GE^) LOGICIAN PROFESSIONAL PAPER 10,9»~-E
Testing an Urban 

Climate Simulator

By GORDON M. GREENE

THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

GEOLOGICAL SURVEY PROFESSIONAL PAPER 1099-E

An example of environmental analysis using 

land use and land cover information

UNITED STATES GOVERNMENT PRINTING OFFICE, WASHINGTON : 1980
UNITED STATES DEPARTMENT OF THE INTERIOR 

CECIL D. ANDRUS, Secretary

GEOLOGICAL SURVEY 

H. William Menard, Director

Library of Congress Cataloging in Publication Data

Greene, Gordon M.

The influences of land use and land cover in climate analysis.

(Geological Survey professional paper; 1099-E)

Bibliography: p.

Supt. of Docs, no.: I 19.16:1099-E

1. Urban climatology Mathematical models. 2. Land use, Urban Environmental aspects Mathematical

models. I. Title. II. Series: United States. Geological Survey. Professional paper; 1099-E. 

QC981.7.U7G73 551.6'9'1732 79-607092

For sale by the Superintendent of Documents, U.S. Government Printing Office

Washington, D.C. 20402
CONTENTS

Page 

Abstract _____________________________ El

Introduction ___________________________ 1 

Review of urban climate modeling __________ 1 

Energy balance simulation in urban environments __ 2 

The model ___________________________ 6 

Application of the model___________________ 7 

Analysis of the model ____________________ 10 

Conclusion ____________________________ 15 

References cited ________________________ 16

ILLUSTRATIONS

FIGURE 1.

2.

3.

4.

5.

6.

7.

8.

Page

Line drawing showing silhouette ratio, which is found by dividing the vertical area by the ground area E3 

Line drawings showing the interception of direct shortwave radiation by buildings ____________ 4 

Line drawings showing the interception of flongwave radiation by buildings and vegetation (adopted

from Myrup and Morgan, 1972) ___________________________________________ 4 

Flow chart of the Outcalt and Carlson URBD model (1975) ____________________________ 7 

Map showing the path of the third flight across Baltimore, Md., along which multispectral scanner

data (including thermal) were obtained by the Environmental Research Institute of Michigan

(ERIM) aircraft on May 11, 1972 __________________________________________ 8 

Graph showing site characteristics for seven land use types ___________________________ 10 

Graphs showing simulated surface temperatures in Baltimore, Md., for various land use categories

based on hourly and averaged daily values on May 11, 1972 __________________________ 12 

Graph showing effect on increased air temperature on the predicted surface temperature ________.__ 16

TABLES

Page

TABLE 1. Recorded meteorological data ———————————.——————————————————————————————————— ^8

2. Surface weather observations at Baltimore-Washington International Airport, May 11, 1972 ——————— 8

3. Land use characteristics _____——————————.————————————————————————————;———————— 9

4. Simulation of solar radiation flux for medium density residential land use, May 11, 1972, Baltimore, Md — 11

5. Simulation of micro-meteorological conditions for medium density residential land use, May 11, 1972,

	Baltimore, Md ______—————————-——————————————————————————————-—————— H

6. Significance test for differences between URBD model using observed hourly data and mean daily data

-1 q

	as input ——___—__———————————-———————————————————————————————————— ±0

7. Control run values of the URBD model for use in sensitivity testing —————————————————————— 13

8. Sensitivity of the URBD model to changes in substrate diffusivity ( K ) ————————————————— 13

9. Sensitivity of the URBD model to changes in the height of obstructions (ho) ——————————————— 14

10. Sensitivity of the URBD model to changes in the surface relative humidity fraction (SRHF) —————— 14

11. Sensitivity of the URBD model to changes in silhouette ratio (SILRAT) ———————————————————— 14

12. Sensitivity of the URBD model to changes in albedo (ALB) ———————————————————————————— 14

13. Rank (R) of absolute value of Tsur, -Tc at 1300 for each land use type ————————————————————— 14

14. Sensitivity of the URBD model to decreased wind speed (n) ———————————————— 15

15. Sensitivity of the URBD model to increased wind speed (n) ——————————————————— 15

16. Comparison of observed and simulated surface temperature, 1400 EST, May 11, 1972 (in degrees

	Celsius) ______________—————-————————————————————————————————————— 15 

17. Simulation of global incoming solar radiation————————————————————————————————————— 15

III
IV CONTENTS

LIST OF ABBREVIATIONS AND SYMBOLS

ALB average shortwave albedo for all surfaces.

BALt_i the sum of Rn, S, H, and LE based on the second 

surface temperature estimate (t-1).

BALt_2 the sum of Rn, S, H, and LE based on the first 

surface temperature estimate (t-2).

BEAM direct shortwave radiation flux, in watts per 

square meter.

CBD Central Business District land use category.

CITY subroutine in the URBD model which computes the 

soil damping depth (ZG ).

COMM commercial area land use category.

Cp specific heat of air at constant pressure.

CPATH solar geometry input to the URBD model.

Ep saturation vapor pressure.

EPHEM subroutine in the URBD model which computes 

the solar declination.

EXT shortwave radiation flux on a horizontal surface 

outside the Earth's atmosphere.

F artificial heat flux due to combustion.

H kinetic or sensible heat flux into the atmosphere.

HDR high density residential land use category.

HEM diffuse shortwave radiation flux.

J Joule.

L latent heat of vaporization.

LDR low density residential land use category.

LE latent energy used in evapotranspiration.

MDR medium density residential land use category.

PARKS park land area land use category.

Qatr specific humidity of the air.

Qsurf specific humidity at the ground surface.

READIN geographical and meteorological input data to 

the URBD model.

RH relative humidity.

Ri bulk Richardson number.

Blw,T longwave radiation emitted by the Earth's surface.

RIW ^, longwave radiation emitted by the Earth's atmosphere.

Rn radiation balance at the terrestrial surface (net 

radiation).

Rsw \ solar shortwave radiation reflected by the Earth's 

surface.

R8W 1 solar shortwave radiation incident upon the Earth's 

surface.

RUNTRI subroutine in the URBD model used to determine the condition of the soil temperature 

profile.

S net heat flux to buildings, roads, and substrate.

SECANT algorithm used in the URBD model to compute 

a new surface temperature estimate, in order to 

stabilize the soil temperature profile.

SHDRAT shadow ratio.

SIG Stefan-Boltzman constant.

SILRAT silhouette ratio.

SKY area of a ground observer's sky hemisphere, in 

percent.

SOLURB subroutine in the URBD model which computes

the shortwave radiation input. 

SRHF surface relative humidity fraction. 

SUN backscattered radiation flux on a horizontal surface

at the Earth's surface. 

Talr air temperature, in degrees Celsius. 

Tc control run temperature of the URBD model for each

land use type. 

TERRAIN total shortwave radiation flux averaged over

the urban surface.

Tm mean diurnal air temperature, in degrees Celsius. 

Tmaa. maximum temperature, in degrees Celsius. 

Tmin minimum temperature, in degrees Celsius. 

Tnew model computed surface temperature, based on 

manually derived high- and low-surface temperature estimates.

TRANS transportation land use category. 

TSKY radiation temperature of the sky hemisphere. 

Tsurf surface temperature, in degrees Celsius. 

Tt_^ second surface temperature estimate, used in the 

URBD model to stabilize the soil temperature profile. 

Tt_ 2 first surface temperature estimate, used in the 

URBD model to stabilize the soil temperature profile. 

T2 temperature at the first node below the ground surface, in degrees Celsius. 

UBAL subroutine in the URBD model which computes

Rn, S, H, and LE. 

UPRIG subroutine in the URBD model which computes

the amount of radiation on a vertical surface. 

