- Email: [email protected]
Winter surface energy budget in Denver, Colorado
Pergamon
Atmospheric Environment Vol. 9, No. 13, pp. 1579-1587, 1995 Elsevier Science Ltd Printed in Great Britain
1352-2310(94)00142-1
WINTER SURFACE ENERGY BUDGET IN DENVER, COLORADO DOMINIQUE RUFFIEUX Cooperative Institute for Research in Environmental Sciences, University of Colorado, and NOAA/ERL/Environmental Technology Laboratory, Boulder, CO 80303, U.S.A (First received 1 August 1992 and in final form 6 March 1994)
Abstract--The surface energy budget plays a major role in the evolution of the boundary layer in an urban environment. Because of the complex pattern of city surfaces, measurement and simulation of the surface energy budget are very difficult to perform. This paper presents a study of the winter surface energy budget conducted in downtown Denver, Colorado. By combining measurements with model simulations using a Geographical Information System, the temporal and ,spatial evolution of the surface energy budget inside downtown Denver is described. Results show the importance of heat emission from building walls, local shadowing by tall buildings, subsurface heat flux, and the effects of snow cover on the surface energy budget. Key word index: Urban surface energy budget, numerical simulation, Denver, case study.
1. INTRODUCTION The formation of an urban heat island is strongly related to the local surface energy budget (Terjung, 1970; Oke, 1974, 1982; Arnfield, 1982). Various analyses of the urban surface energy budgets can be found in the literature. Todhunter and Terjung (1990) discussed the large-scale relationships between the surface energy budget and different synoptic weather types, while several other studies have focused on the comparison between urban and rural sites (i.e. Kerschgens and Hacker, 1984; Adebayo, 1990; Kerschgens and Kraus, 1990). On a smaller scale, Nunez and Oke (1977) analyzed the surface energy budget inside an urban canyon, while Todhunter et al. (1992) modelled net radiation over a suburban snowpack. Individual aspects of the urban energy balance have also been studied by, for example, Kalanda et al. (1980), who estimated the surface energy balance using the Bowen ratio-energy balance approach, and by Yap and Oke (1974). who used the eddy correlation technique to estimate the sensible heat flux in an urban area. A major problem with surface energy budget studies in urban areas is how results from a single location pertain to the entire urban domain. The complexity of urban building materials, albedos, and geometries along with anthropo~;enic emissions of heat increases the difficulties of measuring and simulating real cases. This paper presents a slightly different approach to the problem. A typical evolution of the thermal and radiative components can be defined from measure-
ments taken in specified locations within the city. Then, a numerical model spatially extends this local evolution by using a Geographical Information System (GIS). The computer simulation produces a surface energy budget for each cell of a topographical grid by modifying actual measurements to fit the specific characteristics of each cell contained in the GIS. Denver's simulation results show strong variations in the surface energy budget in space and in time. In winter, the sensible heat flux varies, during the daytime, from 1 0 0 W m -2 for a shaded location to 300 W m - 2 for a sunny spot located in the center of downtown. During the night, the differences between inside and outside downtown are smaller, but can still reach values of 60 W m - 2, mainly because of anthropogenic heat and heat emitted by the buildings. The presence of snow affects the urban surface energy budget by reflecting a higher amount of short-wave radiation and by decreasing strongly the subsurface flux,
2. THE URBAN SURFACE ENERGY BUDGET
2.1. Definition The urban surface energy budget can be expressed as follows: Q*+QF+QG+QH+QE=O
(Wm -2)
(1)
where Q* is the net radiation, Qv is the anthropogenic heat flux, Qo is the subsurface heat flux, Q~ is the
1579
1580
D. RUFFIEUX
sensible heat flux, and QE is the latent heat flux. The net radiation can be expressed as follows:
Q*=KJ,+L~,-KT-LT I-Wm- 2],
(2)
where K is the short-wave radiation flux, L is the long-wave radiation flux, and the arrows point in the direction of the flux. In an urban area, the incoming longwave radiation is modified by the urban structures: at the street level the amount of heat emitted by the sky is reduced because of the limited view of the sky determined by the sky view factor. This lack of radiation is often compensated for by the increase in downward flux of heat emitted by the building walls and by anthropogenic sources. The magnitude of reflected shortwave radiation, K T, and subsurface heat flux, QG, are dependent on the urban surface make-up, albedo, and thermal conductivity of the soil. Due to the lack of information about anthropogenic flux density, QF, in the Denver metropolitan area, it was impossible to estimate this term individually, so it was included in the estimate of heat emitted by the building walls (see Section 2.3.3). In the following simulations, we estimated the energy available for cooling or warming, in absence of advection, as residual. 2.2. Geographical information system The primary purpose of a GIS is to manage spatial information to facilitate decision making. Thus, all GISs have capabilities for encoding, storing, processing and displaying mapped data (Tomlin, 1979). These
data are organized in a series of "layers", each layer representing a particular characteristic such as topography, slope, etc. The GIS is used as a source of information to display maps, to calculate new characteristics from a given set of data, and finally to display maps of these new characteristics. In this study, we used a GIS of downtown Denver to calculate the surface energy budget. From single location data, the surface energy components were estimated by modulating them with the GIS. The domain used in the model is characterized by a high density of buildings and a lack of open space such as parks. This domain represents the metropolitan region encompassing downtown Denver and the Continuous Air Monitoring Program (CAMP) site (Fig. 1). The input matrix comprises two superimposed grids, 2.0 by 1.7 km, which define the surface topography and building heights. The topographical grid is computed by interpolating between 250 digitized elevations for downtown Denver, which is built on a slope of less than 1° running northwest to southeast. The second grid consists of the heights of the 90 tallest buildings in Denver. The resultant matrix is a 200 column by 170 row spatial grid of downtown Denver, specifying the elevation (topography plus building height) at each of the 34,000 grid cells. Figure 1 shows a three-dimensional representation of the grid looking from the northeast across Denver plus an expanded view of the two sites selected for the temporal evolution of the surface energy
loo
lOmeters
Fig. 1. Three-dimensional view of downtown Denver topographical grid. The areas surrounding "Camp" and "City core" are defined in the two windows.
