microscale numerical prediction over montreal with the canadian external urban modeling system

Microscale Numerical Prediction over Montreal with the Canadian ExternalUrban Modeling System

SYLVIE LEROYER, STEPHANE BELAIR, AND JOCELYN MAILHOT

Meteorological Research Division, Environment Canada, Dorval, Canada

IAN B. STRACHAN

Department of Natural Resource Sciences, McGill University, Sainte-Anne-de-Bellevue, Canada

(Manuscript received 7 January 2011, in final form 1 June 2011)

ABSTRACT

The Canadian urban and land surface external modeling system (known as urban GEM-SURF) has been

developed to provide surface and near-surface meteorological variables to improve numerical weather pre-

diction and to become a tool for environmental applications. The system is based on the Town Energy

Balance model for the built-up covers and on the Interactions between the Surface, Biosphere, and Atmo-

sphere land surface model for the natural covers. It is driven by coarse-resolution forecasts from the 15-km

Canadian regional operational model. This new system was tested for a 120-m grid-size computational do-

main covering the Montreal metropolitan region from 1 May to 30 September 2008. The numerical results

were first evaluated against local observations of the surface energy budgets, air temperature, and humidity

taken at the Environmental Prediction in Canadian Cities (EPiCC) field experiment tower sites. As compared

with the regional deterministic 15-km model, important improvements have been achieved with this system

over urban and suburban sites. GEM-SURF’s ability to simulate the Montreal surface urban heat island was

also investigated, and the radiative surface temperatures from this system and from two systems operational

at the Meteorological Service of Canada were compared, that is, the 15-km regional deterministic model and

the so-called limited-area model with 2.5-km grid size. Comparison of urban GEM-SURF outputs with re-

motely sensed observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) reveals

relatively good agreement for urban and natural areas.

1. Introduction

With the overwhelming majority (80%) of Canadians

living in urban areas, the prediction of surface and near-

surface meteorological variables at the microscale

(;100 m) in urban areas takes on increased importance

for reasons that include health, safety, energy consid-

erations, and the prevention of human discomfort.

Climate change studies [Intergovernmental Panel on

Climate Change (IPCC) reports; Houghton et al. 2001]

suggest that extreme weather events such as heat waves

will become more frequent and will have larger ampli-

tude. The consequences of such meteorological events

on population death rates have been recently studied in

different urbanized regions in the world (Smargiassi

et al. 2009; D’Ippoliti et al. 2010; Smoyer et al. 2000;

Huang et al. 2010; Tong et al. 2010). In such studies, air

temperature (or apparent air temperature) measured

at airport stations is assumed to be representative of

the temperature in cities. Smargiassi et al. (2009) have

used radiative surface temperature retrievals from

space-based remote sensing to assess intraurban vari-

ability. Their results suggest that the potential for ther-

mal discomfort varies across the Montreal urban area.

Unfortunately, the statistical method used in Smargiassi

et al. (2009) is limited by the number of satellite images

considered (two dates), and by the fact that the radia-

tive surface temperature is considered, rather than the

screen-level air temperature, the latter being more

representative of dwellers’ thermal comfort. These two

meteorological variables differ because of complex phys-

ical processes in the urban surface layer (Roth et al. 1989;

Arnfield 2003).

Corresponding author address: Dr. Sylvie Leroyer, Meteoro-

logical Research Division, Environment Canada, 2121 Trans-

Canada Highway, Dorval QC H9P1J3, Canada.

E-mail: [email protected]

2410 J O U R N A L O F A P P L I E D M E T E O R O L O G Y A N D C L I M A T O L O G Y VOLUME 50

DOI: 10.1175/JAMC-D-11-013.1

� 2011 American Meteorological Society

In response to this growing need to more precisely

quantify the relations between large-scale meteorology,

street-level air temperature, and inside-building tem-

perature across a metropolitan area, significant effort

has been made to improve the physical representation of

urban environments in current numerical weather pre-

diction (NWP) systems. High-resolution weather fore-

casts in urban areas can have both short- and long-term

applications. For the short term, they can give decision

makers on-time information for the determination of

potential dangerous areas during extreme weather

events. For the long term, they can be used as a tool for

scenario-based urban planning projects to mitigate the

urban heat island effects and to minimize energy con-

sumption. The impact of foreseeable scenarios on re-

gional climate can also be evaluated.

Adequate representation of urban surface processes

in atmospheric systems requires considerable horizontal

resolution. Fortunately, the spatial resolution of NWP

systems has continued to significantly increase in the last

decade. The smallest grid size currently used in the op-

erational systems in Canada (Erfani et al. 2005) and in

France (Bouttier 2007) is 2.5 km, and is on the order of

1 km in the Met Office Unified Model (UM) (Clark

et al. 2009). An increase in horizontal resolution is often

associated with the development of a land surface ex-

change model in which the surface is seen as a combi-

nation of different tiles associated with different types

of covers, among which an urban tile can be represented

(Essery et al. 2003; Lemonsu et al. 2009; Le Moigne

2009).

However, inclusion of urban processes in operational

forecasts is not common because urban areas typically

represent only a small fraction of the coarse grid cells of

these models, and because the computational cost as-

sociated with sophisticated urban models is not negli-

gible. Clark et al. (2009) showed the evolution of the

representation of urban areas at the Met Office. The

implementation was first made with a simple model

that did not require more computational cost (Best

2005), which was replaced by a more complex model

because the oversimplified scheme failed to properly

reproduce the surface energy partitioning (Best et al.

2006). Meteo-France is another national center that has

included an urban surface model in one of their oper-

ational systems. Indeed, the Town Energy Balance

(TEB) urban surface model (Masson 2000) was im-

plemented in the Applications of Research to Opera-

tions at Mesoscale (AROME) system for the 2.5-km

forecasts (Bouttier 2007).

With these concerns in mind, the Environmental

Prediction in Canadian Cities (EPiCC) research net-

work (www.epicc.uwo.ca) was established to improve

the numerical systems used at the Meteorological Ser-

vice of Canada (MSC) to predict weather and air quality

in densely populated urban areas. The network used

modeling, remote sensing, and long-term measurements

(a 2-yr observational campaign) in Montreal and Van-

couver, two of Canada’s largest cities with quite differ-

ent climate and urban fabric.

In parallel to this research effort, an external land

surface modeling system, called the Global Environ-

mental Multiscale Surface (GEM-SURF) system, has

been developed at the MSC (Carrera et al. 2010;

Bernier et al. 2011) to better forecast high-resolution

meteorological fields at and near the surface at a rela-

tively low computational cost. Based on this approach,

the land surface component of MSC’s Global Envi-

ronmental Multiscale (GEM) model is integrated at

high resolution (with grid sizes of the order of 100 m),

in an ‘‘external’’ manner, that is, separately from the

fully three-dimensional model. The external surface

system is driven by meteorological fields obtained

from a coarser-resolution forecast system.

