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Comparison of STEVE and ENVI-met as temperatureprediction models for Singapore contextWong Nyuk Hien a , Marcel Ignatius a , Anseina Eliza a , Steve Kardinal Jusuf b & RositaSamsudin ba Department of Building, National University of Singapore, 4 Architecture Drive, Singapore,117566b Centre for Sustainable Asian Cities, National University of Singapore, 4 Architecture Drive,Singapore, 117566

To cite this article: Wong Nyuk Hien, Marcel Ignatius, Anseina Eliza, Steve Kardinal Jusuf & Rosita Samsudin (2012):Comparison of STEVE and ENVI-met as temperature prediction models for Singapore context, International Journal ofSustainable Building Technology and Urban Development, 3:3, 197-209

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Technical Paper

Comparison of STEVE and ENVI-met as temperature prediction models for Singapore context

Wong Nyuk Hiena*, Marcel Ignatiusa, Anseina Elizaa, Steve Kardinal Jusufb and Rosita Samsudinb

aDepartment of Building, National University of Singapore, 4 Architecture Drive, Singapore 117566; bCentre for Sustainable AsianCities, National University of Singapore, 4 Architecture Drive, Singapore 117566

(Received 21 August 2012)

In urban areas, natural land soil has been replaced by asphalt roads and concrete buildings, which absorb and retain moreheat during the day, creating the Urban Heat Island (UHI) phenomenon. Current studies show that UHI impact mitigationstrategies are to increase the open spaces to allow urban ventilation and plant green cover. To complement this, atemperature prediction model could be effective for simulating and quantifying the temperature reduction for everydeveloped strategy. This paper will look into two prediction methods: STEVE and ENVI-met. Screening Tool for EstateEnvironment Evaluation (STEVE) is a prediction tool which is able to calculate the Tmin, Tavg and Tmax of the point ofinterest for certain urban settings. The temperature at that particular point is the result of its surrounding environment withinthe buffer zone. Output data from STEVE will be used as a database for a Geographic Information System (GIS) to producetemperature maps. ENVI-met is a Computational Fluid Dynamics (CFD) based micro-climate and local air quality model.It calculates temperature within the interval times for 24 to 48 hours. The calculation is based on the grid (x,y) with aspecified grid distance. This resolution allows analysis of small-scale interactions between individual buildings, surfaces andplants. The major differences between the models are the wind-speed variable, raster map, surface temperature and the localclimate context. STEVE calculation focuses on typical calm day conditions which excludes the wind speed variable, whileENVI-met consider it as one of the parameters. The GIS raster map generated from STEVE predicted temperature is basedon a buffer zone with specified diameter, while ENVI-met is based on grid pixels or cells which produces temperature mapsin more detail resolution. The objective of this study is to compare both prediction models so as to understand their benefitsand limitations, in order to justify which model is more appropriate for a tropical urban context, and in this case Singapore.

Keywords: prediction tool; ENVI-met; STEVE; urban heat island; tropical climate

1. Introduction

The Urban Heat Island (UHI) phenomenon is a condition in

which temperatures in urban areas are higher than their

surroundings. This is as a result of urbanisation which

increases anthropogenic heat emissions in urban centres,

whilewater,wind and greenery that can help to cool down an

urban area have concurrently decreased. Also, due to a

greater area of asphalt roads and concrete buildings, more

heat is absorbed and retained, while the heat reflection ratio

decreases. A temperature prediction model could be effec-

tive for simulating andquantifying the temperature reduction

for any strategies or mitigation methods proposed. The

amount of time consumed in order to measure local urban

temperature bymean of simulation has been one of the main

obstacles in performing urban micro-climate analysis, while

the expertise of the simulation software used can also be

considered as another factor. On the other hand, Screening

Tool forEstateEnvironmentEvaluation (STEVE) is offering

an alternative method to predict the local urban temperature

of a certain tropical urban area (STEVE was developed

and built based on field measurement data in the

Singapore context). This paper focuses on comparing two

different applications for assessing temperature prediction

on estate level: STEVE and ENVI-met.