VERT total shortwave radiation on a vertical surface, in

watts per square meter. 

bALB slope, or 6 value, computed in the Student's *-test,

of the albedo parameter. 

baiLRAT slope or 6 value, computed in the Student's ttest, of the silhouette ratio parameter.

bgjifjp slope, or 6 value, computed in the Student's t-test,

of the surface relative humidity fraction parameter.

bit slope, or b value, computed in the Student's t-test,

of the surface roughness parameter. 

bt slope of a specified regression line. 

h0 obstacle height, in centimeters. 

k von Karman's constant (0.4). 

p atmospheric pressure, in millibars. 

r coefficient of correlation.

t the period of a specified temperature fluctuation. 

W watt.

z height above, or depth below, surface. 

zd atmospheric damping height, in centimeters. 

zo soil damping depth, in centimeters. 

z0 surface roughness length, in centimeters. 

A« the change in height above, or depth below, surface,

in centimeters.

ic thermal diffusivity, in square centimeters per second. 

\ thermal conductivity, in Joules per centimeter per

second per degree Celsius.

p wind speed at height z, in centimeters per second. 

p air density, in grams per cubic centimeter.
THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

TESTING AN URBAN CLIMATE SIMULATOR 1

By GORDON M. GREENE

ABSTRACT

Geographic data, generally in the form of terrain information, are a primary input to urban climate simulation models. 

Five urban terrain parameters: silhouette ratio, substrate diffusivity, obstruction height, surface relative humidity fraction, and albedo account for most of the geographic input. 

In a test of the sensitivity of model-predicted surface temperatures to simulated changes in the five urban terrain 

parameters, the influences of these parameters on the urban 

thermal regime of Baltimore, Md., were analyzed. Results 

indicated that changes on the order of 30 percent, in individual urban terrain parameters, have relatively little effect on 

surface temperatures. When more than one of these parameters are considered together, however, changes within the 

±30 percent range become more significant. The major benefit of the urban climate model in this analysis is not as a 

predictive tool, but as a method whereby relationships between Input components are examined.

INTRODUCTION

The use of the energy-balance relationship to explain the partitioning of shortwave and longwave 

radiation at the Earth's surface can be deceptively 

simple. Stated in an equation:

Net radiation=heat conducted into the 

ground-f-turbulent transfer of heat by (1) 

convection and evaporation.

Traditionally, the ideal setting for such a model of 

reality is an infinite, smooth plain with unvarying 

wind speed, air temperature, relative humidity, and 

solar radiation flux.

In contrast, consider this ideal against the reality 

of a few square blocks near the center of a large 

city. Streets are lined with multistory buildings, 

one side warmed by the early morning sun, the 

other side retaining the nighttime coolness. One 

block holds a small park with asphalt tennis courts, 

a large fountain used for wading, and a number of

l The research reported herein was funded in part by the National 

Aeronautics and Space Administration and U.S. Geological Survey as 

one phase of the Central Atlantic Regional Ecological Test Site 

(CARETS) Project.

"Department of Geography, University of Michigan, Ann Arbor, 

Mich.

large old trees. A small shopping area is separated 

from the park by a four-lane street, busy with commuters. The shops are mostly two stories, some with 

freshly tarred roofs. A study of the energy balance 

relationship of such an area requires an accounting 

of the tremendous variety in structures, their materials, shapes, sizes, and orientations. Myrup 

(1969) and Outcalt (1972a, b) were among the 

first to quantify some of these variables, frequently 

referred to as urban terrain parameters, and incorporate them into urban simulation models.

The purpose of this report is to examine the 

influences of five urban terrain parameters: silhouette ratio, substrate diff usivity, obstruction 

height, surface relative humidity fraction, and 

albedo, on the urban thermal regime. The analysis 

is based on a test of the sensitivity of modelpredicted surface temperatures to simulated changes 

in the five parameters. The simulation analysis was 

made in accordance with conditions existing in 

Baltimore, Md., on May 11, 1972. Land use and 

land cover data, from which values for the terrain 

parameters could be obtained, as well as thermal 

infrared images, against which the model could be 

verified, were available for that date in Baltimore.

REVIEW OF URBAN CLIMATE MODELING

Historically, the first major step in the study of 

urban climate relations was the recognition that 

large cities often have higher air temperatures in 

the city center than at the suburban fringes. This 

phenomenon was termed the "heat-island" effect 

and has been well described in the literature (Bornstein, 1968; Terjung, 1970).

The temperature differences between urban and 

rural areas result from numerous factors. Thermal 

characteristics of steel, concrete, and asphalt promote higher heat absorption and storage. A city 

center has a large proportion of vertical surfaces 

intercepting shortwave and longwave radiation. The 

atmospheric composition may be modified by more 

dust and aerosols over a city (Tuller, 1973). Rain 

in cities is usually drained away soon after falling, 

removing the possibility of net solar radiation being

El
E2 THE INFLUENCES OP LAND USE AND LAND COVER IN CLIMATE ANALYSIS

used for evaporation (Hage, 1975). Finally, human 

activities in cities produce heat from a large number 

of sources.

As urban climate investigations became more 

sophisticated, increasing emphasis was placed on 

explaining differences in microclimates within the 

city proper (Terjung and others, 1970). Those 

factors which distinguish an urban area from the 

countryside are not spatially homogeneous and may, 

therefore, be studied as a function of land use 

(Outcalt, 1972a). Alexander and others (1976), 

suggest that the concept of an urban heat island 

may exist only as a function of faulty research 

methodology. For example, the use of street-level 

spot observations taken from a car may smooth 

out microclimate diversity within a city, if the 

immediate influence of a street on surface temperatures is stronger than that of the surrounding environment. In effect, the overall "texture" of an 

urban surface is so varied that the concept of a 

representative site for different land uses is meaningless. The only realistic approach, therefore, is 

to use area-averaging parameters (Myrup and 

Morgan, 1972; Marotz and Coiner, 1973).

The increasing use of remotely sensed thermal 

data provides the synoptic view of urban climates 

that early investigators were attempting to create. 

The ability to analyze the microclimate at a specific 

point and height above the ground is sacrificed, 

however, since the remotely sensed thermal image 

is a two-dimensional projection of a three-dimensional surface. Even so, the temperature field shown 

for a remotely sensed area is a unique value caused 

by the complex interaction of solar radiation with 

the particular geometric and thermal properties 

of that area. As demonstrated by Outcalt (1972a), 

remotely sensed data can be used to categorize land 

use and land cover, and the interaction of land use 

and land cover with climatic variables.

One method for studying this interaction is 

through computer modeling, which simulates the 

events assumed to be taking place. Models applied 

to urban climates are generally classified as attempts to study either mesoscale or microscale interactions (Schneider and Dickinson, 1974). Mesoscale models usually operate in two dimensions and 

are used to investigate the nature of the urban 

boundary layer. Emphasis is placed on those factors which alter the turbulent transfer processes 

for heat, moisture, and momentum over an urban 

area, similar to a micrometeorologist studying the 

changes in turbulence caused by the transition from

a grassy field to an asphalt runway (see, for example, Gutman and Torrance, 1975; Leahey and 

Friend, 1971; McElroy, 1973; and Bornstein, 1975).

The approach used in this study relies on a microscale one-dimensional energy balance model pioneered by Myrup (1969). His first model has since 

been expanded for application to Ann Arbor, Mich., 

(Outcalt, 1972a) and Baltimore, Md. (Alexander 

and others, 1976). Myrup's original work also has 

been expanded and applied in a detailed study to 

Sacramento, Calif. (Myrup and Morgan, 1972).

The use of the model is helpful in studying urban 

climates because of the complexity of the city atmospheric interaction. Another benefit of this simulation model is that it allows one to link relationships which are only empirically justifiable to those 

processes which are well understood. The model 

provides the framework for arranging the information one has available as a system.

