMSE
0.08
0.52
0.22
0.34
0.85
0.64
Fractional Bias (FB)
0.20
0.50
0.28
0.42
0.38
-0.57
F1.5
0.81
0.19
0.50
0.25
0.38
0.25
F3
1.00
0.94
1.00
0.94
0.75
0.81
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Paper 151
Table A- 2: Statistical summary of comparison of downwind centreline non-dimensional ground level
concentrations measured in the wind tunnel and predicted by AUSPLUME in ‘default’ configuration.
Undisturbed H b
Neutral
Normalised Mean
Stable 1.3
Undisturbed 1.5H b
Stable 1.1
Neutral
Stable 1.3
Stable 1.1
1.31
1.29E+0
2
0.94
1.38
0.88
0.95
0.45
7.71E+07
5.27E+09
5.44E+10
1.98E+24
2.92E+28
-0.26
0.06
-0.32
0.13
0.05
0.76
F1.5
0.75
0.63
0.19
0.69
0.50
0.19
F3
0.88
0.75
0.63
0.81
0.56
0.38
NMSE
Fractional Bias (FB)
Cube 90 H b
Neutral
Normalised Mean
Stable 1.3
Cube 90 1.5H b
Stable 1.1
Neutral
Stable 1.3
Stable 1.1
1.67
1.48
2.44
1.44
1.45
2.48
2.38E-01
1.15E-01
6.09E-01
2.31E+00
6.37E-01
3.24E+00
-0.50
-0.39
-0.84
-0.36
-0.37
-0.85
F1.5
0.56
0.63
0.31
0.63
0.25
0.31
F3
1.00
0.94
0.75
0.88
0.75
0.50
NMSE
Fractional Bias (FB)
Cube 45 H b
Neutral
Normalised Mean
Stable 1.3
Cube 45 1.5H b
Stable 1.1
Neutral
Stable 1.3
Stable 1.1
0.93
0.64
0.76
0.89
0.78
1.75
1.35E-01
3.21E-01
1.96E-01
2.07E-01
3.90E-01
5.69E-01
Fractional Bias (FB)
0.07
0.44
0.28
0.12
0.25
-0.54
F1.5
0.75
0.25
0.63
0.75
0.44
0.25
F3
1.00
1.00
1.00
0.94
0.88
0.81
NMSE
CASANZ 2011 Conference - Auckland - 31 July - 2 August 2011
Paper 151