STEVE, developed by Dr. Steve Kardinal Jusuf from

the National University of Singapore, is an application

that calculates the minimum temperature (Tmin), average

temperature (Tavg), and maximum temperature (Tmax) of a

certain point of interest for either the existing or future con-

dition (proposed master plan) of the estate or some specific

urban area [1]. The temperature predicted at that particular

point is as a result of the surrounding environment within the

buffer zone. Currently, the STEVE application serves as a

plug-in or tool which is used under the Geographic Infor-

mation System (GIS) platform named ArcGIS. In the end, for

the purposeof amoregraphical urban analysis, data calculated

from STEVE is used within ArcGIS to produce urban

temperature maps.

ENVI-met is a Computational Fluid Dynamics (CFD)-

based micro-climate and local air quality model [2].

The software, developed by Prof. M. Bruse from the

University of Mainz and his team, is a three-dimensional

ISSN 2093-761X print/ISSN 2093-7628 online

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*Corresponding author. Email: marcel.ignatius@nus.edu.sg

International Journal of Sustainable Building Technology and Urban Development

Vol. 3, No. 3, September 2012, 197–209

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non-hydrostatic micro-climate model including a simple

one dimensional soil model, a radiative transfer model and

a vegetation model [3]. Technically, ENVI-met was

designed to simulate the surface-plant-air interactions

within the limited grid cells size of a certain urban

environment, with a typical resolution of 0.5 to 10m in

space and 10 seconds in time. For this study, ENVI-met

was deployed to calculate temperature within the interval

times for 24 to 48 hours. The calculation is based on the

grid (x,y) with specified grid distance. This resolution

allows analysis of small-scale interactions between

individual buildings, surfaces and plants.

The major differences between STEVE and ENVI-met

are wind-speed variable consideration, raster map output,

surface temperature and the local climate context.

The STEVE calculation focuses on calm day conditions

which excludes the wind speed variable, while ENVI-met

includes it as one of the calculation variables. The GIS

raster map that is produced from the STEVE model is

based on buffer zonewith specified diameter (in this case 50

metre radius), while ENVI-met is based on pixels formed

by the grid which will givemore detailed resolution. ENVI-

met is able to provide surface temperature prediction

at different heights while the STEVE calculation is

based on a pedestrian height of 2 metres. Hypothetically,

STEVE, which was developed based on 3 years local data

measurement, is deemed to be more appropriate and

accurate for tropical urban areas, compared to ENVI-met.

The objective of this study is to give a comparison of

both prediction models to understand their benefits and

limitations. By all means, the authors do not intend to

judge if one approach is better than the other. The study is

more of a justification for selecting a proper and efficient

method for analysing tropical urban micro-climate (in this

case, from the Singapore context).

2. Literature review

2.1 Urban heat island

UHI phenomenon is a condition in which temperatures in

urban areas are higher than their surroundings. Isotherms on

a map show an urban area with higher temperatures

emerging like a warm island floating on a cooler sea. This is

the origin of the name of “UrbanHeat Island”. Alterations of

the urban surface by people results in diversemicro-climates

whose aggregate effect is reflected by the heat island.

Building, paving, vegetation, and other physical elements of

the urban fabric are the active thermal interfaces between the

atmosphere and land surface. High-building density and

change of street surface may lead to overheating by human

energy release and absorption of solar radiation on dark

surfaces and buildings [4]. The composition and structure

within the urban canopy layer, which extends from the

ground to about roof level, largely determines the thermal

behaviour of different sites within a city [5,6].

UHI can also be observed in every town and city as the

most obvious climatic manifestation of urbanisation [7].

In Japan, Tokyo’s average temperature has increased by

about 38C, and that of Osaka has increased by 28C over the

past 100 years. Cumulatively, since it has been reported

that global warming has raised Japan’s average tempera-

ture by about 18C, the temperature increase due to the UHI

effect is probably about 28C degrees in Tokyo and about

18C in Osaka [8]. In Washington and Tokyo, a rate of one-

half to one degree Fahrenheit increase every 10 years was

observed in the context of the summer’s maximum

temperatures during the last 30–80 years [4].