Since this external land surface modeling system is

expected to become an important component of MSC’s

operational environment in the next few years, the main

objective of the current study is to augment it with

a realistic representation of urban areas. A second ob-

jective is to compare the numerical results with obser-

vations and to show the added value of this system in

comparison with current MSC’s operational systems.

The urban version of the Canadian land surface mod-

eling system (urban GEM-SURF) is applied to the

Montreal metropolitan area during summertime to

examine and evaluate the microscale spatial structures

of surface and near-surface variables (e.g., in terms

of urban heat island effect) resulting from this city’s

heterogeneity.

This paper is organized as follows. Section 2 presents

the urban GEM-SURF system and the preprocessing

steps for the simulation over the Montreal metropolitan

area. It is followed (section 3) by the description of

the two datasets chosen for the evaluation of the results,

the long-term local measurements during the EPiCC

field experiment in Montreal at three sites, and two-

dimensional instantaneous Moderate Resolution Imag-

ing Spectroradiometer (MODIS) satellite imagery. Com-

parison of the surface energy budgets and near-surface

meteorological fields measured at the three sites with

numerical outputs is shown and discussed in section 4.

The predicted and observed urban heat island patterns

at the Montreal city scale are investigated in section 5

based on predicted radiative surface temperature from

different models and on MODIS satellite images. Con-

clusions are given in the last section.

DECEMBER 2011 L E R O Y E R E T A L . 2411

2. Method

a. The Canadian external urban and land surfaceforecast system (urban GEM-SURF)

Two recent studies have shown the benefits of using the

Canadian external land surface forecast system GEM-

SURF. Carrera et al. (2010) used the new land surface

system to examine the hydrological impact of mountain

snow in the South Saskatchewan River basin. Bernier

et al. (2011) adapted this experimental system to produce

high-resolution forecasts of snow properties and of

screen-level air temperature during the Vancouver 2010

Olympic and Paralympic Games. Both studies showed

improvements over other systems currently operational

at MSC for the prediction of snow conditions and of near-

surface air temperature.

Urban GEM-SURF first consists of downscaling atmo-

spheric forcing obtained from outputs at the surface and at

the lowest atmospheric level of a coarser-resolution three-

dimensional forecast model on a microscale-resolution

grid (120-m grid size in this study). The atmospheric

variables required for land surface modeling are the

downwelling radiation (solar and longwave), precipitation

rate, surface pressure, near-surface air temperature, hu-

midity, and winds. Based on Carrera et al. (2010) and

Bernier et al. (2011), spatial refinement of near-surface

air temperature is obtained by applying hydrostatic

corrections to the height difference between the high-

resolution (external system) and low-resolution (3D

atmospheric model) grids. Specific humidity is modified

by assuming a constant relative humidity. Finally, the

precipitation phase is modified according to the down-

scaled near-surface air temperature.

The surface is represented as an aggregation of soil and

vegetation, open waters, sea and lake ice, continental ice

(continental glaciers and ice sheets), and urban areas,

with each type of cover having its specific parameteriza-

tion scheme to simulate the relevant physical processes.

Urban areas and land surfaces predominate in the area

studied here. Land surfaces are represented with the In-

teractions between the Surface, Biosphere, and Atmo-

sphere land surface model (ISBA; Noilhan and Planton

1989; Belair et al. 2003a,b), and urban environments are

represented with TEB.

Several versions of the ISBA land surface scheme have

been developed throughout the years (Noilhan and

Planton 1989; Noilhan and Mahfouf 1996). The one used

in this study is a two-layer force-restore version adapted

and implemented for Canadian purposes (Belair et al.

2003a,b). In this version, thermal and hydrological ex-

changes between the soil and the atmosphere are consid-

ered through a soil-surface layer and a deeper root-zone

layer. The model requires effective gridcell information

on the vegetation type, on the soil texture, and on a set of

23 specific parameters that depend on the type of vegeta-

tion [including for instance, leaf area index (LAI), fraction

of vegetation coverage over the ground, albedo, aero-

dynamic roughness length, and heat capacity of vegeta-

tion]. The fraction of vegetation and the LAI also evolve

through the year.

The single-layer urban canopy model TEB is used to

represent the effects of urban morphology and materials

on the micrometeorology. Based on the canyon concept

(Oke 1981), computation of the surface energy budget

(SEB) and of the surface temperature is done separately

for the roof, the road, and the wall surfaces, considering

the shadowing effects and the radiative trapping inside

the streets, with isotropic properties. The model requires

effective gridcell information on the urban cover types

(fractional coverage of building—or roof, and of artifi-

cial surfaces—or road), on the canopy structure (build-

ing height, street aspect ratio, aerodynamic roughness

length), and on the urban fabric thermal and radiative

properties (albedo, emissivity, heat conductivity, and

heat capacity for each urban facet layer).

TEB has been extensively evaluated against mea-

surements in (i) offline mode with single point evaluations

(Masson et al. 2002; Lemonsu et al. 2004, 2010; Pigeon et al.

2008; Leroyer et al. 2010) and (ii) coupled mode with at-

mospheric models such as the Canadian GEM model

(Lemonsu et al. 2009), the French MesoNH model

(Lemonsu et al. 2006a; Lemonsu and Masson 2002; Sarrat

et al. 2006), and the Regional Atmospheric Modeling

System (RAMS; Freitas et al. 2007). It has never been

evaluated with the configuration used in this study.

b. Urban and land surface characteristics

Detailed and accurate representation of the heteroge-

neous land use–land cover (LULC) is required to forecast

meteorological variables at the 100-m scale. The required

surface inputs are listed in section 2a. For the Montreal

metropolitan region, three different databases are used

to obtain a more realistic urban and land surface char-

acteristics description; the methodology adopted is de-

scribed hereafter and illustrated in Fig. 1.

First, the Earth Observation for Sustainable Devel-

opment of Forests (EOSD) land cover map is provided by

Natural Resources Canada (NRCan) at a 25-m resolution

for all of Canada. This 36-class dataset provides a fairly

high-resolution and reliable description of the forested

area even though it sometimes fails to correctly identify

other cover types. For example, the urban areas, crops,

and shrubs in the Montreal region are poorly specified in

this dataset.

Second, the Canada Centre for Remote Sensing

(CCRS) land cover map is provided under the Earth


Science Sector’s (ESS) Program ‘‘Understanding Canada

from Space’’ (UCS), at a 250-m resolution and using

MODIS satellite scenes acquired in 2005. It is composed

of 39 cover types, including a uniform urban class de-

scribing metropolitan areas as well as smaller towns.