Winter surface energy budget in Denver budget (see Section 3.1). For each of the 34,000 grid cells of the GIS, a value of altitude, slope, orientation, surface albedo, surface emissivity, thermal conductivity, and sky view factor was allocated. For cells with more than one type, the predominant type was put in. The horizontal resolution is 10 m and is enough to differentiate street from buildings while the topographical grid vertical resolution is 1 m. This information was used during the simulation to produce an estimate of the energy budget for the surface of each cell. The use of a GIS gives an alternative to the definitions of a "plan-view urban surface and near-surface active layer" proposed by Oke (1988). A cell is defined by a two-dimensional surface (10 m x 10 m) defined in a three-dimensional coordinate system and characterized by its elevation, orientation, and slope. 2.3. Model calculations 2.3.1. Initialization. Within the initialization process, the Sun's position and the total energy at the top of the atmosphere are calculated. The actual elevation and azimuth angles of the Sun can be computed at any time for a specific location. The total energy at the top of the atmosphere is calculated as a function of the Sun's declination angle, the equation of time, and the ratio of the dis~Iance between the Earth and the Sun and their mea~L distance (Ruffieux et al., 1990). 2.3.2. Short-wave radiation calculation The direct radiation reaching the surface can be expressed as
F~=Es × t xcos(js)
(Wm -2)
(3)
where F J, is the incoming direct radiation, Es is the energy at the top of the atmosphere, Js is the angle between the "Sun vector" and the perpendicular to the slope, and t is the total atmospheric transmittance (Ruffieux et al., 1990). Each cell is tested in conjunction with the Sun's angle, starting with the cells closest to the Sun, to determine the possibility of shadowing on other cells. The diffuse anisotropic radiation is computed by using a simplified regression equation in which diffuse radiation is the double sum of partial luminances for every 5 ° of elevation and azimuth:
O.[=O.OO76154Lo
87.5 °
357.5 °
~,
~,
he= 2 . 5 ° a c ~ 2 . 5 °
L'costj¢)cos(h~) (Wm -2)
(4)
where D is the diffuse anisotropic radiation, Lo is the zenithal luminance of the sky, L' is the ratio between the relative luminance of a point of the sky and L0, h, and a, are the elevation and azimuth of the computed point, and j, is the angle of incidence of radiation in a point of sky on an inclined surface (Ruffieux et al., 1990). The incoming short-wave radiation K~, is the sum of the direct and diffuse radiation fluxes [equations (3) and (4)]. "['he loss of diffuse radiation due to smaller sky view factor is compensated by the multiple reflections insiide the street canyons.
1581
The outgoing short-wave radiation (KT) is calculated by multiplying the incoming short-wave radiation by the surface albedo. The surface albedo was measured in various locations inside downtown and we found that most values ranged from 0.18 to 0.22. For the simulations over Denver, we averaged the values to get a mean albedo of 0.2. 2.3.3. Lono-wave calculations The incoming longwave flux of radiation is the sum of the atmospheric flux and the emission by the walls. The atmospheric long-wave radiation La~ is computed using the simple empirical function (Paltridge and Platt, 1976)
L,.[---e,trT~F
(Win -2)
(5)
where T, is air temperature, a is the Stefan-Boltzmann constant, and tF is the sky view factor. The air emissivity, e,, is estimated with ~,=0.65
x Dp 0 " 0 0 8 ( W m
(6)
-2)
where Vpis the vapour pressure. Comparison of results using this formula and in situ measurements show good agreement (__.10 Wm-2). The emission of heat from building walls (L~ ~) + anthropogenic sources (QF) are estimated from in situ measurements. A pyrgeometer mounted on a car measured incoming long-wave radiation in various locations and at different times of the day. Table 1 shows the averaged increase of heat due to the building walls and anthropogenic sources. We estimated Lw ~ + QF by subtracting the values recorded in open terrain from values recorded downtown, and we derived an empirical equation to compute, at each grid cell, the emission of heat by the walls and by anthropogenic sources from the incoming long-wave heat flux modulated by the sky view factor, ~F, and the poskion of the walls relative to the Sun, a:
L,,J, + Q F = a ( 1 - t F ) L a J ,
(Wm -2)
(7)
For each grid cell, we computed the building view factor (1 - W ) which defines the portion of visible walls (Fig. 2). The coefficient a ranged from 0.5 (completely shaded walls) to 0.8 (walls completely in sun). The total incoming long-wave radiation, L~, is the sum of equations (5) and (7). The outgoing long-wave radiation, LT, is a function of the surface temperature, its emissivity plus the reflected part of the incoming long-wave radiation, L~ (Oke, 1987):
LT=(~aT*.)+(1--~)L+
(Wm -2)
(8)
Table 1. Increase of incoming long-wave radiation inside downtown Day
Wm -2 %
Night
In Sun
In shade
75 21
30 8
50 15
1582
D. RUFFIEUX
Ioo
1-¥
~
0.60 0.55 0.50 0.45 0.40 0.35 0.30 0.25 0.20 0.15 0.10
C
v,-
0.05 0.00
o.