Contextually, there were some studies conducted to

investigate the Singapore UHI, as early as 1964, including

one conducted byNieuwolt [9]where it was found that there

was 3.58C temperature difference between the city area and

airport area. The city rural temperature differences

phenomena was believed to be due to the greater absorption

of solar radiation and to reduced evapotranspiration in the

city. In a following study byChia [10], the author considered

the variations in cloud pattern effects on the micro-climate,

where she found out that a combination of low solar

radiation receipts and low wind speed together with a low

cloud ceiling reduced the city rural temperature and relative

humidity differences. On a more advance approach, Nichol

[11] presentedUHI in Singapore through the remote sensing

technology, where it recorded roughly over 48C difference

was observed from the satellite image of Singapore. The

observation of UHI in Singapore provides evidence that the

local buildings do have a great impact on the local climate.

Diurnal UHI dynamics exploration was conducted by Roth

[12], in the spring and summer of year 2001, and showed

night time heat island magnitudes of up to 48C with the

lowest temperatures observed in densely vegetated areas.

Amore recent studybyWongandChen [13] investigated

the severity and impact of UHI on environmental conditions

and identified the possible causes by using thermal satellite

imaging and mobile survey. In Singapore, the satellite

image shows the UHI effect during the daytime. The “hot”

spots are normally observed on exposed hard surfaces in the

urban context, such as industrial areas, the airport and

Central Business District (CBD) areas. The satellite image

also shows some “cool” spots, which aremostly observed on

the large parks, the landscape in-between the housing estates

and the catchment area.

2.2 Mitigating UHI

Urban morphology is a major factor that influences the

thermal behaviour in the city and creates UHI impact. Oke

stated thatdesigns for street canyons inhigh- andmid-latitude

cities should (1) maximise shelter from wind for pedestrian

comfort, (2) maximise dispersion of air pollutants, (3)

minimise urban warming to reduce the need for space

cooling, and (4)maximise solar access [14].Oke’s analysisof

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urban geometry in relation to air-flow patterns, radiation

exchange, and thermal behaviour resulted in a range of

canyon geometries and building densities that achieve a

“zone of compatibility”. Another study done by Arnfield

revealed that calculation of irradiances on urban canyon

walls and floors provide quantitative guidelines on street

geometry [15]. He found that irradiance values generally

were smaller for canyon walls than for floors, and that

controllingcanyon-floor irradiancewasmore critical at lower

latitudes because of higher solar angles. North-South street

orientation provided less summer and more winter canyon-

floor irradiance than east-west street orientation. Using the

Cluster Thermal Time Constant (CTTC) model, Swain and

Hoffman carried out a study where in hot regions, cities with

North-South streets haveweakerurbanheat-island intensities

(0.88C) than cities with East-West streets [16]. Their

sensitivity analysis illustrated how increasing aspect ratios

(canyon height to width) results in a stronger daytime “cool

island” due to increased shade, and a weaker nocturnal heat

island due to appreciably reduced maximum temperature.

2.3 Screening Tool for Estate Environment Evaluation(STEVE)

STEVE has been developed based on the air temperature

prediction models [1]. These prediction models were

based on the empirical data collected over a period of close

to 3 years as part of the development of an assessment

method to evaluate the impact of estate development,

which includes the assessment method of the existing

greenery condition [17] and the greenery condition for a

proposed master plan in an estate development [18].

In the development of the empirical model, air

temperature data that had been gathered in the previous

studies were combined with the most recent data, which

includes estate-wide and canyon types of measurements.

The measurement points cover various types of land uses,

including residential, commercial, business park, edu-

cation, industrial, park, open space and sport facility.