Classifications from these two databases were rear-

ranged to fit a predefined set of LULC classes (26 in total)

used for GEM’s version of ISBA. These two modified

datasets were then combined to give a coherent 120-m

map of the region (a temporary 120-m map), using the

EOSD dataset for forest and grass covers and CCRS for

remaining classes used to specify ISBA’s parameters.

The third database is an urban classification of the

Montreal metropolitan area provided by Lemonsu et al.

(2006b). The methodology is based on satellite imagery

and digital elevation model (DEM) datasets, and was

previously applied to Montreal, Vancouver, and Okla-

homa City, Oklahoma. For Montreal, a 60-m classifica-

tion was obtained using Landsat-7 satellite imagery, total

elevation from the Shuttle Radar Topography Mission

(SRTM-DEM) database, and bald Earth topography

from the Canadian Digital Elevation Dataset level 1

(CDED1). A decision-tree model was applied to obtain

12 final homogeneous classes, identified to be represen-

tative of North American cities’ urban fabric (see the

appendix). The parameters required by TEB were speci-

fied for each class considering aerial photographs. The

fraction of natural covers that exist in these urbanized

classes, with discrimination between trees, grass, and

bare soil, was also determined.

This classification is transposed on the final 120-m grid,

so that each grid cell on the new mesh contains four grid

cells of the classification. All urbanized pixels are selected

in priority. The other cover types and the urban areas for

the rest of the domain are selected from the temporary

120-m vegetation map mentioned above. The final 120-m

grid is represented on Fig. 2 with the main landmarks. As

the urban classification does not cover the entire area of

study, the west and south transitions are in evidence with

the disappearance of very sparse urbanized patches gen-

erally composed of classes ‘‘low-density suburbs’’ and

‘‘mix built and nature.’’

For urbanized pixels, the built-up fraction is considered

by the TEB scheme, and the natural cover fractions found

in each pixel are thus considered by ISBA.

c. Atmospheric forcings

This system is developed with the objective of be-

coming operational at MSC and covering all of Canada,

first in a one-way mode (this study), and in the near future

in a two-way mode (by using the surface turbulent fluxes

as a lower boundary condition for the atmospheric mod-

el’s vertical diffusion scheme). Therefore, forcings from

the operational 15-km regional deterministic model

(REG; Mailhot et al. 2006) were selected instead of

other operational products (like, e.g., the 2.5-km oper-

ational products mentioned in the introduction) as it is

the most reliable operational product with moderate

resolution covering all of Canada.

The REG model is used at MSC to produce 48-h

forecasts over North America 4 times per day (at 0000,

0600, 1200, and 1800 UTC) with a 6-h spinup, providing

a continuous cycle. It is worth noting, though, that there

were only two runs per day before 12 June 2009 (0000

and 1200 UTC), including the period used in this study,

and each run was used for a period of 12 h (6–17 h after

initialization). The REG configuration is based on a hy-

drostatic version of the model with 58 vertical levels; the

first level being at about 43 m above canopy level (ACL),

and with a time step of 450 s. Surface meteorological

FIG. 1. Schematic diagram of the methodology used to obtain the LULC classification over the Montreal

metropolitan area.


variables and fluxes are computed with ISBA. It can be

noted that soil temperature and soil water content are

assimilated every day at 0000 UTC considering screen-

level air temperature and humidity observations (Belair

et al. 2003a).

The GEM-SURF system has been shown to partially

correct the problem of the lack of resolution in the coarse

model for the prediction of surface and near-surface

meteorological variables, for the high-resolution hydrol-

ogy (Carrera et al. 2010) and for the small-scale topog-

raphy (Bernier et al. 2011). The small-scale representation

of the urban effects on surface and near-surface meteo-

rological variables is another issue that is investigated in

this study, whereas they are not taken into account in the

coarse model. This methodology relies on the assumption

that the possible errors on the atmospheric forcings, due

for instance to the lack of representation of the urban

boundary layer features over the city in the coarse model

(because of insufficient horizontal resolution and physical

parameterizations), are negligible in comparison with the

benefit of including high-resolution canopy heterogeneity

and the appropriate physical parameterizations (in TEB

and ISBA). However, this issue should be partially

resolved when two-way interactions will be included in

GEM-SURF.

d. Experimental design

The simulation analyzed in this study was obtained

using a specific experimental design of the urban GEM-

SURF system, even if several other alternate configura-

tions were tested. This integration, hereinafter referred

to as ‘‘uGS,’’ provides continuous forecasts during the five

warmest months in the Montreal region, from 0000 UTC

1 May 2008 to 0000 UTC 1 October 2008. The time

step is 30 min and the atmospheric forcings are specified

every hour. The initial conditions on 1 May 2008 are

prescribed using MSC’s surface analyses (Belair et al.

2003a). The St. Lawrence River surface temperatures are

prescribed every day with the analysis product. The ther-

mal roughness length on road and roof surfaces is pa-

rameterized following Kanda et al. (2007) (Leroyer et al.

2010). Anthropogenic fluxes were assigned at midheight of

the canyon for the two urban classes with a large portion of

roads and parking (with a constant value of 10 and 5 W

m22 for the sensible and latent heat fluxes). A few modi-

fications have been brought to the vegetation look-up

FIG. 2. LULC classification of the Montreal metropolitan region, and location of the main

landmarks. The experimental domain is projected on a latitude–longitude 120-m grid. The

dominant class in the grid cell is represented for (a) all covers and (b) urban covers only.


tables in ISBA as compared with the original version of

Belair et al. (2003a,b) (decrease of LAI; decrease of

albedo for grass and crop classes).

3. Datasets

To evaluate the urbanized land surface external model-

ing system, two different datasets were used. The first is

based on local continuous observations gathered during the

EPiCC experiment on three sites chosen to be represen-

tative of rural, urban, and suburban canopies. The second is

from satellite imagery that provides two dimensional ob-

servations of radiative surface temperature for a few scenes.

a. The EPiCC measurement network in Montreal

The EPiCC measurement network in Montreal was

composed of three tower sites, with different land use

characteristics. Their locations are shown in Fig. 3a. The

‘‘rural’’ site (RUR) represents the reference site for non-

urbanized area. It is located southwest of Montreal Island

in a farmland with very few buildings. The flux tower (Fig.

3c) is located about 800 m north of a highway and 1.5 km

away from the lake shore. Meteorological measurements

were taken at 2 m above ground level (AGL) until 14 July

2008 and after this date at 5 m AGL to account for the

vegetation growth (corn field). Surface temperature was

automatically measured by an infrared thermometer.