C~
Fig. 2. Building view factor (l - qu) in downtown Denver.
where ss is the surface emissivity, and Ts is the surface temperature. For each grid cell, the model calculates the surface temperature depending on the surface temperature at CAMP, the surface emissivity and the thermal conductivity of the cell (Ruffieux, 1985), the presence or not of shadow on the cell, and if it is located inside the city core or not (Ruffieux et al., 1990). 2.3.4. Subsurface heat flux, latent heat flux, and sensible heatflux. Heat flux into the ground, Qo, is computed using soil temperature profiles measured at CAMP, T(z}. The subsurface flux is calculated as the change in soil heat storage, with each grid element assigned a heat capacity, (Ctz)), depending on its surface type: 3 0 cm
QG~Q(3Ocm)-~
f Ctz)~dz (Wm-2) (9) 0
where Q3o cm is the heat flux measured at 30 cm inside the soil with a soil heat flux plate. F o r simplification and due to a lack of measurements, the tops of the buildings were assigned the same physical characteristics as the concrete pavement. For the simulations presented in this paper, the latent heat flux, Q~, is neglected because of the absence of trees and lawns and the low gradient of moisture between the dry surfaces and the air. For the simulation of 15 January 1992, the melting of the snow and subsequent evaporation are neglected because of the low air temperatures measured on this day ( - 1 0 to -- 15°C). The sensible heat flux (including the unknown anthropogenic heat flux) is estimated as a residual. Because of the weak advection present on the simulation days (wind speed < 2 m s - t), the residual energy terra, Qn, represents the energy available for local heating or cooling.
Winter surface energy budget in Denver
MEASUREMENTS
INPUT
Soilheatflux
plate JTemperature -30 crn
SIMULATION
J T I m e ndee for J ~ s u n n y & ehEled T condRIone I
ITemperature __ -5 cm
~----------~
I
I
co"
Relativet~umdty
JSolar re~latlon
OUTPUT
l
I
__•lnterms dMdualI s
'
[
~
Temperature 2 m
1583
|
SensiblehastS I --~anthropogenlcheatI J QH&QF [ / (res~u=,) I
L__ I---~
Control
JAlbedo Fig. 3. Flow chart of interrelations between in situ measurements and model. For abbreviations and symbols, refer to the text.
The flow chart in Fig. 3 is a schematic showing the relationships between in situ measurements and model calculations.
e0o
soo 400
3O0 3. S U R F A C E
ENERGY
BUDGET
DOWNTOWN
SIMULATION
IN
2O0
DENVER
10o
Two examples are presented in this paper, 8 November 1991 and 15 January 1992. Both clays are characterized by the absence of clouds and low wind speeds. The first day was a typical late fall day with moderate incoming solar radiation, low atmospheric moisture, and strong surface heating. The second day had snow cover over the entire Denver region, low atmospheric moisture and low air temperatures. The fluxes are computed for each cell of the grid. From these, two grid points were chosen to illustrate the temporal evolution of the surface energy budget terms (calculated at 5 min intervals). C A M P is the site where the subsurface, surface, and air temperatures and moisture were recorded. This location is representative of the outside of downtown and is affected by afternoon shadowing effects. For the second site, we chose a cell in the center of downtown to illustrate the "inside urban core" evolution (Fig. 1).
o -100
-2oo -~0 0
3
6
9
12
15
18
21
24
18
21
24
Time MST
(a)
eoo
soo 4oo
i
KI~ U
"
i
i~
3oo
,~20o ~10o o ----------100
3.1. Temporal evolution of surface energy budget
-200
Figures 4a and b and 5a and b show a model simulation of the surface energy budget parameters calculated for the two sites on the two chosen days. The 8 November simulations (Figs 4a and b) show a strong negative subsurface flux, QG, during the day
-a0o
3
6
9
12 Time MST
15
(b) Fig. 4. Simulated surface energy budget components, 8 November 1991: (a) CAMP; (b) city core.
1584
D. RUFFIEUX
500
300
K~
4OO
200
300
~100
20O
:~100 0 -100
-300
0
3
6
9
(a)
I-
300-
.
-
12 nmeMS'r
15
18
21
24
.
LT
6
9
12
15
18
•
•
+
12
15
18
21
24
•
;
3
6
+
21
24
~:+oo 0 -
-200
(b)
3
~moMST
200
0
-300
0
(a)
3OO
100
.+00
+
-100
-2OO
-100 0
3
6
9
12
15
18
21
24
Time MST
0
(b)
9
"n~ Ms'r
Fig. 5. Simulated surface energy budget components, 15 January 1992: (a) CAMP; (b) city core.
Fig. 6. Simulated sensible heat flux time series: (a) 8 November 1991; (b) 15 January 1992.
at both sites. This flux is a result of the large soil thermal capacity and the large diurnal amplitude of surface temperature. The afternoon shadows from the buildings cool the surface and reverse this tendency. The shadowing effect is visible at both sites during the afternoon, but it is more important downtown. Inside downtown a switch in the long-wave radiation occurs. The downward long-wave flux, LL is larger at the canyon floor than the outgoing long-wave flux, LT, because of heat emission by the building walls exposed to the Sun and stronger surface cooling caused by shadowing of the cell. On 15 January (Figs 5a and b), a winter day with uniform snow cover, the presence of snow tends to reduce the diurnal amplitude of both the outgoing long-wave flux (decreased surface warming) and the subsurface flux. The higher albedo (set to 0.6 over the entire domain) of the snow increases drastically the amount of reflected short-wave radiation (KT) reflected back to the atmosphere. Finally, the shadowing effect seems to be more important at both locations, because of the different positions of the Sun in the sky. The sensible heat flux time series illustrates the degree of surface cooling (negative) or warming (positive) for the two sites (Figs 6a and b). On both days, the
city core has relatively high values of sensible heat flux. On 8 November (Fig. 6a), the nocturnal sensible heat flux was low at CAMP (0 to - 2 0 W m -2) and positive within the city core (40-60 Win-2). The shadowing effect, in which all direct solar radiation is blocked, seems to play a major role during the day. The winter case evolution (15 January) looks the same as that for 8 November, but with a shift of the curves downwards, because of the weaker effect of the subsurface flux (snow acts like a insulating layer) and a higher surface albedo (Fig. 6b). 3.2. Spatial evolution of the surface energy budget The contrast between the city core and the outside appears to be greatest during the early afternoon. At 1430 Mountain Standard Time (MST), the Sun elevation is about the same for the two days (20°) and the maximum incoming solar radiation is about the same (778Wm -2 for 8 November, and 811 W m -2 for 15 January). The 8 November (Fig. 7a) available energy values are positive (warming) everywhere except in the areas shadowed by buildings. The region with relatively high values encompasses the city core. For 15 January (Fig; 7b), the situation is approximately the same: the orientation Of the shadow is slightly more south and
Winter surface energy budget in Denver
1585
W m -2
110 100 9O 80 70 6O 50 40 30 20 10 0 -10 -20 -30
(a)
Fig. 7a.