The daily minimum (Tmin), average (Tavg) and

maximum (Tmax) temperature of each point of measure-

ment were calculated as the dependent variables of the air

temperature prediction model. The independent variables

for the models can be categorised into:

Climate predictors: daily minimum (Ref Tmin),

average (Ref Tavg) and maximum (Ref Tmax) temperature

at reference point; average of daily solar radiation

(SOLAR). For the SOLAR predictor, average of daily

solar radiation total (SOLARtotal) was used in Tavg

models, while average of solar radiation maximum of the

day (SOLARmax) was used in the Tmax model. SOLAR

predictor is not applicable for the Tmin model. These data

are obtained from the weather station.

Urban morphology predictors: percentage of pavement

area over R 50m surface area (PAVE), average height to

building area ratio (HBDG), total wall surface area (WALL),

Green PlotRatio (GnPR), sky view factor (SVF) and average

surface albedo (ALB). These data are provided by the

government agency and cross-checked by field survey.

Before the model was developed, the radius of the

influence area was determined. A radius of 50 metres was

deemed as a suitable one after a series of influence area

studies comparing radius values from 25–100m (Figure 1).

The temperature models were then developed by examining

the variables’ regression coefficient values and their

correlations with the dependent variables.

Wind speed, one of the most common variables, was

excluded in the model development, since the models focus

on calm day conditions (wind speed , 3m/s). Meanwhile

another common variable, altitude was excluded from the

model development since the data collected showed altitude

has very little influence on air temperature condition.

In the first stage of model development, trend analysis

was carried out to identify and discuss the behaviour of the

models’ variables (based on the data collected in field

measurement), by examining the variables’ regression

coefficient values and their correlations with the

dependent variable. Not all of the independent variables

are significant. However, it is important to analyse how

these variables behave in determining the air temperature.

The next stage is to develop the air temperature prediction

models that use only the significant variables.

The air temperature regression models were developed

based on the data collected over a period of close to 3

years. It is necessary to validate the models with another

period of measurement data, in this case, with fairly

clear and calm day conditions (wind speed , 3m/s). The

air temperature prediction models can be written as

follows:

Tmin ð8cÞ ¼ 4:061þ 0:839Ref Tminð8CÞ

þ 0:004PAVE ð%Þ2 0:193GnPR

2 0:029HBDGþ 1:339E2 06WALL ðm2Þ

Tavgð8CÞ ¼ 2:347þ 0:904Ref Tavgð8CÞ þ 5:786E

2 05 SOLARtotal ðW=m2Þ þ 0:007PAVE ð%Þ

2 0:06GnPR2 0:015HBDGþ 1:311E

2 05WALL ðm2Þ þ 0:633 SVF

Tmaxð8CÞ ¼ 7:542þ 0:684Ref Tmaxð8CÞ

þ 0:003 SOLARmaxðW=m2Þ

þ 0:005PAVE ð%Þ2 0:016HBDGþ 6:777E

2 06WALL ðm2Þ þ 1:467 SVF þ 1:466ALB

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Since it is impossible to put the all the theoretical

background and prediction model development into this

paper, the author will only underline the essential elements

of STEVE, while a more detailed explanation, data

validation, and its application for other studies can be read

from the related papers, which can be found in [17–21].

3. Hypotheses

Both ENVI-met and STEVE have the same objective: to

create a temperature map in the shape of raster images.

In this case, since STEVE was developed and built based

on local climate data (Singapore), in terms of predicting

the temperature output, it will be more representative for

reflecting the real condition, compared to ENVI-met.

However, since STEVE has a limitation on creating the

raster image based on interpolating the result from a

100 £ 100m grid; ENVI-met definitely produces a better

and more accurate resolution since it simulates the result

pixel by pixel and grid dependent.

4. Methodology

Comparisons between STEVE and ENVI-met were

drawn on a scenario comprising five building massings,

each 48m in height. In STEVE, the ground surface

was assumed to be fully paved (Green Plot Ratio or

GnPR value is zero). The same setting was also applied in

ENVI-met, where the soil is set to be fully pavement

(concrete).