The two urbanized tower sites were equipped with a

25-m tower (Fig. 3b). The instrumentation at these sites is

described in Bergeron and Strachan (2011). The ‘‘urban’’

site (URB) was located in the Rosemont–Petite–Patrie

district, where the previous studies of Montreal Urban

Snow Experiment (MUSE) 2005 and 2006 in winter-

time conditions were conducted (Lemonsu et al. 2008,

2010; Leroyer et al. 2010) because of its representation

of a typical Montreal high-density residential district. For

this site, measurements were interrupted on 13 July be-

cause of an instrumentation problem. The ‘‘suburban’’

site (SUB) is located in the Pierrefonds–Roxboro area,

which is representative of a typical North American

suburban area (single family detached houses) with a

much larger fraction of vegetation.

b. MODIS satellite dataset

Two-dimensional scenes were acquired from the

thermal information from MODIS launched on the

satellite Terra. The MOD11A1 version-5 level-3 Land

Surface Temperature (LST) product (Wan and Li 1997;

Wan et al. 2002; Wan 2008) used in this study is processed

by the Land Processes Distributed Active Archive

FIG. 3. EPiCC field experiment in Montreal. (a) Location of the three tower sites and (b) aerial photographs of the

two urbanized sites. The instrumented towers are highlighted with the yellow lines. (c) Photograph of the tower

installed in a farmland at the rural site (photographs provided by the EPiCC research network in October 2007 and

February 2008).


Center (LPDAAC) (https://lpdaac.usgs.gov/). This prod-

uct includes values generated in a sinusoidally projected

tile by mapping the level-2 LST product (MOD11_L2)

retrieved with the generalized split-window algorithm on

0.938-km grids (Wan and Dozier 1997; Wan et al. 2002).

MOD11A1 is composed of daytime and nighttime LSTs,

quality assessment, observation times, view angles, clear-

sky coverage, and emissivity estimated in bands 31 and 32.

Data are corrected for atmospheric effects (conversion

from top of the atmosphere radiance to surface reflec-

tance, considering molecular absorptions) using atmo-

spheric temperature and water profiles (Vermote et al.

1997). The specific scenes corresponding to the simulation

domain presented in this study were extracted using the

MODIS Reprojection Tool (MRT).

The accuracy of the LST was estimated by Wan et al.

(2002) to be about 1 K in homogeneous rural regions.

Several studies have also shown the relevance of this

dataset for urban areas. By comparing MODIS LST

with data from higher-resolution sensors [the Thermal

Airborne Imager (TABI) and the Advanced Spaceborne

Thermal Emission and Reflection Radiometer (ASTER)],

Pu et al. (2006) pointed out that the MODIS product is

suitable for the synoptic overview of an urban area and

for studying the urban thermal environment. Rigo et al.

(2006) compared the longwave surface radiation flux

obtained with three different satellite sensors with in situ

observations during the Basel Urban Boundary Layer

Experiment (BUBBLE) experiment (Rotach et al.

2005). The MODIS data were found to have differences

[6(3–5)%] with in situ flux of the same order of magnitude

than 1.1 km-Advanced Very High Resolution Radiometer

(AVHRR) and 60-m Landsat Enhanced Thematic Map-

per Plus (ETM1) over the urban and suburban sites.

4. Results at the EPiCC sites

This section presents an objective evaluation of the

simulations from the high-resolution urban uGS and from

the 15-km operational regional deterministic model

(REG, also used as forcings for urban GEM-SURF)

based on observations obtained at the EPiCC tower

sites. Model evaluation is performed only at times

when quality-processed measurements data are avail-

able, which remove data taken during weather events

such as rainfall and high wind speed from the analysis

(Bergeron and Strachan 2011).

The model outputs evaluated in this study include the

different components of the SEB. In uGS, the net radia-

tion and turbulent heat fluxes (sensible and latent) result

from a linear combination of both urban (calculated by

TEB for the built-up fraction) and natural sources (cal-

culated by ISBA for the natural fraction). By applying

the closure equation (and the notation of Leroyer et al.

2010), the residual term Qres is calculated so that Qres 5

Q* – QH – QE, with Q* being the net radiation and QH and

QE being the sensible and latent heat fluxes. The residual

term represents all the components that are not specifically

computed (energy stored or released by the canopy, an-

thropogenic heat fluxes, water phase change, etc.). The net

radiation is obtained by Q* 5 SY 1 S[ 1 LY 1 L[, where

SY and LY refer to incoming solar and longwave radiation

fluxes and S[and L[ refer to solar and longwave radiative

fluxes emitted upward. The temperature and humidity in

and above the canopy are also evaluated against obser-

vations as they are critical elements for urban heat island

and human comfort consideration. They are calculated in

uGS at the same height as the measurements using di-

agnostics depending on surface-layer stability functions.

The LULC fractions considered by the models at the

three EPiCC sites are given in Table 1. In uGS, all frac-

tions found in the grid cell are reported, whereas in REG

only fractions of vegetation in the grid cell are reported

and normalized because the grid cell can also contain

water at that resolution (e.g., St. Lawrence River).

a. SEB at the EPiCC sites

The components of the SEB averaged from 1 May to

30 September 2008 at the RUR site are shown in Fig. 4

TABLE 1. LULC fraction in the 120-m GEM-SURF simulations (uGS) and in the 15-km REG at the three EPiCC sites.

RUR URB SUB

uGS 1 crops 0.37 impervious surface g built 0.16 impervious surface g built

0.27 building 0.10 building

0.22 short grass and forbs g natural 0.38 short grass and forbs g natural

0.14 mixed wood forests 0.36 mixed wood forests

REG 0.57 crops 0.64 short grass and forbs 0.48 long grass

0.30 long grass 0.15 evergreen needleleaf trees 0.30 short grass and forbs

0.04 evergreen needleleaf trees 0.11 long grass 0.13 evergreen needleleaf trees

0.04 mixed shrubs 0.04 crops 0.04 mixed shrubs

0.02 deciduous broadleaf trees 0.03 desert 0.03 crops

0.02 mixed wood forest 0.02 mixed shrubs 0.01 deciduous broadleaf trees

0.01 short grass and forbs 0.01 irrigated crops 0.01 irrigated crops


for EPiCC observations and for the REG and uGS runs.

Results obtained with REG and uGS are very close for

all the components. The small differences observed be-

tween these two systems occur for several reasons: (i)

the spatial mapping of ISBA’s vegetation type and pa-

rameters is not identical for the two systems (even if the

dominant type is crops in both systems; see Table 1), (ii)

there is no soil moisture assimilation in uGS whereas this

method is used in the REG simulation, and (iii) the at-

mospheric forcings are slightly modified by the down-

scaling method. When averaging the fluxes over a long

period, these differences are smoothed. The net radiation

Q* for both models is close to observations, with a slight

overprediction of the maximum. Turbulent sensible QH

and latent QE heat fluxes at the surface are overpredicted

on average during daytime by about 50 W m22. As

a consequence, the residual term Qres is underestimated

for that period.