ENERGY BALANCE SIMULATION IN 

URBAN ENVIRONMENTS

The underlying principle of the surface energy 

balance in equation 1 is the conservation of energy. 

In a steady-state condition, the amount of energy 

gained must equal the amount of energy lost. 

Change is thought of as an instantaneous change to 

a new steady state. For urban applications, equation 

1 can be rewritten as:

Rn+F=LE+H+S, (2) 

where:

Rn = radiation balance at the terrestrial

surface (net radiation). 

F = artificial heat flux due to combustion 

LE = latent energy used in evapotranspiration 

H = kinetic or sensible heat fiux into

the atmosphere

S = net heat flux to buildings, roads, and 

substrate.

Following convention, the energy flux for any 

term is considered positive if directed toward the 

surface, negative if directed away. Although many 

authors have used F as a component of the energy 

relationship (for example, Gutman and Torrance, 

1975; Myrup, 1969), it was not included in this 

model since there are not enough data to give a 

meaningful artificial heat flux value to the different 

land uses. It is not clear if this is a significant omis-
TESTING AN URBAN CLIMATE SIMULATOR E3

sion. Yu and Wagner (1975) estimated F as 140 

W/m-2 for New York City on a clear winter day 

with an air temperature of 0° C. A more reasonable 

estimate for May would be the estimate of heat flux 

due to vehicular activity of 7 W/m-2 , although 

air conditioning should also be considered. Both 

these heat fluxes are much smaller than the mean 

midday flux of longwave and shortwave radiation 

of 870 W/m-2 .

By specifying the boundary conditions, one can 

compute the energy flux contributed by each of 

the four energy balance components from the following assumptions and procedures. While each 

component can be expressed as a function of surface temperature, it is not possible to analytically 

solve equation 2 for the unique equilibrium surface 

temperature which balances the equation. One can, 

however, use either an analog model or a numerical 

iteration scheme to find the surface temperature.

The appendix to the Baltimore study (Alexander 

and others, 1976) contains a good summary of the 

derivations for each of the energy flux components. 

Rather than repeating those equations, this section 

presents the components in their finite-difference 

form for use in a digital computer model and explains the modifications used to adapt the energy 

balance relation to a city. In addition, this section 

examines the response of the energy flux components in the urban terrain parameters.

The first component, net radiation (#„), can be 

broken down into its shortwave and longwave 

forms:

(3) Rn+(R8w l-R8w '

where R8W l is the solar shortwave radiation incident upon the Earth's

surface, Rtw t is the solar shortwave radiation reflected by the Earth's

surface, Rtw l is the longwave radiation 

emitted by the Earth's atmosphere,

and /?iwt is the longwave radiation emitted 

by the Earth's surface.

Looking at the shortwave equation:

R«.l -R,W 1 =ld-ALB)x(l-SHDRAT) 

x(BEAM + HEM)] + (SHDRATx 

HEM) + (SILRATxVERT) (4)

where:

AL£=average shortwave albedo for all 

surfaces

SHDRAT=shadow ratio (discussed below) 

BEAM=direct shortwave radiation flux 

HEM=dif£use shortwave radiation flux 

S/L#A!T=silhouette ratio (discussed below)

VERT=total shortwave radiation on a vertical surface.

The definition of the urban terrain parameter 

SILRAT is illustrated in figure 1. The role SILRAT

EXPLANATION 

Ground area

Vertical area

FIGURE 1.—SILRAT, where SILRAT =

vertical area 

ground area .

plays as a roughness factor is shown in figure 2. The 

average height of obstructions for a particular area, 

or for a particular land use, is represented by h0. 

As building density increases (fig. 2A to B), the 

total amount of incident shortwave radiation per 

unit ground area also increases. Figure 2C is included to show that past a certain critical value 

of SILRAT, the amount of incident shortwave 

radiation will decrease as the density increases. The 

increase in shadowed area with larger zenith angles 

is roughly approximated by:

S#D.RA!r=[l-cos(zenith angle)] (5)
E4 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

Evaporative heat loss is similar:

FIGURE 2. — The interception of direct shortwave radiation 

by buildings.

The net longwave radiation is computed as:

Riwl -Riwl =[(l-SILRAT) xSIGxTSKY 4 ]

+ [(SlLRAT-l)xSIGxTsurf*], (6)

where SIG is the Stefan-Boltzman constant and 

TSKY is the apparent radiant temperature of the 

sky hemisphere. Emissivity is assumed to be 1. In 

this case, SILRAT is used as a value roughly 

equivalent to a variable called the view factor, denned as that portion of radiation leaving a surface 

that is intercepted by another surface. A representative sky hemisphere, as "seen" from a point 

radiation source, is illustrated in figure 3A. This 

radiation source is located at point a in figure SB. 

Note, however, that the value of the view factor 

could change somewhat depending on the position 

of a in figure 3#, while SILRAT would remain constant. The approximation is used only in the absence 

of actual values for the view factor.

The two terms that contribute to turbulent heat 

transfer are each computed using analogous relationships. Sensible heat flux to the air can be calculated as:

LE = *2pML 2 X |(1-18 X Ri) 1 " | X (Qai-Qsur/)^

where:

k — von Karman's constant (0.4) 

P — atmospheric pressure 

n = wind speed at height z 

Cp — specific heat of air at constant

pressure

zd = atmospheric damping height (discussed below) 

z0 = surface roughness length (discussed

below)

Ri = bulk Richardson number 

Tair = air temperature 

TSurf = surface temperature 

Qair = specific humidity of the air 

Qsurf = specific humidity at the ground

surface 

L = latent heat of vaporization.

In general, wind speeds are slower near the 

ground than they are away from the surface due 

to the drag exerted on moving air by the ground 

surface. When temperatures are nearly constant 

with height, wind speed increases linearly with the 

natural logarithm of the height (Sellers, 1965). If 

one assumes some layer of air close to the ground, 

across which heat is transferred solely by molecular 

processes, it is possible to imagine a non-zero height, 

z0 , at which the wind speed is zero. This height is 

called the roughness length.

Presumably, as the surface texture gets rougher, 

the lower surface of the boundary layer rises due 

to turbulence, and the height at which the wind 

speed is zero also rises. The height above which the 

effect of the surface roughness is no longer felt, 

the atmospheric damping height, is represented by

The logarithmic relationship between zd, z0 , and 

^ has been derived from observations over uniform 

flat surfaces to a height of about 20 m (Sellers, 

1965). Under these conditions, z0 can be derived as 

the ^/-intercept of the log-law relation. Since the 

assumption of a uniform surface is clearly violated 

in urban areas, Lettau suggested an approximation 

of z0 as a function of the average height of obstructions and the silhouette ratio (Lettau, 1969):

H= l/4

2 X |(1-18 X Ri) / I ><

z0=Q.5xh0 x SILRAT (9)

He also notes that the parameter SILRAT loses
TESTING AN URBAN CLIMATE SIMULATOR E5

validity as it approaches unity. Like SILRAT, the 

use of z0 can be abused in extreme conditions. Looking again at figure 2, it is clear that, as the buildings crowd closer together, there will be a point 

when the roof tops function as the new ground surface and z0 goes back toward 0.

The second factor in equations 7 and 8 corrects 

for the unstable air conditions occurring throughout most of a diurnal cycle. The bulk Richardson 

number, Ri, in this adiabatic correction function 

is computed by Sellers (1965) as:

i= (980/ Tair) X [(Tair (10)

The urban terrain parameter SRHF, the surface 

relative humidity fraction, is used to compute the

FIGURE 3.—The interception of longwave radiation by buildings and vegetation (adopted from Myrup and Morgan, 

1972). A, Sky hemisphere view from point a; view 

factor-(l-SKY)/total area. B, Site view.

specific humidity at the surface: 

n _ .622pXEP XSRHF

V£s«r/— ————————————————————— ,

p—Ep

(ID

where Ep is the saturation vapor pressure. Myrup 

(1969) used the surface relative humidity fraction, 

SRHF, instead of surface relative humidity because 

of the heterogeneous nature of a given urban area. 