The needed boundary condition for STEVE has been

described in the previous chapter, whereas several

parameters from both climate and urban morphology

predictors (in this case, from the 5 buildings scenario) were

calculated to produce the predicted temperature. While for

ENVI-met, the buildings aremodelled accordingly with the

Figure 1. Sample of urban area measurement point in influence area radius of 25m, 50m, 75m and 100m.

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same dimensions as in the STEVE calculation (Figure 2),

and for the climatic condition, the valueswere set within the

configuration setting (CF) file, based on the reference

weather data used by STEVE aswell. TheCF inputs needed

for simulation are initial temperature (averaged from the

reference data), wind speed, and relative humidity (RH).

For ENVI-met, the only difference is that it does not use

dynamic temperature profile, compared to STEVE which

uses a 24-hourly temperature profile in order to produce

Tmin, Tavg and Tmax. Because STEVE focuses on low

wind speed conditions, before comparing the result from

both sides the authors embarked on a mini parametric study

for different wind speeds, in order to explore whether

ENVI-met could simulate properly under the low wind

speed condition. In the end, these boundary conditions for

both methods were made as similar as possible in order to

embark on an appropriate comparison study.

For this study purpose, 6 March 2010 has been selected

as the simulation day, since it is not just a typical calm day

(suitable for STEVE calculation), but also the hottest of

year 2010. Hence, the boundary condition configurations

are as follow:

(1) For STEVE. Based on hourly data generated by the weather

station, the maximum, minimum and average

temperature on 6March 2010 are: 31.28C, 25.498C,

and 27.988C respectively.. Daily solar radiation total (SOLARtotal):

5062.945W/m2.. Solar radiation maximum of the day (SOLAR-

max): 683.5W/m2.

(2) For ENVI-met. Simulation date: 6 March 2010.. Initial temperature atmosphere: 301K (or 27.988C,

average temperature).. Average Relative Humidity: 70%.. Roughness length: 0.1 (for urban area).

5. Data and analysis

5.1 ENVI-met simulation for different wind speed

By observing the temperature maps from five different

scenarios regarding the wind speed condition (Figure 3), it

appears that ENVI-met simulation results are not quite

accurate for the low wind-speed condition (below 1m/s),

as they show inconsistencies of wind directions. A more

consistent wind direction can only be seen when the

speed is set starting from 1m/s. Therefore, in order to

compare it with the STEVE result, the proposed scenario

was simulated by ENVI-met with minimum wind speed

of 1m/s.

5.2 Comparison of STEVE and ENVI-met

To create a raster map within ArcGIS, STEVE deployed a

circular buffer zone with a 50m radius. Then, STEVE

calculated and predicted the temperature in that particular

buffer centre point which was influenced by the

environment condition within. On the other hand, the

ENVI-met calculation is technically based on pixels with

the particular size of the grid as defined within the ‘create

model domain’ settings, and it uses a uniform mesh with a

maximum grid size of about 300 £ 300 £ 35 cells with the

horizontal extension ranging between 0.5–10m and a

typical vertical height of 1 to 5 metres (this study use 5

metres for each x, y, z grid cell)

These two different methods appear to vary the raster

map output as well (Figure 4). For instance, in the red

highlighted location, STEVE displayed an equal or

uniform temperature within the gaps between buildings,

while ENVI-met showed temperature variation on same

location. On a bigger picture, ENVI-met showed the same

condition between the building gaps, while STEVE has a

different temperature prediction. The explainable reason

for this result is the different grid sizing for both

applications; ENVI-met provides better resolution as the

temperature is calculated for every grid cell, while the

STEVE raster image is based on a 100 £ 100m grid data

interpolation (based on a 50m radius buffer zone). Thus, in

order to have better resolution of the temperature map, the

authors were experimenting by adding more buffer zones

within ArcGIS for the STEVE calculation.

To achieve a better raster map resolution, this study

tried another scenario where 27 additional measurement

points were deployed within the same building arrange-

ment. While normal STEVE methodology uses an attached

buffer zone, this study overlapped all those additional buffer

Figure 2. Model settings on GIS using STEVE (left) and ENVI-met (right).