Figure 5 provides summary statistics obtained for the

three tower sites as compared with measurements. The

bias (hatched bar charts) and the standard deviation er-

rors (STDE; full bar charts) calculated for uGS and REG

are in watts per meter squared. At the site RUR (Fig. 5a),

the STDE in uGS is almost the same as in REG, except

for QE, for which uGS performs better (the improvement

is 13% on STDE). This improvement is likely to be due to

the decrease in uGS of LAI for long grass and crop classes

in ISBA, especially in July and August (figures not

shown). The higher resolution may also have a positive

impact on the results.

It should also be noted that Q* biases are negligible

while STDEs exhibit large values (about 90 W m22).

A large portion of the Q* STDEs is related to errors in

radiative forcings, notably for SY (SW-in in Fig. 5), which

exhibit values of about 130 W m22 for STDE. These er-

rors are only significant during daytime (not shown). The

small Q* biases, on the other hand, result from compen-

sation of larger biases of individual components of the

SEB. For instance, the positive biases for SY are partly

compensated by the negative biases for LY (LW-in in

Fig. 5), again highlighting the importance of the mete-

orological forcings for this type of system.

The regional deterministic model (providing the at-

mospheric forcings) is known to underestimate the cloud

fraction. Although the operational model has recently

been updated with an improved radiative scheme, the

problem of overprediction of SY and underprediction of

LY in GEM is still an issue as highlighted by Markovic

et al. (2008) and Paquin-Ricard et al. (2010). It should also

be pointed out that there are some noticeable differences

FIG. 4. Mean diurnal cycle at RUR for the period from 1 May to 30 Sep 2008 of the SEB observed (blue line) and

simulated in uGS (red triangles) and in REG (black squares). Note that observations and uGS outputs are plotted at 30-min

intervals and REG outputs are plotted at 1-h intervals: (top) (left) Q* and (right) QH; (bottom) (left) QE and (right) Qres.


for SY and LY between uGS and REG (Fig. 5) that

result from the (linear) interpolation technique used

for these variables from coarse to high-resolution grids

under cloudy conditions (i.e., which exhibits large

spatial variability).

For the urban site (Fig. 6), results show that the net

radiation is overpredicted during daytime just after noon

by about 80 W m22 in uGS and 60 W m22 in REG. The

statistics of the corresponding variable Q* reported in

Fig. 5b exhibit STDE similar to RUR (Fig. 5a), but with

larger biases. It can be noted that this difference between

RUR and URB is explained mostly by the very small LY

biases at the URB site, while SY STDEs remain relatively

large and positive.

The maximum of daily QH at URB, averaged over

the 2.5 months, is overpredicted in both uGS and REG

(Fig. 6). Although this overprediction is larger for uGS,

the new external system outperforms REG in simu-

lating the diurnal cycle of the other components of the

SEB, namely QE and Qres. Clearly, uGS better repre-

sents the heat stored into the city materials and inside

the street canyon, which results in a positive time shift

(about 45 min) of the Qres maximum compared to the

net radiation maximum, as seen in the observations. In

uGS, QH also fits well with evening and nighttime obser-

vations, while QE is correctly simulated for the complete

daytime cycle. In contrast, the latent heat fluxes obtained

in REG follow a diurnal course that is more representative

of natural covers with larger evapotranspiration due to

presumed abundant vegetation (see Table 1). The residual

term in uGS compares generally well to observations, with

underestimation during daytime because of cumulative

errors from the other SEB terms. As seen in Fig. 5b, uGS

leads to substantially smaller STDEs when compared with

REG for QH, QE, and Qres. The improvement achieved is,

respectively, 14%, 41%, and 34%.

Similar charts averaged from 1 May to 30 September

2008 are shown in Fig. 7 for the suburban site, which is

located in a mixed environment with a lot of vegetation

but with regular arrangements of detached houses and

paved areas (Table 1). The mean diurnal cycle for net

radiation Q* is well predicted in uGS for this site, whereas

it is slightly underpredicted in REG during daytime. As

found in Fig. 5c, errors for the radiative forcings (SY and

LY) are very similar for uGS and REG. As a result, bias

differences for Q* are likely to be due to discrepancies for

the upward radiative components.

The sensible heat flux maximum (Fig. 7) is overpre-

dicted by about 70 W m22 in uGS and 140 W m22 in

REG. The latent heat flux is slightly underpredicted

in REG, whereas it is slightly overpredicted in uGS.

Consequently, the residual term is underestimated in

both simulations, even if the diurnal evolution in uGS

better fits observations with a good simulation of the

release of energy in the evening. As was the case for

URB, the summary statistics shown in Fig. 5c indicate

that predictions of QH, QE, and Qres are again sub-

stantially improved in uGS, by 29%, 40%, and 28%,

respectively, as compared with REG.

b. Clear-sky days

In this section, a set of clear-sky days was selected to

reduce as much as possible the influence of cloud cover

underprediction in the radiative forcings in the analysis of

the results. Nine days were selected based on the diurnal

cycle of SY observed at the three EPiCC sites: 6, 13, 25,

and 28 May, 12 June, and 2, 5, 6, and 7 July.

Figure 8 shows the comparison of the SEB components

averaged for the corresponding days at the urban site.

For these clear days, Q* reaches maximum values of

650 W m22 around noon and minimum values of about

2100 W m22 during nighttime for both the observations

and the model integrations (uGS and REG). The corre-

sponding incoming solar radiation is fairly well predicted,

as is the incoming longwave radiation, except for a small

underestimation before sunrise for LY (not shown). This

situation is also found for the two other EPiCC sites RUR

FIG. 5. STDEs and biases (W m22) for uGS and REG compared

with EPiCC measurements for (a) RUR, (b) URB, and (c) SUB

EPiCC tower sites.


and SUB (not shown). Therefore results presented in this

section benefit from a good prediction of the downwelling

radiative forcings. The heat QH is still slightly over-

predicted during daytime (Fig. 8) but to a lesser extent

than for the entire warm-season period (cf. Fig. 6). The

evapotranspiration QE is well predicted in uGS, which is

not the case for REG. Storage and release processes (as

seen on QH and Qres components) are again better rep-

resented in uGS than in REG.

Evaluation of near-surface air temperature and hu-

midity at the tower sites is presented in Fig. 9. At the

RUR site (Fig. 9a), temperature at 2-m height (above

canopy level) is fairly well predicted by both uGS and

REG during daytime, with a slight overestimation (al-

most 18) late in the afternoon. The air temperature is

overpredicted by about 28 at night in the two integrations.

It should be noted that the screen-level height is not ex-

actly the same for uGS and REG (2 versus 1.5 m), but this

does not seem to be the cause of major discrepancies

between the two systems (at least for air temperatures).