Since the surface relative humidity is considered to 

be 100 percent when the soil contains enough water, 

Myrup calculated SRHF by finding the relative 

proportion of an area covered by freely transpiring 

vegetation.

To calculate the soil heat flux, the model solves

(12)

where A is the thermal conductivity and Az is the 

change in depth. In this model, eight nodes are 

used to compute the soil temperature profile. The 

surface node is T8Mr/, and T2 is the ground temperature at the first node below the surface, set at 5 cm 

in this study. The program uses an implicit solution to a thermal diffusion equation to update the 

soil temperature profile based on a method suggested by Outcalt and Carlson (1975).

Thermal conductivity is not an input parameter 

due to the diverse surfaces and materials in any 

one land use. Instead, thermal diff usivity, *, is calculated as a function of SRHF by scaling * between 

the diffusivity of dry concrete (0.02 cm2 sec-1 ) 

and wet soil (0.005 cm2 sec-1 ).

K=(SRHF x 0. . (13)

The assumption is that as the SRHF becomes 

smaller, the percentage of surface covered by concrete becomes larger, and the value of * approaches 

the K of concrete (Outcalt, 1972a). Thermal conductivity is found as the product of K and an average heat capacity of 0.21 J cm-3 (Sellers, 1965).

Equations 2 through 13 may now be used to estimate the effects of changes in the urban terrain 

parameters, silhouette ratio, height of obstructions, 

surface relative humidity fraction, shortwave albedo, and thermal diffusivity on the surface temperature.

The variable SILRAT is used in the computation 

of Rn, H, and LE. As SILRAT increases, Rtw l - 

Riwl becomes less negative, making Rn larger. In 

equation 4, an increase in SILRAT also increases 

the amount of energy intercepted on vertical sur-
E6 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

faces, further increasing Rn. If all other variables 

are held constant, Tsurf will increase.

SILRAT also affects H and LE on the opposite 

side of the equal sign in equation 2 because it is 

used to compute z0 . An increase in z0 increases the 

turbulent exchange coefficient, the first factor in 

equation 7. The value of the second factor, the adiabatic correction for unstable air, also will increase 

but at a much slower rate.

The overall effect of an increased z0 will be to 

magnify the direction of flux as determined by 

Tair-Tsurf. For example, if Tsurf is larger than Totr, 

an increased z0 will cause H to become more negative. With all else held constant, a stronger H 

away from the surface will cause T8urf to drop.

Similarly, an increased z0 increases the magnitude 

of LE, with the sign depending on the relation 

Qair-Qsurf- It would be possible to have the signs 

of Qair-Qsurf and Tair-TgUrf such that an increased z0 

would magnify LE away from the surface but increase H toward the surface.

Equation 9 shows that h0 , the height of obstructions, is linearly related to z0 . One would expect an 

increase in h0 to have the same effect on H and LE 

as a comparable increase in SILRAT if Rn was held 

constant. Note that an increased h0 would not create 

an increased vertical surface area since SILRAT 

is input separately.

The effects of changes in ALB and « are the most 

transparent of all the parameters. An increase in 

ALB causes R8W l -R8W t to decrease and the value 

of Tsurf must, therefore, decrease to compensate. 

Equation 12 demonstrates that an increased \ would 

increase the magnitude of the soil heat flux. For 

example, at midday Tsurf is much higher than T2. 

An increased A. would increase the heat flux into 

the ground and cause Tsurf to drop if all else were 

constant. The thermal profile would also adjust 

more quickly to changes at the surface since the 

speed of diffusion has increased.

The parameter SRHF affects both LE and S. 

As equation 13 shows, an increase in SRHF decreases the value of « which lowers the magnitude 

of S. The value of Q8urf in equation 11 is also determined by SRHF being affected by it linearly. The 

relative magnitude of Qsurf then determines whether 

the flux of evaporation increases or decreases. For 

example, an increase in SRHF would force LE to 

become more negative if Qaurf was larger than Q0ir> 

forcing Tsurf to decrease.

Of the five urban terrain parameters, only 

changes in ALB cause predictable changes in surface temperature. In all other cases, the direction

of the component flux is a function of the sign of 

the temperature or specific humidity difference. In 

the case of SILRAT, h0, and SRHF, more than one 

component is adjusting to the change.

THE MODEL

The model used for this study is based on a 

surface-energy budget simulator developed by Outcalt and Carlson (1975). Their report describes in 

detail the structure of the program. This section 

generally outlines the model used, (URBD), and 

describes changes made to accommodate the urban 

influences on the energy-balance components as discussed in the last section.

If a city is considered as a mosaic of textures 

and materials, an urban model must differentially 

respond to the same meteorological conditions based 

on certain site characteristics. Outcalt's original 

study of land use effects (1972a) analyzes the 

responses of four land use types, each defined by 

the specification of three urban terrain parameters, 

surface relative-humidity fraction, obstruction 

height, and silhouette ratio.

For this study, albedo was added in order to accentuate the differences between heavily and lightly 

vegetated areas. In addition, roughness length was 

input instead of obstruction height since the data 

used to operate the model were provided in this 

form. This substitution is merely for convenience 

and does not affect the simulated temperature values since z0 and h0 are linearly related as shown in 

equation 9.

Other .input variables include latitude of the site 

and the time of year, which are used to calculate 

the solar geometry. The density of dust particles in 

the air, and amount of precipitable water to calculate the amount of shortwave radiation transmitted 

through the atmosphere, are also specified as input 

variables. Air temperature, atmospheric pressure, 

relative humidity, and wind speed are read into the 

model as the meteorological boundary conditions. As 

the model is currently written, these last four variables are specified for each time step the model 

is run, every hour in this case. The model in this 

form is referred to as the observed hourly input 

model. With slight modification in the input procedure, however, the model can also run on mean 

daily values for those variables using a wide range 

of time intervals. This form of the model is called 

the daily mean value input model.

The overall flow of the program is illustrated in 

figure 4. Notice that the model cycles through the
TESTING AN URBAN CLIMATE SIMULATOR E7

daily (24 hourly) data twice in order to stabilize 

the soil temperature profile before solving for the 

equilibrium surface temperature. Fed by a high 

guess and a low guess for the surface temperature, 

the SECANT algorithm computes a new guess with 

the following relationship:

T _r

1 new — -I i- 1 X BALt-{\

BALt \-BALt-2

(14)

where Tt^ refers to the previous guess of the temperature and Tt-2 refers to the guess prior to Tt-i. 

BAL is the current sum of fln , S, H, and LE. The 

routine converges to #AL=0±0.7W/m-2 usually 

within six iterations.

The subroutines used in the Outcalt and Carlson 

URBD model (1975) were not designed for city 

use, which necessitated some restructuring and 

modification. For example, the shadow generator, 

SHDRAT, has been included in subroutine CPATH, 

the solar geometrician. Subroutine SOLURB, which 

calculates shortwave radiation, does not allow the 

albedo to vary as a function of the angle of incidence. Such a refinement would not be justified since 

the albedo for any land use is an average for such 

diverse materials as roofs, vegetation, and concrete. 

Subroutine RUNTRI has also been simplified by 

disallowing variable porosity and thermal conductivity in the substrate. Instead, thermal diffusivity 

is assumed to take on values that can only range 

between the diff usivity of dry concrete (roughly 

0.02 cm2 sec-1 ) and that of wet soil (roughly 

0.005 cm2 sec-1 ).