the cooling is stronger. The contrast between inside and outside the city core is of the same amplitude as on 8 November (gain of 100 Wm-2), and the available energy field outside downtown averages 50 W m - z compare~L to 70 W m - 2 on 8 November. The integration of the available energy over 24 h over the area shows that the presence of snow on the ground decreases the degree of warming during the day (change in albedo) and acts like an insulating layer for the subsurface flux (change in thermal capacity). Therefore, the total available energy averaged over the entire domain was 2.1 MJ on 8 November and - 3 . 2 MJ on 15 January.
4. DISCUSSION
The surface energy budget components vary strongly in space and in time inside downtown Denver. The simulation of the surface energy budget,
combined with local measurements, gave us a good spatial and temporal overview of their evolution. Other studies showed that the differential cooling and warming plays an important role in the micrometeorology and in the air quality of the city in winter. Here are some of the effects of the winter surface energy budget on the urban boundary layer. • By including the emission of heat by buildings, the model was able to simulate a region of energy excess in Denver. This area of higher available energy is located inside downtown Denver, and is maximized during the night. • During the day, the surface energy budget is strongly affected by the shadowing effect due to the high downtown buildings which can change the sign of the available energy. In situ measurements showed that early shadowing enhanced the afternoon formation of a local stable layer near the ground (Ruitieux et al., 1990).
D. RUFFIEUX
1586
Kilometers
0 C
m. O W m -2
110 100 90 8O 7O 60 5O 40 30 20 10 0 -10 -20
v
m. ,r-
0
(b) e,i Fig. 7. Simulated sensible heat flux in downtown Denver, 1430 MST: (a) 8 November 1991; (b) 15 January 1992.
• In the absence of snow, the subsurface flux of energy seems to have an amplitude much higher than expected and plays a significant role in the evolution of the processes occurring at the ground-air interface. Further investigations will focus on the longer-term surface energy evolution (e.g. warm air advected over a previously cooled surface), and its implications for air quality. Finally, we will apply this type of simulation of the surface energy budget to different types of complex topographic regions.
Acknowledgements--This study was supported by the Swiss National Science Foundation and in part by Colorado Department of Health. This author would like to thank all those involved in this project, especially Mr C, King and Mr D. Wolfe who aided in data collection.
REFERENCES
Adebayo Y. R. (1990) Aspects of the variation in some characteristics of radiation budget within the urban canopy of Ibadan. Atmospheric Environment 24B(1), 9-17. Arnfield A. J. (1982) An approach to the estimation of the surface radiative properties and radiation budgets of cities. Phys. Geog. 3, 97-122. Kalanda B. D., Oke T. R. and Spittlehouse D. L. (1980) Suburban energy balance estimates for Vancouver B.C., using the Bowen ratio-energy balance approach. J. appl. Met. 19, 791-802. Kerschgens M. J. and Hacker J~ M. (1984) On the energy budget of the convective boundary layer over an urban and rural environment. Beitr. Phys. Atmos..~(2), 171-185. Kerschgens M. J. and Kraus H. (1990) On the energetics of the urban canopy layer. Atmospheric Environment 24B, 321-338. Nunez M. and Oke T. R. (1977) The energy balance of an urban canyon. J. appl. Met. 16, 11-19.
Winter surface energy budget in Denver Oke T. R. (1974) Review of urban climatology, 1968-1973. WMO Technical Note No. 134(383), 137 pp. Oke T. R. (1982) The energetic basis of the urban heat island. Q. J. R. Met. Soc. 108, 1-23. Oke T. R. (1987) Boundary Layer Climates, 2rid edition, 435 pp. Methuen, London. Oke T. R. (1988) The urban energy balance. Progress in phys. Geooraphy 471-508. Paltridge G. W. and Platt C. M. R. (1976) Radiative Processes in Meteorology and Climatology, 318 pp. Elsevier, Amsterdam. Ruflieux D. (1985) L'agglom6ration de Fribourg et son influence sur la ventilation. Ph.D. thesis, Editions Universitaires, Fribourg, CH. Ruflieux D., Wolfe D. E. and Russell C. (1990) The effect of building shadows on the vertical temperature structure of the lower atmosphere in downtown Denver. J. appl Met.