For specific humidity, results from uGS and REG are

again similar, but exhibit a slight bias (underprediction by

about 1 g kg21), and do not capture the rapid increase

and decrease for this variable likely caused by either

vegetation stomatal activity or change of wind direction

(and footprint-fetch area).

Near-surface air temperature and humidity at the

urban site are compared in Fig. 9b. Observations are

obtained at the top of the tower (at 25 m AGL), in the

main street (at 5 m AGL), and in the alley street (house

backyard, at 5 m AGL). Numerical outputs from uGS

are obtained at the same heights using surface-layer

diagnostics. Near the surface, results are averaged at

5 m AGL. Observations indicate that the mean mini-

mum temperature for the selected clear-sky days is al-

most the same (128C) at 0500 LT in both the street (5 m)

and at the top of the tower (25 m). The mean maximum

temperature, however, is about 18 larger in the street than

at the top of the tower in late afternoon (1600–1700),

consistent with QH analyzed previously (see Fig. 8), and

explained by the fact that near-neutral conditions occur

during the night (not the case during daytime). Results

also show that nighttime near-surface cooling is more

important in REG because urban processes are not rep-

resented. The diurnal cycle of relative humidity inside the

street is well represented in uGS, even though specific

humidity at the top of the tower is underpredicted by

about 1 g kg21.

At the suburban site (Fig. 9c), observations are ob-

tained at the top of the tower (at 25 m AGL), in the main

street (at 3 m AGL), and in the house backyard (at 2 m

AGL). Again, numerical outputs from uGS are obtained

FIG. 6. As in Fig. 4, but for URB and for the period from 1 May to 13 Jul 2008.


at the same heights using diagnostics. Near-surface re-

sults are averaged at 2.5 m AGL. The results indicate that

temperature in uGS fits well with observations. In par-

ticular, air temperature near the ground (2.5 m AGL)

both observed and simulated by uGS is lower than at the

top of the tower during nighttime (about 1.58–28C less at

the end of the night) because of vegetation radiative

cooling. The maximum screen-level air temperature at

1.5 m AGL is underpredicted in REG. The near-surface

relative humidity in the street and alley canyons is well

represented in uGS but the specific humidity at the top of

the tower is still underpredicted. Screen-level specific

humidity in REG is almost constant during the day.

Urban heat islands can be defined in several manners

(Roth et al. 1989; Arnfield 2003; Voogt and Oke 2003).

The atmospheric heat islands (at canopy level or for the

planetary boundary layer) represent warmer air in ur-

banized areas than in the surroundings. More specifically,

air temperature heterogeneity depends on local urban

areas features. For Montreal, Fig. 9d depicts the resulting

near-surface intraurban heat island between the URB

and SUB sites, observed and simulated in uGS and REG

during clear-sky days. Note that measurement height is

different for URB and SUB (respectively 5 and 2.5 m

AGL). Almost 28 of difference are observed between

URB and SUB during the night, whereas an inverse trend

is observed around noon. It is consistent with the expected

urban heat islands diurnal variations found in the literature

(Oke 1982). Heat is trapped in the dense and narrow streets

during the day and released during the night, leading to

warmer air temperature. The uGS system produces the

same trend, although it underestimates the nocturnal var-

iation of TURB 2 TSUB, mostly because of errors in air

temperature discussed above, but it clearly outperforms the

REG system in simulating intraurban local climate.

5. Spatial distribution of the surface urbanheat island

As opposed to the atmospheric heat islands, the

surface urban heat island (SUHI) is defined as the

thermal pattern as observed at the surface level, as, for

example, the radiative temperature that is observed

by a remote sensor or simulated by a numerical model.

In this section, we examine the ability of the urban

GEM-SURF system to forecast the spatial variability of

radiative surface temperature during clear-sky days. A

sample output from uGS is first compared with current

operational systems, that is, the regional 15-km model and

the Canada-East 2.5-km limited-area model (LAM). Up-

scale outputs from uGS are also compared with MODIS

satellite images available at coarser resolution.

FIG. 7. As in Fig. 4, but for SUB for the period from 1 May to 30 Sep 2008.


a. Improvement of forecasting in urban area

Figure 10 shows simulated radiative surface tem-

perature for several prediction systems with different

spatial resolutions on 6 July at 1100 LST. The high-

resolution (GEM-SURF) forecast (Fig. 10a) exhibits

substantial heterogeneity of the SUHI. Many hot spots

on the Montreal Island are apparent, such as the main

highways and the industrial or commercial areas nearby,

the airport, the downtown, and the eastern high-density

residential district areas (one can refer to Fig. 1 for the

landmarks on the domain). Cooler areas inside the city

are also seen, such as the Mont-Royal Park, a low-density

residential district just north of it, and the western part

of the island.

Outside the city, radiative surface temperature is al-

ways lower at that time of the day, with some differences

depending on the surface land use. Figure 10b presents

the result obtained with the same forecast, but upscaled to

the same spatial resolution as the MODIS images (reso-

lution of 938 m). To be able to compare the results with

satellite images, a spatial filter has been applied, so that

the result at each grid cell is weighted with the values at its

location and with the eight neighbors’ values. Even if

some details are lost with this process, the intensity and

the main distribution of the SUHI are preserved in the

reconstructed scene.

The prediction valid at the same time from the 15-km

regional model is shown in Fig. 10c. Clearly, the coarse

resolution cannot reproduce much details, and the SUHI

is completely absent from the forecast. The numerical

forecast produced with the 2.5-km LAM model is pre-

sented in Fig. 10d. More details are achieved with the

LAM system in comparison with the regional system, es-

pecially for the region surrounding the island of Montreal.

But the spatial distribution of the SUHI is quite different

from that of the uGS (high-resolution or upscaled result).

The urban areas in the LAM 2.5 km are represented using

a unique urban class, treated as a type of vegetation in

ISBA. Not surprisingly, the diurnal cycle of meteorologi-

cal variables in urban environment does not seem to be

well reproduced with MSC current operational systems.

b. Comparison with MODIS images

Reconstructed scenes from uGS are compared in

Fig. 11 with MODIS scenes for six clear-sky days (6, 13,

25, and 28 May, 2 and 6 July). Those days are selected as in

section 4b, except for the days with the satellite view angle

(SVA) larger than 458 (because some corrections were

applied in the MODIS product; Wan et al. 2002). The

simulated scenes (Fig. 11a) were obtained following the

method described in section 5a, with a linear interpolation

in time to fit with satellite image characteristics. The

FIG. 8. As in Fig. 6, but for nine clear-sky days (6, 13, 25, and 28 May; 12 Jun; 2, 5, 6, and 7 Jul 2008).