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boundaries as a result of additional measurement points

(Figure 5).

The diagrams above (Figures 5 and 6) display the

difference of utilising more measurement points compared

to the previous attempt in order to achieve a more

accurate temperature map. The highlighted area shows the

difference between the two methods. In the previous

attempt, only the centre portion of the building gaps

showed a low temperature while with the overlapping

buffer zones, every gap in-between buildings indicated

a low temperature result. Within ArcGIS, the colour

coding of a certain buffer zone depends on its surrounding

measurement point location (interpolation method).

Therefore, to generate a detailed temperature map

Figure 3. ENVI-met simulation result with different wind speed.

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prediction within a small-scale city parcel using STEVE

might be misleading compared to using ENVI-met.

When they are compared next to each other, the raster

map generated from STEVE with the overlapping buffer

zone method showed a similar pattern to the one generated

from ENVI-met (Figure 7). The noticeable difference

between these two lies at the maximum-minimum

temperature prediction. The temperature prediction from

ENVI-met displays a higher temperature (values ranging

from 302.33 K to 303.05 K (29.338C to 30.358C))

compared to STEVE, where the temperature values

range from 32.18C to 32.688C.

5.3 Wind speed

As previously mentioned, the STEVE calculation focuses

on calm day conditions, which excludes the wind speed

variable, while the ENVI-met calculation considers this.

Figure 8 shows the comparison of STEVE and ENVI-met

if the initial wind speed values for the latter were varied.

ENVI-met has been tested for different wind speed values

of 1, 3, and 5m/s. As expected, the result showed that

when the wind speed increased, the temperature was

reduced. The simulation displayed that for 1m/s wind

speed, the temperature ranged from 29.348C to more than

29.818C, for 3m/s the temperature range was from

28.408C to 29.118C, and for 5m/s the range was 27.938C to

28.638C. On the other hand, the STEVE temperature

prediction for a calm day ranges from 32.18C to 32.688C.

5.4 Temperature profile difference

It is important to note that results from both simulations

show the predicted temperature from ENVI-met was

constantly lower than the STEVE result. In order to

explore this, another study was done to test whether ENVI-

met under predicts the initial temperature (since this tool is

mostly used in temperate country case studies). The added

scenario was to set both STEVE and ENVI-met simulating

Figure 5. Additional buffer zone on STEVE calculation to improve the accuracy of temperature map.

Figure 4. Comparison of temperature map result from both STEVE and ENVI-met.

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an empty land without buildings, and then the result was

compared to the previous one.

Figure 9 below indicates that the prediction tempera-

ture from ENVI-met for empty land (blue line) is lower

than STEVE (red line). Predicted temperatures with

buildings added into the parcel also indicate lower values

for ENVI-met (purple line) compared to STEVE (green

line). Therefore, the diagram provides a proper expla-

nation where initial temperature for ENVI-met happens to

be lower than STEVE, which means it under predicts the

simulated temperature.

Another observation revealed that both STEVE and

ENVI-met showed similar temperature profile trends.

ENVI-met displays a smoother curve line result, mainly

because it was using only an initial boundary condition to

run the simulation, while STEVE implemented a full one

day weather data profile (reference maximum, average,

and minimum temperature) as the input data for generating

the predicted temperature. For this study purpose, 6 March

2010 was the selected day, since it was not just a typical

calm day, but also the hottest of 2010. Since STEVE was

using the actual data, the temperature profile might be as

smooth as the one which is generated by ENVI-met, since

the field measurement data tends to vary depending on the

weather, while the ENVI-met simulation was running

based on ideal conditions.

Figure 6. Temperature map comparison as a result of overlapping buffer areas on STEVE calculation.

Figure 7. STEVE and ENVI-met temperature map (afternoon) result comparison.