1587
29, 1221-1231. Terjung W. H. (1970) The energy balance climatology of a city-man system. Ann. Assoc. Amer. Geooraphers 60(b), 466-492. Todhunter P. E. and Terjang W. H. (1990) The response of urban canyon energy budgets to variable synoptic weather types--a simulation approach. Atmospheric Environment 24B, 35-42. Todhunter P. E., Xu F. and Buttle J. M. (1992) A model of net radiation over suburban snowpacks. Atmospheric Environment 26B, 17-22. Tomlin C. D. and Berry J. K. (1979) A mathematical structure for cartographic modeling in environmental analysis. Annu. Syrup. Proc. American Congress on Surveying and Mapping. Falls Church, VA, pp. 269-284. Yap D. and Oke T. R. (1974) Sensible heat fluxes over an urban area--Vancouver B.C.J. appl. Met. 13, 880-890.
Atmospheric Environment Vol. 9, No. 13, pp. 1579-1587, 1995 Elsevier Science Ltd Printed in Great Britain
1352-2310(94)00142-1
WINTER SURFACE ENERGY BUDGET IN DENVER, COLORADO DOMINIQUE RUFFIEUX Cooperative Institute for Research in Environmental Sciences, University of Colorado, and NOAA/ERL/Environmental Technology Laboratory, Boulder, CO 80303, U.S.A (First received 1 August 1992 and in final form 6 March 1994)
Abstract--The surface energy budget plays a major role in the evolution of the boundary layer in an urban environment. Because of the complex pattern of city surfaces, measurement and simulation of the surface energy budget are very difficult to perform. This paper presents a study of the winter surface energy budget conducted in downtown Denver, Colorado. By combining measurements with model simulations using a Geographical Information System, the temporal and ,spatial evolution of the surface energy budget inside downtown Denver is described. Results show the importance of heat emission from building walls, local shadowing by tall buildings, subsurface heat flux, and the effects of snow cover on the surface energy budget. Key word index: Urban surface energy budget, numerical simulation, Denver, case study.
1. INTRODUCTION The formation of an urban heat island is strongly related to the local surface energy budget (Terjung, 1970; Oke, 1974, 1982; Arnfield, 1982). Various analyses of the urban surface energy budgets can be found in the literature. Todhunter and Terjung (1990) discussed the large-scale relationships between the surface energy budget and different synoptic weather types, while several other studies have focused on the comparison between urban and rural sites (i.e. Kerschgens and Hacker, 1984; Adebayo, 1990; Kerschgens and Kraus, 1990). On a smaller scale, Nunez and Oke (1977) analyzed the surface energy budget inside an urban canyon, while Todhunter et al. (1992) modelled net radiation over a suburban snowpack. Individual aspects of the urban energy balance have also been studied by, for example, Kalanda et al. (1980), who estimated the surface energy balance using the Bowen ratio-energy balance approach, and by Yap and Oke (1974). who used the eddy correlation technique to estimate the sensible heat flux in an urban area. A major problem with surface energy budget studies in urban areas is how results from a single location pertain to the entire urban domain. The complexity of urban building materials, albedos, and geometries along with anthropo~;enic emissions of heat increases the difficulties of measuring and simulating real cases. This paper presents a slightly different approach to the problem. A typical evolution of the thermal and radiative components can be defined from measure-
ments taken in specified locations within the city. Then, a numerical model spatially extends this local evolution by using a Geographical Information System (GIS). The computer simulation produces a surface energy budget for each cell of a topographical grid by modifying actual measurements to fit the specific characteristics of each cell contained in the GIS. Denver's simulation results show strong variations in the surface energy budget in space and in time. In winter, the sensible heat flux varies, during the daytime, from 1 0 0 W m -2 for a shaded location to 300 W m - 2 for a sunny spot located in the center of downtown. During the night, the differences between inside and outside downtown are smaller, but can still reach values of 60 W m - 2, mainly because of anthropogenic heat and heat emitted by the buildings. The presence of snow affects the urban surface energy budget by reflecting a higher amount of short-wave radiation and by decreasing strongly the subsurface flux,
2. THE URBAN SURFACE ENERGY BUDGET
2.1. Definition The urban surface energy budget can be expressed as follows: Q*+QF+QG+QH+QE=O
(Wm -2)
(1)
where Q* is the net radiation, Qv is the anthropogenic heat flux, Qo is the subsurface heat flux, Q~ is the
1579
1580
D. RUFFIEUX
sensible heat flux, and QE is the latent heat flux. The net radiation can be expressed as follows:
Q*=KJ,+L~,-KT-LT I-Wm- 2],
(2)
where K is the short-wave radiation flux, L is the long-wave radiation flux, and the arrows point in the direction of the flux. In an urban area, the incoming longwave radiation is modified by the urban structures: at the street level the amount of heat emitted by the sky is reduced because of the limited view of the sky determined by the sky view factor. This lack of radiation is often compensated for by the increase in downward flux of heat emitted by the building walls and by anthropogenic sources. The magnitude of reflected shortwave radiation, K T, and subsurface heat flux, QG, are dependent on the urban surface make-up, albedo, and thermal conductivity of the soil. Due to the lack of information about anthropogenic flux density, QF, in the Denver metropolitan area, it was impossible to estimate this term individually, so it was included in the estimate of heat emitted by the building walls (see Section 2.3.3). In the following simulations, we estimated the energy available for cooling or warming, in absence of advection, as residual. 2.2. Geographical information system The primary purpose of a GIS is to manage spatial information to facilitate decision making. Thus, all GISs have capabilities for encoding, storing, processing and displaying mapped data (Tomlin, 1979). These
data are organized in a series of "layers", each layer representing a particular characteristic such as topography, slope, etc. The GIS is used as a source of information to display maps, to calculate new characteristics from a given set of data, and finally to display maps of these new characteristics. In this study, we used a GIS of downtown Denver to calculate the surface energy budget. From single location data, the surface energy components were estimated by modulating them with the GIS. The domain used in the model is characterized by a high density of buildings and a lack of open space such as parks. This domain represents the metropolitan region encompassing downtown Denver and the Continuous Air Monitoring Program (CAMP) site (Fig. 1). The input matrix comprises two superimposed grids, 2.0 by 1.7 km, which define the surface topography and building heights. The topographical grid is computed by interpolating between 250 digitized elevations for downtown Denver, which is built on a slope of less than 1° running northwest to southeast. The second grid consists of the heights of the 90 tallest buildings in Denver. The resultant matrix is a 200 column by 170 row spatial grid of downtown Denver, specifying the elevation (topography plus building height) at each of the 34,000 grid cells. Figure 1 shows a three-dimensional representation of the grid looking from the northeast across Denver plus an expanded view of the two sites selected for the temporal evolution of the surface energy
loo
lOmeters
Fig. 1. Three-dimensional view of downtown Denver topographical grid. The areas surrounding "Camp" and "City core" are defined in the two windows.