MODIS scenes (Fig. 11b) were obtained as explained in

section 3b. The differences between the two images (DT 5

LSTuGS 2 LSTMODIS) are shown in Fig. 11c. The corre-

sponding time, SVA, and statistics are presented in

Table 2, where pixels with more than 10% of water

were not considered (which means that the St. Lawrence

River had been removed from statistics). Results are

thus presented for all land covers, including urban and

natural surfaces.

The charts shown in Fig. 11 reveal that the warmest

LSTs generally correspond to built-up areas, both in

uGS and in MODIS. One noticeable exception is found

on 13 May for the MODIS large temperatures over

croplands, which are not very well represented in uGS

with the ISBA land surface scheme. This model weakness

may be related to incorrect land surface characteristics

because of the presence of more bare soils instead of

crops, as specified in the model. (Bare soil has a strong

FIG. 9. Mean diurnal cycle over nine clear-sky days (same days as Fig. 8) of (a) RUR near-surface air temperature and specific

humidity; (b) URB tower temperature and specific humidity, and near-surface temperature and relative humidity; (c) as in (b), but for

SUB; and (d) URB and SUB air temperature difference from observations (line), from uGS simulation at the same height as obser-

vations (triangles), and from REG at the 1.5-m screen-level height (crosses). For URB and SUB, near-surface variables are measured at

the two sides of the house (in the main street and in the alley). The measurement heights are 5 m for URB and 3 and 2 m for SUB

(presented at 2.5 m).


evaporation early in the morning and warms up rapidly

afterward.) In comparison with MODIS retrieved surface

temperatures, uGS produces on average lower LST in

May, and larger LST in July, as indicated on Table 2 with

uGS lower LSTs (by about 22.48C) on 28 May, and larger

LST (by about 1.4 K) on 2 July. However, there is no sys-

tematic error observed over the whole of urban and nat-

ural areas, and the magnitude of the errors appears quite

reasonable (as seen on Table 2, biases vary from 22.1 to

1 K over urbanized pixels and from 22.6 to 1.9 K over

natural pixels). It is also interesting to note that there is

no linear relationship between the differences between

DT and SVA or with the satellite track time.

As indicated by Fig. 11b, the warmest urban areas ob-

served with MODIS are located in the eastern dense

residential districts (Rosemont–Petite–Patrie and the

Plateau districts), the industrial zones near the airport,

and the Longueuil suburban area. In general, uGS over-

predicts LST on the western portion of the downtown

area for all days (although this is not clear for 25 May

because of the presence of clouds). It also seems that LST

over the airport region is slightly overpredicted.

Over vegetation, the maps for 13 May and 2 and 6 July

exhibit some correlation between DT and the cover type

(Fig. 11c), whereas DT remains relatively small for the

other cases. On 13 May, LST is underpredicted by 38 to

108 over crop fields. As mentioned above, MODIS sees

a surface that warms up more rapidly than uGS simulates.

The reasons of this discrepancy can be twofold: 1) The

vegetation may not be at the correct growing stage in

uGS, and 2) the soil water may not be properly specified.

On 2 and 6 July, the maps exhibit an overprediction of

LST in the rural lands located northwest of Montreal, and

in general over areas with mixed wood forest and long

grass (see Fig. 1). It must be pointed out that another

integration was conducted with initialization of soil water

every day with REG forecasts (not shown), and signifi-

cant improvements were observed over crops on 6 July,

but not on 2 July nor on 13 May. This suggests that a more

complete land surface assimilation system should be

coupled to the urban GEM-SURF system to improve

forecasts over natural surfaces.

6. Summary and conclusions

The Canadian urban GEM-SURF system is a compu-

tationally efficient tool that can provide high-resolution

(about 100 m in the horizontal dimension) forecasts of

surface and near-surface meteorological variables in var-

ious densities of urban environments. In this study, urban

GEM-SURF was integrated on the Montreal metropoli-

tan region from 1 May to 30 September 2008 with a

30-min time step. The present study provides the first

evaluation of this system in summer conditions.

Surface energy budgets as well as near-surface air tem-

perature and humidity were evaluated for both GEM-

SURF (run uGS) and MSC’s operational regional model

(run REG) against measurements from the EPiCC field

experiment at three tower sites. This long-term and site

by site evaluation shows that a noticeable improvement

has been obtained with the new GEM-SURF system

(compared with REG) especially for the two urban

residential sites (URB and SUB). The main reasons for

this improvement are related to the specification and

FIG. 10. Simulated radiative surface temperature at different resolutions at 1100 LT 6 July 2008 for (a) uGS at 120 m,

(b) uGS output upscaled at 938 m, (c) REG at 15 km, and (d) Canada-East LAM at 2.5 km.


representation of urban physical processes and to the

very high resolution achieved in uGS allowing more

detailed and accurate information to be fed into the

surface exchange models (surface characteristics and

atmospheric forcing).

The results also underlined some inability of the re-

gional model, which provides the atmospheric forcings

for GEM-SURF, to correctly simulate the incoming solar

radiation during daytime under cloudy conditions, and

the near-surface air temperature during nighttime. These

errors in atmospheric forcings have an impact on the

high-resolution prediction, and this may at least partially

explain some of GEM-SURF’s errors in both rural and

urban environments. Therefore, it is expected that the

performance of the urban GEM-SURF system can still

be improved with more accurate atmospheric forcings.

Horizontal SUHI patterns were examined through

the radiative surface temperatures for clear-sky days.

Details of the SUHI intraurban heterogeneity were sim-

ulated by uGS, whereas the two operational models (the

FIG. 11. Maps of land surface temperature at 938-m grid spacing, derived from (a) uGS simulations and (b) MODIS, together with (c)

the mean differences DT 5 LSTuGS 2 LSTMODIS. All scales are in degrees Celsius. Note that a different scale is used for the two bottom

maps. The main clouds are indicated by white areas for 25 and 28 May.


regional 15 km and the LAM East 2.5 km) failed to

produce realistic urban heat islands. A few MODIS LST

images were selected to assess the quality of GEM-SURF

city-scale spatial variability for the surface temperatures.

This evaluation against satellite imagery revealed signifi-

cant skill for GEM-SURF’s LST simulations for selected

clear-sky days. The differences between GEM-SURF

outputs and MODIS images highlighted some possible

seasonal effect of the vegetation that may not be cor-

rectly handled by the land surface modeling system.

Some specific urban zones were too warm in GEM-

SURF. A more detailed analysis may be necessary to

reveal the link between model errors and the specified

urban input parameters (e.g., morphological, radiative,

and thermal parameters). But overall differences were

found to be reasonably small, ranging from about 23 to

12 K depending on the day.

Finally, it is worth mentioning that following this

study, several aspects of GEM-SURF will be improved.