It is necessary in this model to establish a lower 

boundary for the soil heat flux in order to simulate 

the diffusion of heat through the substrate at each 

time step. The damping depth, z0, is calculated in 

subroutine CITY using the Terzaghi relationship 

(Outcalt and Carlson, 1975):

vl/2 (15)

where t represents the period of the temperature 

fluctuation one is concerned with. In this case, one 

is dealing with diurnal variation so t = 4.32 X 10* 

sec (12 hours). Using the extremes of K as endpoints, the range of «0 is between 51 cm and 102 

cm.

APPLICATION OF THE MODEL

To test the response of the model to differing 

urban terrain characteristics, data were obtained

from a project conducted by the U.S. Geological 

Survey (USGS) (Alexander and others, 1976). 

One of the principal goals of the USGS investigation was to evaluate how well thermal scanner 

data, collected by spacecraft, could be used for 

studying urban climates.

The data for this model analysis come from an 

early experiment in the USGS study. On May 11, 

1972, the Environmental Research Institute of Michigan (ERIM) directed three aircraft flights over 

Baltimore, Md., at an altitude of approximately 

1,500 m. The third ERIM run, flown between the 

hours of 1345 and 1415 e.d.t., provides the observed 

data used for the verification of the URBD model. 

The flight path is shown in f igure 5, running across 

Baltimore from the northwest to the southeast.

CALL READIN

geographic and meteorologic 

data urban terrain parameters

CALLC/7Y

find substrate diffusivity 

soil damping depth

CALL EPHEM 

find solar declination

Initialize soil 

profile temperatures

CALL CPATH 

find solar geometry 

for each time step

CALL SOLWB 

find shortwave radiation input

FUNCTION UPRIG

find amount of radiation

on vertical surface

CfilLUBAL

use high guess for surface 

temperature, find Rn , S, H, LE

END

WRITE: fln ,S, H. LE

and equilibrium 

surface temperature

CALL RUNTRI

condition soil

temperature profile

NO

CftLlUBAL 

use new guess for 

surface temperature

CALLSECW

provide new guess

for surface temperature

CMLUBAL

use low guess for surface 

temperature, find Rn . S. H, LE

FIGURE 4.—Flow chart of the Outcalt and Carlson URBD 

model (1975).
E8 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

76°45' 76°37'30" 76°30'

Baltimore—Washington 

International

FIGURE 5. Path of the third flight across Baltimore, Md., 

along which multispectral scanner data (including thermal) were obtained by the ERIM aircraft on May 11, 

1972.

Weather records for May 11, 1972, show that the 

day was clear and cool with a steady breeze from 

the west. Temperature ranges for May 9-11, 1972, 

listed in table 1, support the observation that a cold 

front passed through the area on May 10, 1972, 

bringing a fairly homogeneous air mass over the 

Washington-Baltimore area (Alexander and others, 

1976). Under these conditions, hourly meteorological observations from Baltimore-Washington International Airport, located eight miles southwest of 

Baltimore in the direction of Washington, are representative of the region. These hourly values of air 

temperature, wind speed, air pressure, and relative 

humidity are listed in table 2.

The principal advantage of the third May 11, 

1972, flight, over the two flown earlier that day, is 

that it scanned an area with a high diversity of 

land use types. The effects of seven land uses on 

surface temperature were simulated in this study 

to give an idea of the possible range of microclimates within a city. These land uses are: lowdensity residential (LDR), medium-density residential (MDR), high-density residential (HDR),

central business district (CBD), commercial 

(COAfM), transportation (TRANS), and urban 

parks (PARKS). The urban terrain characteristics 

of these land use types are presented in table 3. 

Values of these parameters were determined early

TABLE 1. Recorded meteorological data

[Washington, D.C.: Tm(B occurred at 1640 e.s.t., Tmln occurred at 0515 

e.s.t. Highest observed wind speed since 0800: 671 cm sec-1 at 1720 

e.s.t. from west. 

Source: "Washington Post," daily weather information, May 10-12, 1972 ]

Tuesday, May 9, 1972 (24 hours ending at 2000 e.s.t.)

Tma.(°C) Tmtn (°C) Precip. (cm)

Baltimore _______ 23 14 0.02

Washington, D.C. __ 23 16 .18

Wednesday, May 10,1972 (24 hours ending at 2000 e.s.t.)

rmo.(°C) rm(B (°C) Precip. (cm)

Baltimore _______ 12 10 2.11

Washington, D.C. __ 13 11 1.57

Thursday, May 11, 1972 (24 hours ending at 2000 e.s.t.)

rmo.(°C) Tmtn (°C) Precip. (cm)

Baltimore _______ 21 4 0.0

Washington, D.C. __ 23 7 .0

TABLE 2. Surface weather observations at BaltimoreWashington International Airport, May 11, 1972

[Source: National Climate Center, National Oceanic and Atmospheric 

Administration, Asheville, North Carolina 28801]

Time

(e.s.t.)

0055

0155

0255

0355

0455

0555

0655

0755

0855

0955

1055

1155

1255

1355

1455

1555

1655

1755

1855

1955

2055

2155

2255

2355

Mean

T oi ,

<°C)

7.2

7.8

5.6

5.6

4.4

5.0

8.3

12.8

15.0

15.6

16.7

18.3

18.9

19.4

20.6

21.1

21.1

20.6

18.3

16.1

15.6

14.4

12.8

12.8

13.9

/*

(cm sec-1 )

360

309

257

257

309

257

206

309

515

515

463

515

772

412

669

566

566

566

463

463

463

463

412

257

431

P

(mb)

1021

1021

1021

1021

1021

1022

1023

1023

1023

1023

1022

1021

1021

1020

1019

1018

1018

1018

1017

1018

1018

1019

1019

1019

1020

RH

(percent)

0.60

.56

.60

.60

.64

.67

.65

.55

.41

.36

.34

.32

.30

.31

.31

.31

.31

.31

.34

.41

.44

.47

.53

.53

.50
TESTING AN URBAN CLIMATE SIMULATOR E9

TABLE 3.—Land use characteristics 

[Source: Lewis and Outcalt (1976)]

Land use 

Type (

Low density residential 

(LDR) _____________

Medium density 

residential (MDR) _ 

High density 

residential (HDR) __ 

Central business 

district (CBD) ____ 

Commercial (COAfAf) .. 

Transportation 

(TRANS) _________

Parks — Urban 

(PARKS) __________

ALB

percent)

0.18

.18 

.14

.15 

.15

.15

.23

SRHF

(percent)

0.60

.30 

.01

.05 

.05

.15

.90

Zo 

(cm)

73

86 

110

400 

78

6

80

SILRAT

0.13

.19 

.19

.22 

.02

.02

.17

in the USGS-National Aeronautics and Space Administration study (Alexander and others, 1976) 

and were reported in Lewis and Outcalt (1976).

Although the category names may connote specific images of land use, these categories should not 

be interpreted too literally. For instance, the medium-density residential category specified by Lewis 

had a roughness length of 86 cm and a silhouette 

ratio of 0.19. In contrast, Myrup and Morgan 

(1972) used respective values of 532 cm and a 0.50 

ratio to describe the same category. The land use 

categories used in this study are, therefore, general 

labels assigned for evaluating the influence of the 

urban terrain parameters.

A clearer view of the relationships between the 

land use categories is provided in figure 6. Low 

density, medium density, and high-density residential, for example, are chiefly distinguished by 

differences in surface relative humidity. The commercial and central business district categories both 

have very little evaporative surface, but their height 

and roughness characteristics are clearly dissimilar.

Albedo was added as an urban terrain parameter 

primarily because it distinguishes natural surfaces 

from artificial surfaces. Vegetation, in general, 

tends to reflect much more radiation in the near 

infrared range than do nonvegetated surfaces (Sellers, 1965). This effect would tend to decrease the 

amount of energy absorbed, decreasing the surface 

temperature.