Winter surface energy budget in Denver budget (see Section 3.1). For each of the 34,000 grid cells of the GIS, a value of altitude, slope, orientation, surface albedo, surface emissivity, thermal conductivity, and sky view factor was allocated. For cells with more than one type, the predominant type was put in. The horizontal resolution is 10 m and is enough to differentiate street from buildings while the topographical grid vertical resolution is 1 m. This information was used during the simulation to produce an estimate of the energy budget for the surface of each cell. The use of a GIS gives an alternative to the definitions of a "plan-view urban surface and near-surface active layer" proposed by Oke (1988). A cell is defined by a two-dimensional surface (10 m x 10 m) defined in a three-dimensional coordinate system and characterized by its elevation, orientation, and slope. 2.3. Model calculations 2.3.1. Initialization. Within the initialization process, the Sun's position and the total energy at the top of the atmosphere are calculated. The actual elevation and azimuth angles of the Sun can be computed at any time for a specific location. The total energy at the top of the atmosphere is calculated as a function of the Sun's declination angle, the equation of time, and the ratio of the dis~Iance between the Earth and the Sun and their mea~L distance (Ruffieux et al., 1990). 2.3.2. Short-wave radiation calculation The direct radiation reaching the surface can be expressed as
F~=Es × t xcos(js)
(Wm -2)
(3)
where F J, is the incoming direct radiation, Es is the energy at the top of the atmosphere, Js is the angle between the "Sun vector" and the perpendicular to the slope, and t is the total atmospheric transmittance (Ruffieux et al., 1990). Each cell is tested in conjunction with the Sun's angle, starting with the cells closest to the Sun, to determine the possibility of shadowing on other cells. The diffuse anisotropic radiation is computed by using a simplified regression equation in which diffuse radiation is the double sum of partial luminances for every 5 ° of elevation and azimuth:
O.[=O.OO76154Lo
87.5 °
357.5 °
~,
~,
he= 2 . 5 ° a c ~ 2 . 5 °
L'costj¢)cos(h~) (Wm -2)
(4)
where D is the diffuse anisotropic radiation, Lo is the zenithal luminance of the sky, L' is the ratio between the relative luminance of a point of the sky and L0, h, and a, are the elevation and azimuth of the computed point, and j, is the angle of incidence of radiation in a point of sky on an inclined surface (Ruffieux et al., 1990). The incoming short-wave radiation K~, is the sum of the direct and diffuse radiation fluxes [equations (3) and (4)]. "['he loss of diffuse radiation due to smaller sky view factor is compensated by the multiple reflections insiide the street canyons.
1581
The outgoing short-wave radiation (KT) is calculated by multiplying the incoming short-wave radiation by the surface albedo. The surface albedo was measured in various locations inside downtown and we found that most values ranged from 0.18 to 0.22. For the simulations over Denver, we averaged the values to get a mean albedo of 0.2. 2.3.3. Lono-wave calculations The incoming longwave flux of radiation is the sum of the atmospheric flux and the emission by the walls. The atmospheric long-wave radiation La~ is computed using the simple empirical function (Paltridge and Platt, 1976)
L,.[---e,trT~F
(Win -2)
(5)
where T, is air temperature, a is the Stefan-Boltzmann constant, and tF is the sky view factor. The air emissivity, e,, is estimated with ~,=0.65
x Dp 0 " 0 0 8 ( W m
(6)
-2)
where Vpis the vapour pressure. Comparison of results using this formula and in situ measurements show good agreement (__.10 Wm-2). The emission of heat from building walls (L~ ~) + anthropogenic sources (QF) are estimated from in situ measurements. A pyrgeometer mounted on a car measured incoming long-wave radiation in various locations and at different times of the day. Table 1 shows the averaged increase of heat due to the building walls and anthropogenic sources. We estimated Lw ~ + QF by subtracting the values recorded in open terrain from values recorded downtown, and we derived an empirical equation to compute, at each grid cell, the emission of heat by the walls and by anthropogenic sources from the incoming long-wave heat flux modulated by the sky view factor, ~F, and the poskion of the walls relative to the Sun, a:
L,,J, + Q F = a ( 1 - t F ) L a J ,
(Wm -2)
(7)
For each grid cell, we computed the building view factor (1 - W ) which defines the portion of visible walls (Fig. 2). The coefficient a ranged from 0.5 (completely shaded walls) to 0.8 (walls completely in sun). The total incoming long-wave radiation, L~, is the sum of equations (5) and (7). The outgoing long-wave radiation, LT, is a function of the surface temperature, its emissivity plus the reflected part of the incoming long-wave radiation, L~ (Oke, 1987):
LT=(~aT*.)+(1--~)L+
(Wm -2)
(8)
Table 1. Increase of incoming long-wave radiation inside downtown Day
Wm -2 %
Night
In Sun
In shade
75 21
30 8
50 15
1582
D. RUFFIEUX
Ioo
1-¥
~
0.60 0.55 0.50 0.45 0.40 0.35 0.30 0.25 0.20 0.15 0.10
C
v,-
0.05 0.00
o.