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Table 1 reveals that the predicted temperature

difference for the whole day for empty land between

STEVE and ENVI-met could reach 3.778C maximum and

2.388C on average, while the temperature difference when

buildings were added afterwards could reach 3.508C

maximum and 2.268C on average. The temperature

reduction from an empty land scenario to the occupied

one was higher in STEVE (0.998C maximum reduction,

0.438C on average) compared to ENVI-met (0.668C

maximum reduction, 0.118C on average).

5.4 Adjustment of ENVI-MET temperature profile

Comparison between STEVE and ENVI-met above shows

that there is a 28C to 38C difference between the two

prediction models. To achieve the most similar tempera-

ture profile between these two applications, initial

temperature in ENVI-met should be raised into 28C and

38C become 302K (298C) and 303K (308C). The

temperature profile generated by this revised ENVI-met

simulation displayed a smoother curved line compared

with the STEVE profile (6 March 2010 data). Therefore, to

have the proper assessment on the temperature profile

curved line, this study attempted to use different dates

(calm and hot days), which were 28 February 2010 and 26

November 2009.

Figure 10 and Table 2 below show the comparison of

ENVI-met simulation with three different initial tempera-

tures (300K, 302K, and 303K) and STEVE simulation

with three different dates (6 March 2010, 28 February

2010, and 26 November 2009). The results indicated that

Figure 8. STEVE and ENVI-met wind profile comparison.

Figure 9. Comparison of STEVE tool and ENVI-met chart.

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Table

1.

Predictiontemperature

difference

betweenSTEVEandENVI-met.

Tim

e(24hrs)

Massing

(STEVE)

Empty

(STEVE)

Tem

p.diff.

(STEVE)

Point4&

5(ENVI-met)

Empty

(ENVI-met)

Tem

p.diff.

(ENVI-met)

STEVE-ENVI-met

(empty)

STEVE-ENVI-met

(massing)

1:00

27.26

27.47

20.21

25.03

25.34

20.31

2.13

2.23

2:00

26.92

27.11

20.19

24.85

25.06

20.21

2.05

2.07

3:00

26.55

26.72

20.17

24.64

24.82

20.18

1.90

1.91

4:00

26.19

26.35

20.16

24.48

24.61

20.13

1.74

1.71

5:00

25.85

25.99

20.14

24.30

24.42

20.12

1.57

1.55

6:00

25.71

25.85

20.13

24.13

24.24

20.11

1.61

1.58

7:00

25.88

26.02

20.14

23.96

24.09

20.13

1.93

1.92

8:00

25.76

25.89

20.14

23.84

23.97

20.13

1.92

1.92

9:00

26.41

26.58

20.17

24.89

25.22

20.33

1.36

1.52

10:00

28.30

28.56

20.26

26.04

26.43

20.39

2.13

2.26

1:00

29.82

30.30

20.49

27.18

27.69

20.51

2.61

2.64

12:00

30.75

31.45

20.70

27.87

28.46

20.59

2.99

2.88

13:00

31.51

32.37

20.86

28.66

29.16

20.50

3.21

2.85

14:00

32.10

33.09

20.99

29.31

29.71

20.4

3.38

2.79

15:00

30.62

31.29

20.67

29.44

30.06

20.62

1.23

1.18

16:00

30.65

31.32

20.67

29.5

30.16

20.66

1.16

1.15

17:00

30.85

31.57

20.72

29.35

29.99

20.64

1.58

1.50

18:00

31.15

31.93

20.78

28.97

29.50

20.53

2.43

2.18

19:00

30.57

31.23

20.66

28.17

28.43

20.26

2.80

2.40

20:00

30.48

31.12

20.64

27.24

27.51

20.27

3.61

3.24

21:00

30.11

30.67

20.55

26.61

26.90

20.29

3.77

3.50

22:00

29.48

29.90

20.41

26.04

26.41

20.37

3.49

3.44

23:00

28.99

29.28

20.29

25.67

26.00

20.33

3.28

3.32

24:00

28.63

28.90

20.27

25.34

25.65

20.31

3.25

3.29

Max

32.10

33.09

20.99

29.50

30.16

20.66

3.77

3.50

Min

25.71

25.85

20.13

23.84

23.97

20.11

1.16

1.15

Avg

28.77

29.21

20.43

26.48

26.83

20.35

2.38

2.29

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ENVI-met with initial temperature set at 302K and 303K