A simulation run, using the URBD model, for 

May 11, 1972, was made using the data in tables 1 

and 2 in order to test the response of the model to 

changes in the urban terrain values. In addition, 

latitude was specified as 39.9° lat., precipitable 

water was 10.0 mm, and dust particles were given 

as 1.0 particles cm-3 (Lewis and Outcalt, 1976).

An observed sky radiant temperature, TSKY, of 

—5° C was used rather than the common formula of 

TSKY=Tm-22° C. An emissivity of 1.0 was assumed 

throughout the model.

Sample output data for the simulation of the 

micrometeorological conditions on May 11, 1972, 

in the medium-density residential land use category 

are shown in tables 4 and 5. The first five columns 

on the left in table 4 repeat meteorological boundary 

conditions input from table 3. The five columns on 

the right show the results of the shortwave radiation calculations. The shortwave radiation flux on a 

horizontal surface outside the Earth's atmosphere 

is depicted as EXT. The sum of the BEAM, diffused, and backscattered radiation flux on a horizontal surface at the Earth's surface is denoted as 

SUN. The total shortwave radiation flux on a vertical surface normal to the solar azimuth angle is 

represented as VERT. The TERRAIN variable 

averages SUN and VERT by considering the silhouette ratio and amount of area in shadow. The 

energy balance equation components for each time 

interval (3600 sec in this case) and the equilibrium 

surface temperature are listed in table 5.

At 0700 (solar time), for example, the model 

calculates that 344 W/nr2 of total shortwave radiation fall on a horizontal surface in Baltimore 

and 759 W/nr2 strike a vertical surface facing 

the sun. Since the silhouette ratio of vertical to 

horizontal surfaces for medium-density residential 

is 0.19, the vertical surface flux does not contribute 

significant energy to the overall average shortwave 

radiation flux of 344 W/nr2 . At this hour, the net 

radiation flux (Rn ) is 243 W/nr2 due to the sunlight and rapidly increasing air temperature. Heat 

is being conducted into the soil (away from the 

surface) at a rate of —243 W/nr2 while the sensible 

heat flux to the air is —167 W/nr2, indicating that 

convection is removing heat from the surface. 

Evaporative heat flux (LE) is still positive, 166 

W/nr2, indicating that heat is being added to the 

surface as water vapor condenses, forming dew.

Graphs of the simulated surface temperatures, 

as calculated from the standard run for each landuse category, are presented in figure 7. Driven by 

the four meteorological variables listed in table 2 

at each time step, the URBD model computes the 

hourly temperatures as the curves labeled "H." 

The input variable reading procedure in the model 

was modified to use the 24-hour temperature average for the four meteorological variables instead of 

observed hourly values. The curve labeled "D" shows 

the resulting surface temperature.
E10 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

440

CBD

.04 .06 .08 .10 .12 .14 

SILHOUETTE RATIO

.16 .18 .20 .22

FIGURE 6.—Site characteristics for seven land use types.

ANALYSIS OF THE MODEL

When the simulations of surface temperature 

have been completed, the effectiveness of the URBD 

model can be evaluated by answering the following questions:

1. Is there a difference between driving the model 

with mean daily values for air temperature, relative humidity, wind speed, and air pressure, 

and driving the model at each time step?

2. How sensitive is the model to changes in the 

urban terrain parameters?

3. How do the simulated surface temperatures compare with observed results?

The seven standard runs of the URBD model, 

shown in figure 7, illustrate the differences between 

surface temperatures calculated with observedhourly-value input and daily-near-value input. The 

daily input curves are relatively smooth and symmetrical around solar noon. The symmetry is not 

surprising, since energy is being added and subtracted from the model by net radiation. The daily 

input model also generates curves with shallow 

ranges, compared to the 20° C changes in the hourly 

input model. In addition, the maximum surface 

temperatures of hourly input lag behind the daily 

input model by two to three hours.

To determine the underlying causes for these
TESTING AN URBAN CLIMATE SIMULATOR Ell

TABLE 4.—Simulation of solar radiation flux for medium-density residential land use, May 11, 1972, Baltimore, Md.

[Latitude=39.9° 

Month=5 

Soil wet fraction=0.30

Soil thermal diffusivity=0.01S 

Precipitable water (mm)=10.0 

Silhouette ratio=0.19

Solar declination=17.6° 

Day of the momh=ll 

Albedo=0.19

Time increments=3600 

Dust particles (cm-8)=1.0 

Roughness length (cm=86]

Solar 

time

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

9.00

9.00

10.00

11.00

12.00

13.00

14.00

15.00

16.00

17.00

18.00

19.00

20.00

21.00

22.00

T (OC)

7.2

7.8

5.6

5.6

4.4

5.0

6.3

12.8

15.0

15.6

16.7

18.3

18.9

19.4

20.6

21.1

21.1

20.6

18.3

16.1

15.6

14.4

12.8

(cm sec-1 )

360

309

257

257

309

257

206

309

575

575

463

515

772

412

669

566

566

566

463

463

463

463

412

p 

(mb)

1021

1021

1021

1021

1021

1022

1023

1023

1023

1023

1022

1021

1021

1020

1019

1018

1018

1018

1017

1018

1018

1019

1019

(Radiation values, in W/m-2)

RH

0.60

.56

.60

.60

.64

.67

.65

.55

.41

.36

.34

.32

.30

.31

.31

.31

.31

.31

.34

.41

.44

.47

.53

EXT

0

0

0

0

0

6

265

524

765

972

1131

1231

1307

1231

1131

972

765

524

265

6

0

0

0

BEAM

0

0

0

0

0

0

101

291

491

671

812

901

932

902

812

671

492

292

101

0

0

0

0

SUN

0

0

0

0

0

3

144

344

554

729

872

970

994

963

873

731

549

344

145

3

0

0

0

VERT

0

0

0

0

0

3

556

759

786

722

609

494

440

494

610

723

831

761

559

3

0

0

0

TERRAIN

0

0

0

0

0

3

162

344

537

697

807

865

883

866

807

699

538

345

162

3

0

0

0

model differences, the data were analyzed using a 

variation of the Students' t-test as described by 

Outcalt (1972a). The maximum surface temperatures, taken from the graphs, were regressed against 

four of the urban terrain parameters in two multiple-linear regression models: one for the daily 

mean value input, and one for the hourly observed

TABLE 5.—Simulation of micrometeorological conditions for 

medium-density residential land use, May 11, 1972, Baltimore, Md.

Solar Time

0.00 

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

11.00

12.00

13.00

14.00

15.00

16.00

17.00

18.00

19.00

20.00

21.00

22.00

23.00

* 

-84 

-83

-81

-81

-81

-79

72

243

434

592

697

755

777

753

699

591

434

246

69 -87

-91

-91

-90

-89

s

-77 

-55

-47

-47

-63

-66

-156

-243

-238

-265

-303

-292

-232

-310

-230

-211

-159

-95

-58

-40

-56

-70

-89

-66

(W/nf2 )

H

-69 

-39

-29

-29

-45

-41

-82

-167

-246

-273

-283

-297

-320

-264

-281

-227

-171

-103

-54

-37

-54

-66

-77

-36

LE

222

178

157

157

188

185

167

166

50 -54

-112

-165

-225

-179

-188

-153

-105

-49

43

166

201

227

256

185

m

8.4 

8.6

6.3

6.3

5.4

6.0

10.7

16.5

18.7

19.7

21.3

22.8

22.5

24.2

24.1

24.4

23.6

22.0

19.2

16.7

16.4

15.5

14.1

13.7

value input. The Students' t-test was then used to 

compare the slope (b value) for a parameter in one 

model with the slope for the same parameter in 

the other model. When the slopes are significantly 

different, it can be inferred that the action of the 

test terrain parameter in both models helped create 

the resulting differences in surface temperature. 