C~
Fig. 2. Building view factor (l - qu) in downtown Denver.
where ss is the surface emissivity, and Ts is the surface temperature. For each grid cell, the model calculates the surface temperature depending on the surface temperature at CAMP, the surface emissivity and the thermal conductivity of the cell (Ruffieux, 1985), the presence or not of shadow on the cell, and if it is located inside the city core or not (Ruffieux et al., 1990). 2.3.4. Subsurface heat flux, latent heat flux, and sensible heatflux. Heat flux into the ground, Qo, is computed using soil temperature profiles measured at CAMP, T(z}. The subsurface flux is calculated as the change in soil heat storage, with each grid element assigned a heat capacity, (Ctz)), depending on its surface type: 3 0 cm
QG~Q(3Ocm)-~
f Ctz)~dz (Wm-2) (9) 0
where Q3o cm is the heat flux measured at 30 cm inside the soil with a soil heat flux plate. F o r simplification and due to a lack of measurements, the tops of the buildings were assigned the same physical characteristics as the concrete pavement. For the simulations presented in this paper, the latent heat flux, Q~, is neglected because of the absence of trees and lawns and the low gradient of moisture between the dry surfaces and the air. For the simulation of 15 January 1992, the melting of the snow and subsequent evaporation are neglected because of the low air temperatures measured on this day ( - 1 0 to -- 15°C). The sensible heat flux (including the unknown anthropogenic heat flux) is estimated as a residual. Because of the weak advection present on the simulation days (wind speed < 2 m s - t), the residual energy terra, Qn, represents the energy available for local heating or cooling.
Winter surface energy budget in Denver
MEASUREMENTS
INPUT
Soilheatflux
plate JTemperature -30 crn
SIMULATION
J T I m e ndee for J ~ s u n n y & ehEled T condRIone I
ITemperature __ -5 cm
~----------~
I
I
co"
Relativet~umdty
JSolar re~latlon
OUTPUT
l
I
__•lnterms dMdualI s
'
[
~
Temperature 2 m
1583
|
SensiblehastS I --~anthropogenlcheatI J QH&QF [ / (res~u=,) I
L__ I---~
Control
JAlbedo Fig. 3. Flow chart of interrelations between in situ measurements and model. For abbreviations and symbols, refer to the text.
The flow chart in Fig. 3 is a schematic showing the relationships between in situ measurements and model calculations.
e0o
soo 400
3O0 3. S U R F A C E
ENERGY
BUDGET
DOWNTOWN
SIMULATION
IN
2O0
DENVER
10o
Two examples are presented in this paper, 8 November 1991 and 15 January 1992. Both clays are characterized by the absence of clouds and low wind speeds. The first day was a typical late fall day with moderate incoming solar radiation, low atmospheric moisture, and strong surface heating. The second day had snow cover over the entire Denver region, low atmospheric moisture and low air temperatures. The fluxes are computed for each cell of the grid. From these, two grid points were chosen to illustrate the temporal evolution of the surface energy budget terms (calculated at 5 min intervals). C A M P is the site where the subsurface, surface, and air temperatures and moisture were recorded. This location is representative of the outside of downtown and is affected by afternoon shadowing effects. For the second site, we chose a cell in the center of downtown to illustrate the "inside urban core" evolution (Fig. 1).
o -100
-2oo -~0 0
3
6
9
12
15
18
21
24
18
21
24
Time MST
(a)
eoo
soo 4oo
i
KI~ U
"
i
i~
3oo
,~20o ~10o o ----------100
3.1. Temporal evolution of surface energy budget
-200
Figures 4a and b and 5a and b show a model simulation of the surface energy budget parameters calculated for the two sites on the two chosen days. The 8 November simulations (Figs 4a and b) show a strong negative subsurface flux, QG, during the day
-a0o
3
6
9
12 Time MST
15
(b) Fig. 4. Simulated surface energy budget components, 8 November 1991: (a) CAMP; (b) city core.
1584
D. RUFFIEUX
500
300
K~
4OO
200
300
~100
20O
:~100 0 -100
-300
0
3
6
9
(a)
I-
300-
.
-
12 nmeMS'r
15
18
21
24
.
LT
6
9
12
15
18
•
•
+
12
15
18
21
24
•
;
3
6
+
21
24
~:+oo 0 -
-200
(b)
3
~moMST
200
0
-300
0
(a)
3OO
100
.+00
+
-100
-2OO
-100 0
3
6
9
12
15
18
21
24
Time MST
0
(b)
9
"n~ Ms'r
Fig. 5. Simulated surface energy budget components, 15 January 1992: (a) CAMP; (b) city core.