shows a similar profile to STEVE especially at 8.00am to

11.00am time, but the gap became bigger afterwards when

the profile went to the maximum temperature. Another

observation was that the STEVE predicted maximum

temperature was shifting for each date. On 28 February

2010 profile, the maximum temperature occurred at

4.00pm, which was similar for the three ENVI-met

profiles, but the value difference between both prediction

models was quite significant, ranging from 1.888C to

4.108C.

6. Discussions and conclusions

The first comparison study of the temperature prediction

model between STEVE and ENVI-met revealed that the

ENVI-met raster map output resolution was more detailed

compared to STEVE, as the ENVI-met calculation is

based on each grid cell calculation while STEVE is based

on the attached 100 £ 100m buffer zones. Thus, to

achieve better raster map resolution, additional measure-

ment points were required in STEVE. This experiment

demonstrated that the overlapping buffer zone method

gave a different raster map output compared to the initial

attached buffer zone. Because the ArcGIS data interp-

olation method was used to generate the raster image, the

temperature colour chart produced is measurement point

location dependent. Consecutively, a raster map with

more points with the overlapping buffer zone scenario

appears to have a similar temperature map distribution

compared to the ENVI-met result.

From the urban analysis capability point of view,

STEVE might be more appropriate for handling a city-

scale study area, since it does not have a limitation for the

grid sizing, while ENVI-met does. In the current version,

the latter has a restraint where the grid sizing has a

maximum number of grid cells, which makes it impossible

to simulate the micro-climate condition of a bigger scale

city area with appropriate detail included. Currently, the

software developer explained this is mainly because

ENVI-met runs on a standard x86 personal computer

runningWindows XP or Vista and does – at the moment –

not take advantage of more than one processor or

distributed computing [22].

Another notable finding from the study is that the

predicted temperature from ENVI-met was lower com-

pared to STEVE. An additional scenario was completed by

simulating an empty land using both STEVE and ENVI-

met, where lower temperature values were shown for the

latter method. The output from these two scenarios

indicated that initial temperatures for ENVI-met are lower

than for STEVE, which means it under-predicts the

temperature parameter.

The 2–38C difference most probably comes from other

“background” or other factors from the data collected

from the field for developing STEVE, whereas in ENVI-

met, the simulation was running in ideal conditions. In the

current version, ENVI-met only considers interactions

between atmosphere, buildings, soils, vegetation, andwater

bodies, whereas the other factors such as anthropogenic

heat (which can be one of the “background” factors) are

most probably not captured within the ENVI-met

simulation.

STEVE, with its capability for analysing a city-scale

area for generating temperature maps, with much less time

needed to conduct the simulation, and having been

Figure 10. Comparison of STEVE tool and ENVI-met temperature chart.

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developed using local temperature conditions (tropical),

has several advantages compared to ENVI-met. Never-

theless, it does not mean ENVI-met is an inferior option,

since it also has capabilities for analysing other things such

as flow around and between buildings, turbulence, heat

and vapour exchange processes, etc., whereas the current

version of STEVE only focuses on local temperature

prediction. In the end, in terms of utilising a better

temperature prediction model, STEVE could be the better

and suitable choice for studying the tropical urban area.

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Table 2. Comparison of STEVE and ENVI-met temperature.

Time(24 hrs)

STEVE(06-Mar-10)

STEVE(28-Feb-10)

STEVE(26-Nov-09) ENVI-met on 300K ENVI-met on 302K ENVI-met on 303K

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Max 33.09 34.26 34.43 30.16 31.65 32.38Min 25.85 26.27 25.63 23.97 25.50 26.21Avg 29.21 29.83 29.37 26.83 28.28 28.98

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