The results of this test, applied to both daily maximum temperatures and temperature ranges, are 

listed in table 6.

The similarity of the maximum temperatures observed in figure 7 suggests that there were no significant model differences caused by the terrain parameters, and the t-test confirms this. All eight test 

statistics for (H-D)/S.E.(H) and (H-D)/S.E.(D) 

fall below the critical region. It is surprising, 

though, that the large differences in range cannot 

be attributed to any of the variables tested. Despite 

diurnal range differences of up to 20° C, the test 

statistics were even smaller than in the test on 

maximum surface temperature.

The next step was to examine the sensitivity of 

the hourly input model to changes in the terrain 

parameters. Sensitivity testing of a model can be 

approached several ways. At one extreme, the model 

can be subjected to large perturbations in parameter 

values and the temperature responses can then be 

examined. At the other extreme, parameter values 

can be changed by the potential experimental 

measurement error (Myrup and Morgan, 1972).
E12 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

31.00

23.75

16.50

9.25

2.00

31.00

23.75

16.50

9.25

i I r

I____I____I

B

2.00

D -

0.00 6.00 12.00 18.00 24.00 0.00 6.00 12.00 18.00 24.00 0.00 6.00 12.00 18.00 24.00 0.00 6.00 12.00 18.00 24.00

SOLAR TIME SOLAR TIME SOLAR TIME SOLAR TIME

D E F G

FIGURE 7.—Simulated surface temperatures in Baltimore, Md., for various land use categories based on hourly and averaged daily values on May 11, 1972. H indicates meteorological variables input hourly; D indicates meteorological variables input daily. A, Low-density residential. B, Medium-density residential. C, High-density residential. D, Central 

business district. E, Commercial land use. F, Transportation land use. G, Parks—urban land use.

In climate simulation, it is often meaningless to 

have large perturbations in just one variable since 

such a change is normally correlated with changes 

in a number of other variables. In this study, a 

middle ground between the extremes was sought 

by- increasing and decreasing terrain parameter 

values by 30 percent.

The control run temperatures for 0500 and 1300 

hours, using the land use input data described 

previously, are shown in table 7. Each of the parameters, ALB, fe0, SILRAT, *, and SRHF were then 

altered by 30 percent, while all other variables 

were kept equal to their control run values. The 

results of these tests are listed in tables 8-12 and 

summarized in table 13.

Very little change in surface temperatures can 

be discerned from the data at 1300. The overall 

average deviation from the control temperature is

±0.53° C. The maximum deviation,-2.7° C, occurred 

when SRHFxl.3 was applied to the category 

PARKS. Over this range of perturbation, all five 

parameters acted linearly, causing temperatures to 

vary as much above the control values as below. 

Compare, for example, the columns labeled "TaurfTc" t at 1300 in table 10. A 30 percent decrease in 

SRHF creates a temperature change with the same 

magnitude, but opposite direction, as a 30 percent 

increase in SRHF.

Overall, changes in SRHF caused the largest 

temperature changes. Looking at the tables, it is 

clear that changes in SRHF had the greatest effect 

on those land use types with high SRHF values. 

Conversely, K had the most influence on those land 

use types with low SRHF values.

The results at 0500 are not as easily explained. 

In almost all tests, both increased and decreased
TESTING AN URBAN CLIMATE SIMULATOR E13

TABLE 6.—Significance test for differences between URBD 

model using observed hourly data and mean daily data 

as input

TABLE 7.—Control run values of the URBD model for use in 

sensitivity testing.

[T0 is the control run temperature]

Maximum surface temperature

Hourly values (H)

Daily values (D)

Standard

error, S.E. (H)

Standard

error, S.E. (D)

(H-D)/S.E. (H) _

(H-D)/S.E. (D) _

__ —16.0

__ —19.4

43.7

92.4

.08

.04

—12.6

—16.1

4.1

8.6

.9

.4

0.002 2.3

—.007 .2

.009 6.5

.009 13.8

2.25 .32

1.0 .15

Diurnal temperature range

Hourly values (H)

Daily values (D) _

Standard

error, S.E. (H)

Standard

error, S.E. (D)

(H-D)/S.E. (H) _

(H-D)/S.E. (D) _

__ —14.8

_ —29.6

53.7

92.3

.28

.16

—8.0 —0.004 —1.7

—1.9

5.0

8.6

—1.22

—.71

—.01 —5.3

.005 8.0

.009 13.8

1.2 .45

.67 .26

parameter values caused a slight increase in temperature. The only exceptions were those land use types 

which had an extreme value for the parameter 

being tested. Although the model appears to be 

ignoring the magnitude of the change in each 

parameter, it is more likely that the change does 

not have a linear effect on the energy-balance components. By 0500, the surface temperature had a 

number of hours to come to equilibrium with Tair 

and T2 in the absence of additional radiation. Since 

the signs of the values Tair - Tsurf and T2 - T8urf 

control the direction of flux, the components could 

be particularly sensitive.

The results of the test for differences between 

the hourly input model and the daily input model, 

and the sensitivity tests suggest that changes in 

individual urban terrain parameters have very 

little impact on the URBD model. The wide diurnal 

range of surface temperatures observed in figure 7,

LDR _________________ . MDR _______________ . HDR _________________ . CBD ___ ________ . COMM __ _ _ _____ .

TRANS ______________ .

PARKS ______________ .

0500

. _______ 4.6

. _______ 6.0

. _______ 7.5

9.1

. _______ 6.8

. _______ 5.0

_ _______ 3.1

1300

20.8

24.2

29.0

27.9

27.5

27.1

17.4

discussed earlier, suggests that wind speed and air 

temperature may be quite influential. Since hourly 

values for both wind speed and air temperature 

more than doubled over the course of May 11, 

1972, such dynamic atmospheric conditions represent a realistic test of the model.

The response of the model to large changes in 

wind speed is shown in tables 14 and 15. By examining the temperature change between /xX0.25 and 

/xXO.50 and comparing it to the change between 

/xXl.5 and /AX2.0, one can conclude that the model 

is more sensitive to changes in wind speed at low 

velocities. Land use types with little evaporative 

surface area, such as HDR, CDB, and COMM, however, showed very little response to changes in 

speed. The test of the model response to changes in 

air temperature was combined with the verification 

of the model so the two will be discussed simultaneously.

Observed radiant surface temperatures at 1400 

e.s.t. for Baltimore on May 11, 1972, are shown in 

table 16. These values were corrected temperatures 

obtained from the ERIM thermal scanner data 

(Lewis, Outcalt, and Pease, 1975). As shown in 

figure 8, the model consistently underpredicts. 

Since the average deviation from the line of perfect prediction is -14° C, 14° C was added to each 

hourly air temperature, and the model was rerun. 

By adding 14° C, the average hourly temperature

TABLE 8.—Sensitivity of the URBD model to changes in substrate diffusivity

[Temperatures in degrees Celsius]

.X0.7 • Xl.3

Land use 0500 1300 0500 1300

Tsurf

LDR ___ _____________

MDR ________________

HDR ________________

CBD ______ ___________

COMM ______________

TRANS _ _________

PARKS _ ______

_____ 5.0

_____ 6.6

__ ———— 8.4

9.8

. _____ 7.6

_____ 5.7

_ _____ 3.3

0.4

.6

.7

.8

.7

.2

on q

24.8

30.8

28.8

29.0

28.9

17.3

0.1

.6

1.8

.9

1.5

1.8 -.1

5.0

6.2

7.3

8.8

6.8

5.4

3.6

0.4

.2 -.2

-.3

.0

.4

.5

90 7

23.7

27.6

27.1

26.3

25.7

17.5

-0.1

-.5

-1.4

-.8

-1.2

-1.4

.1
E14 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

TABLE 9.—Sensitivity of the URBD model to changes in the height of obstructions (h»)

[Temperatures in degrees Celsius]

Land use

LDR ________ . MDR _ ___ _.