Fig. 6. Simulated sensible heat flux time series: (a) 8 November 1991; (b) 15 January 1992.
at both sites. This flux is a result of the large soil thermal capacity and the large diurnal amplitude of surface temperature. The afternoon shadows from the buildings cool the surface and reverse this tendency. The shadowing effect is visible at both sites during the afternoon, but it is more important downtown. Inside downtown a switch in the long-wave radiation occurs. The downward long-wave flux, LL is larger at the canyon floor than the outgoing long-wave flux, LT, because of heat emission by the building walls exposed to the Sun and stronger surface cooling caused by shadowing of the cell. On 15 January (Figs 5a and b), a winter day with uniform snow cover, the presence of snow tends to reduce the diurnal amplitude of both the outgoing long-wave flux (decreased surface warming) and the subsurface flux. The higher albedo (set to 0.6 over the entire domain) of the snow increases drastically the amount of reflected short-wave radiation (KT) reflected back to the atmosphere. Finally, the shadowing effect seems to be more important at both locations, because of the different positions of the Sun in the sky. The sensible heat flux time series illustrates the degree of surface cooling (negative) or warming (positive) for the two sites (Figs 6a and b). On both days, the
city core has relatively high values of sensible heat flux. On 8 November (Fig. 6a), the nocturnal sensible heat flux was low at CAMP (0 to - 2 0 W m -2) and positive within the city core (40-60 Win-2). The shadowing effect, in which all direct solar radiation is blocked, seems to play a major role during the day. The winter case evolution (15 January) looks the same as that for 8 November, but with a shift of the curves downwards, because of the weaker effect of the subsurface flux (snow acts like a insulating layer) and a higher surface albedo (Fig. 6b). 3.2. Spatial evolution of the surface energy budget The contrast between the city core and the outside appears to be greatest during the early afternoon. At 1430 Mountain Standard Time (MST), the Sun elevation is about the same for the two days (20°) and the maximum incoming solar radiation is about the same (778Wm -2 for 8 November, and 811 W m -2 for 15 January). The 8 November (Fig. 7a) available energy values are positive (warming) everywhere except in the areas shadowed by buildings. The region with relatively high values encompasses the city core. For 15 January (Fig; 7b), the situation is approximately the same: the orientation Of the shadow is slightly more south and
Winter surface energy budget in Denver
1585
W m -2
110 100 9O 80 70 6O 50 40 30 20 10 0 -10 -20 -30
(a)
Fig. 7a.
the cooling is stronger. The contrast between inside and outside the city core is of the same amplitude as on 8 November (gain of 100 Wm-2), and the available energy field outside downtown averages 50 W m - z compare~L to 70 W m - 2 on 8 November. The integration of the available energy over 24 h over the area shows that the presence of snow on the ground decreases the degree of warming during the day (change in albedo) and acts like an insulating layer for the subsurface flux (change in thermal capacity). Therefore, the total available energy averaged over the entire domain was 2.1 MJ on 8 November and - 3 . 2 MJ on 15 January.
4. DISCUSSION
The surface energy budget components vary strongly in space and in time inside downtown Denver. The simulation of the surface energy budget,
combined with local measurements, gave us a good spatial and temporal overview of their evolution. Other studies showed that the differential cooling and warming plays an important role in the micrometeorology and in the air quality of the city in winter. Here are some of the effects of the winter surface energy budget on the urban boundary layer. • By including the emission of heat by buildings, the model was able to simulate a region of energy excess in Denver. This area of higher available energy is located inside downtown Denver, and is maximized during the night. • During the day, the surface energy budget is strongly affected by the shadowing effect due to the high downtown buildings which can change the sign of the available energy. In situ measurements showed that early shadowing enhanced the afternoon formation of a local stable layer near the ground (Ruitieux et al., 1990).
D. RUFFIEUX
1586
Kilometers
0 C
m. O W m -2
110 100 90 8O 7O 60 5O 40 30 20 10 0 -10 -20
v
m. ,r-
0
(b) e,i Fig. 7. Simulated sensible heat flux in downtown Denver, 1430 MST: (a) 8 November 1991; (b) 15 January 1992.
• In the absence of snow, the subsurface flux of energy seems to have an amplitude much higher than expected and plays a significant role in the evolution of the processes occurring at the ground-air interface. Further investigations will focus on the longer-term surface energy evolution (e.g. warm air advected over a previously cooled surface), and its implications for air quality. Finally, we will apply this type of simulation of the surface energy budget to different types of complex topographic regions.
Acknowledgements--This study was supported by the Swiss National Science Foundation and in part by Colorado Department of Health. This author would like to thank all those involved in this project, especially Mr C, King and Mr D. Wolfe who aided in data collection.
REFERENCES
Adebayo Y. R. (1990) Aspects of the variation in some characteristics of radiation budget within the urban canopy of Ibadan. Atmospheric Environment 24B(1), 9-17. Arnfield A. J. (1982) An approach to the estimation of the surface radiative properties and radiation budgets of cities. Phys. Geog. 3, 97-122. Kalanda B. D., Oke T. R. and Spittlehouse D. L. (1980) Suburban energy balance estimates for Vancouver B.C., using the Bowen ratio-energy balance approach. J. appl. Met. 19, 791-802. Kerschgens M. J. and Hacker J~ M. (1984) On the energy budget of the convective boundary layer over an urban and rural environment. Beitr. Phys. Atmos..~(2), 171-185. Kerschgens M. J. and Kraus H. (1990) On the energetics of the urban canopy layer. Atmospheric Environment 24B, 321-338. Nunez M. and Oke T. R. (1977) The energy balance of an urban canyon. J. appl. Met. 16, 11-19.
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1587
29, 1221-1231. Terjung W. H. (1970) The energy balance climatology of a city-man system. Ann. Assoc. Amer. Geooraphers 60(b), 466-492. Todhunter P. E. and Terjang W. H. (1990) The response of urban canyon energy budgets to variable synoptic weather types--a simulation approach. Atmospheric Environment 24B, 35-42. Todhunter P. E., Xu F. and Buttle J. M. (1992) A model of net radiation over suburban snowpacks. Atmospheric Environment 26B, 17-22. Tomlin C. D. and Berry J. K. (1979) A mathematical structure for cartographic modeling in environmental analysis. Annu. Syrup. Proc. American Congress on Surveying and Mapping. Falls Church, VA, pp. 269-284. Yap D. and Oke T. R. (1974) Sensible heat fluxes over an urban area--Vancouver B.C.J. appl. Met. 13, 880-890.