Although the uGS system improves the prediction of the

SEB at the URB and SUB sites (compared to REG),

these types of urban and suburban mixed environments

present some difficulties. In particular, modifications to

TEB are currently being made to better represent vege-

tation inside the street canyons. Instead of computing

independent surface energy budgets and near-surface

meteorological variables for built-up and natural sur-

faces (and applying a simple tile aggregation), the im-

pact of urban vegetation (i.e., grass, trees and bare soils

in the front and backyards or trees along roads) on the

radiative and thermodynamic budgets in TEB will be

taken into account (outputs from ISBA for urban vege-

tation will be used in TEB). This new method will

be more relevant for studying the impact of greening

strategies for urban heat island mitigation. The urban

classification used to obtain the surface characteristics,

based on satellite images, can also be affected by vege-

tation as seen from the atmosphere (some trees are quite

tall and cover the roofs during summer in the SUB area).

Other databases (vector databases) as well as other

methodologies (combination with space-based informa-

tion and with census data) are being examined for the

specification of urban characteristics. Other projects are

currently ongoing to improve the interaction and ex-

changes between the urban canopy and the atmosphere

by including additional vertical levels in the canopy, al-

lowing a more direct prediction of vertical profiles of

temperature, humidity, and winds in the urban canyons,

and better exchanges with the atmosphere.

Acknowledgments. This research was funded by the

Canadian Foundation for Climate and Atmospheric

Sciences (CFCAS). The authors thank all the participants

of the Environmental Prediction in Canadian Cities

(EPiCC) field experiment, especially the Montreal team

and Onil Bergeron for data support. The authors are

also grateful to Aude Lemonsu and Alexandre Leroux

for their main contribution to the elaboration of the

urban classification, through a Chemical, Biological, Ra-

diological and Nuclear (CBRN) Research and Technol-

ogy Initiative (CRTI) project. Acknowledgments are also

given to MSC’s developers of the Canadian external land

surface modeling system.

APPENDIX

Satellite-Based Urban Fabric Classifications forNorth American Cities

The urban covers and the associated input param-

eters for TEB are specified by an urban classification

(section 2b). A summary of the methodology developed

by Lemonsu et al. (2006b) to provide urban classifications

for North American cities is presented in this appendix.

A more detailed scientific documentation, together with

60-m maps for Oklahoma City and Montreal, and eval-

uation of the classification over Montreal with ground

truth (using Quickbird data) can also be found online at

http://eer.cmc.ec.gc.ca/index_e.php?page5s_activites/

s_crti/s_crti-02-0093rd/s_publications/publications_e.html.

TABLE 2. Comparison of uGS radiative surface temperature forecasts with MODIS products. Range of SVA over the domain, satellite

track time, bias [S(LSTuGS 2 LSTMODIS)], and STDE for all the pixels with less than 10% of water (land covers), for pixels with more than

10% of urban land cover (urban), and for pixels with less than 10% of urban land cover (natural).

Days 6 May 13 May 25 May 28 May 2 July 6 July

Range of SVA (8) 30–35 28–42 3–8 22–27 22–27 12–18

Time (LT) 1124 1130 1100 1048 1120 1054

Land covers Bias (K) 21.9 20.9 20.8 22.4 1.4 1.3

2146 pixels STDE (K) 2.4 2.8 2.7 3.2 2.2 2.2

Urban Bias (K) 21.1 0.2 21.6 22.1 1 0.5

958 pixels STDE (K) 1.8 1.9 3.2 2.8 1.8 1.8

Natural Bias (K) 22.6 21.7 20.2 22.6 1.8 1.9

1188 pixels STDE (K) 2.8 3.4 2.1 3.5 2.5 2.5


The overall methodology relies on two different da-

tabases, which were first used for unsupervised classi-

fications at 15-m resolution. Land use fractions from

satellite databases, as well as independent information

on building height and building fraction from elevation

databases, were then aggregated at 60-m resolution.

Starting with the first dataset, a satellite image from

either ASTER or Landsat (depending on the more recent

available image for a specific city), is used to produce

multispectral data at 15 m based on a Gram–Schmidt pan-

sharpening algorithm. An unsupervised classification is

performed at this resolution to produce maps of 11 land

use or elements, including six nonurban surfaces (‘‘ex-

cluded covers,’’ ‘‘water,’’ ‘‘trees,’’ ‘‘low vegetation,’’

‘‘grass,’’ and ‘‘soil and rocks’’) and five urbanized ele-

ments (‘‘roofs,’’ ‘‘concrete,’’ and ‘‘asphalt’’; two types of

mixing of vegetation and asphalt: ‘‘V&A 1’’ and ‘‘V&A

2’’). The fractions of all those 11 elements in a 60-m pixel

adds up to unity. The ‘‘built’’ fraction refers to the sum

of the fractions of urbanized elements.

The second dataset is the canopy elevation obtained

at 15 m by subtracting the bald Earth’s topography from

the total elevation. The total elevation is obtained from

DEM, whereas the bald Earth’s topography is obtained

from CDED1 for Montreal (other databases can be used

for other cities). This procedure thus provides additional

information on average building height (‘‘height’’) and

building fraction (‘‘built2’’) in the 60-m pixel.

The names given to the elements indicated above in

quotes represent the first-guess land use in the unsuper-

vised classification, and do not exactly reflect the final

fraction of the element that will be provided to the TEB

scheme. For example, the ‘‘asphalt’’ element fraction is

likely to overestimate the impervious fraction (i.e., the

road surface as named in the TEB scheme), as it could

include some dark roofs. A decision tree had to be ap-

plied, using information from the two datasets, to elimi-

nate these inaccuracies for the land cover fractions.

This decision tree is shown in Fig. A1, as it was applied

on the 60-m maps for the element fractions and for

FIG. A1. Partial reproduction of the decision-tree model of Lemonsu et al. (2006b). Selection criteria applied for

60-m urbanized pixels (with less than 20% of water and less than 90% of vegetation) are represented. White and gray

boxes refer respectively to the positive or negative answer to the previous selection criterion (the boxes show the

further selection criteria or the final class). Solid box lines refer to a selection criterion. Dashed box lines refer to

a final class. Selection criteria are applied on fractions of ‘‘asphalt,’’ ‘‘roof,’’ ‘‘V&A 1,’’ ‘‘V&A 2,’’ ‘‘built,’’ and

‘‘built2’’ and on the average building ‘‘height’’ (see details in the text).


height. In this appendix, pure vegetation classes are not

presented because they are not used in this study (see

section 2b). In total, 21 tests were conducted on the

fractions of the different elements and building height,

and the final product of the 60-m classification is com-

posed of 11 urban classes. A twelfth class was added for

the particular case of Oklahoma City to represent in-

dustrial areas (instead of the ‘‘very low vegetation’’ class

but with different roof material).

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