HDR ________ .

CBD ________ .

COMM ______ .

TRANS _____ .

PARKS ____ .

TABLE

Land use

LDR ________ . MDR _______ . HDR ________ .

CBD ________ . COMM _____ ..

TRANS _____ .

PARKS _____ .

n0 X0.7 h0 X1.3

0500 1300 0500

Tsurf Tsurf— Tc Tsurf Tsurj — Tc Tsurf Tsurf~Tc Tsurf

. ________________ 4.9 0.3 21.4 0.6 5.0 0.4 20.3

. ___ ______ 6.2 .2 24.6 .4 6.5 .5 23.8

. __________ 7.4 -.1 28.9 -.1 8.0 .5 29.0

. __ _______ 8.7 -.4 27.9 .0 9.6 .5 27.8

. __________ 6.9 .1 27.5 .0 7.4 .6 27.4

. __________ 5.4 .4 27.1 .0 5.6 .6 26.9

. __________ 3.5 .4 18.1 .7 3.5 .4 16.8

1300

Tsurf-Tc

-0.5

-.4

.0 i

-.1 

-.2

-.6

10. — Sensitivity of the URBD model to changes in the surface relative humidity fraction (SHRF) 

[Temperatures in degrees Celsius]

SRHF X0.7 SRHF Xl.3

0500 1300 0500

Tsurf Tsurf— Tc Tsurf Tsurf— Tc Tsurf Tsaif—Tc Tsurf

. _______ — - ——— 5.7 1.1 22.8 2.0 4.1 -0.5 18.9

. __________ 6.7 .7 25.4 1.2 6.0 .0 23.0

. __________ 7.8 .3 29.0 .0 7.7 .2 28.9

. __________ 9.4 .3 28.3 .4 9.1 .0 27.4

. __________ 7.2 .4 27.7 .2 7.1 .3 27.2

. __________ 5.6 .6 27.2 .1 5.5 .5 26.7

. __________ 4.9 1.8 20.1 2.7 1.8 -1.8 14.7

1300

Tsurf— Tc

-1.9

-1.2

-0.1

-.5

-.8

-.4

-2.7

TABLE 11. — Sensitivity of the URBD model to changes in silhouette ratio (SILRAT) 

[Temperatures in degrees Celsius]

Land uie

LDR ________ . MDR _______ . HDR ________ . CBD _ __ . COMM ______ .

TRANS ____ .

PARKS _____ .

SILRAT X 0.7 SILRAT X 1.3

0500 1300 0500

Tsurf Tsurf— Tc Tsurf Tsurf— Tc Tsurf Tsurf~Tc Tsurf

. __________ 4.9 0.3 21.3 0.5 5.0 0.4 20.4

. __________ 6.1 .1 24.4 .2 6.5 .5 24.0

. __________ 7.3 -.2 28.7 -.3 8.1 .6 29.2

. __________ 8.7 -.4 27.8 -.1 9.7 .6 27.9

. __________ 6.8 .0 27.4 -.1 7.4 .6 27.5

. __________ 5.4 .4 27.0 -.1 5.6 .6 26.9

. __ _______ 3.5 .4 18.0 .6 3.5 .4 17.0

1300

Tsurf-Tc

-0.4

-.2

.2

.0

.0 

o

-.4

TABLE 12. — Sensitivity of the URBD model to changes in albedo (ALB) 

[Temperatures in degrees Celsius]

Land use

LDR ________

MDR _______ . HDR ________ . CBD _ _ __ .

COMM ______ .

TRANS _____ .

PARKS _____ .

ALB X 0.7 ALB Xl-3

0500 1300 0500

Tsurf T,urf—Tc Tsurf Tsurf— Tc Tsurf Tsurf '~ Tc Tsurf

. __________ 5.0 0.4 21.1 0.3 5.0 0.4 20.5

. __________ 6.3 .3 24.5 .3 6.3 .3 23.9

. __________ 7.8 .3 29.2 .2 7.8 .3 28.7

. _____ ____ 9.3 .2 28.0 .1 9.3 .2 27.7

. _____ _ ___ 7.1 .3 27.7 .2 7.1 .3 27.2

. __________ 5.5 .5 27.4 .3 5.5 .5 26.6

. __________ 3.5 .4 17.8 .4 3.5 .4 17.0

1300

Tsurf— Tc

ft Q

-.3

-.3

-.2

-.3

-.5

-.4

TABLE 13. — Rank (R) of absolute value of Tsurf-Tc at 1300 for each land use type 

[Temperature value is the average of parameterxO.7 and parameter x 1.31

Parameter

SRHF _______

ho ___________

SILRAT _____

ALB ________

/C —

LDR MDR HDR CBD COMM TRANS

°C (R) °C (R) °C (R) °C (R) °C (R) °C (R)

_ ___ 1.95 (1) 1.20 (1) 0.05 (3) 0.45 (2) 0.25 (2) 0.25 (3)

_ ___ .55 (2) .40 (3) .05 (3) .00 (5) .05 (3) .10 (5)

_______ .45 (3) .20 (5) .25 (2) .05 (4) .05 (3) .15 (4)

_______ .30 (4) .30 (4) .25 (2) .15 (3) .25 (2) .40 (2)

_ ___ .10 (5) .55 (2) 1.60 (1) .85 (1) 1.35 (1) 1.60 (1)

PARKS

°C(R)

2.70 (1) 

.65 (2) 

.50 (3) 

.40 (4) 

.10 (5)
TESTING AN URBAN CLIMATE SIMULATOR E15

TABLE 14.—Sensitivity of the URBD model to decreased wind speed (/t) 

[Temperatures in degrees Celsius]

0X0.25 tXO.50

Land use 0500 1300 0500 1300

Tsurf

LDR __________ .

MDR _________ .

HDR __________ .

CBD ________________ .

COMM ______________ .

TRANS _____________ .

PARKS ____________ .

. _____ 4.6

. _____ 5.4

. _____ 6.0

_ __ 7.4

. _____ 5.7

. _____ 4.8

. _____ 3.6

0.0 -.6

-1.5

-1.7

-1.1

-.2

.5

25.4

26.9

OQ O

28.2

27.5

27.8

23.1

4.6

2.7 -.2

.3

.0

.7

5.7

4.8

5.8

6.8

8.4

6.3

5.1

3.5

0.2 -.2

-.7

-.7

-.5

.1

.4

23.1

25.6

28.8

28.0

27.5

27.5

20.0

2.3

1.4 -.2

.1

.0

.4

2.6

TABLE 15.—Sensitivity of the URBD model to increased wind speed (/t) 

[Temperatures in degrees Celsius]

t Xl.5

Land use 0500 1300 0500 1300

4-Tc

LDR _________________ .

MDR ______ _____ .

HDR __________ .

CBD ________________ .

COMM ________ .

TRANS _____________ .

PARKS ____________ _

5.1

. _____ 6.7

. _____ 8.4

Q n

7 ft

. _____ 5.9

._ ____ 3.4

0.5

.7

q

.6

1.0

.9

.3

10 ft

OO A

OQ n

97 7

27.4

26.6

16.1

1 9

.0

-.1

-.5

-1.3

5.1

a Q

Q Q

10.1

Q O

a o

3.4

0.5

1.4

1.0

1.4

1 9

.3

1ft Q

99 Q

oo n

97 7

27.4

9fi A.

15.3

1 a

1 o

.0

rj

-2.1

TABLE 16- —Comparison of observed and simulated surface temperature, UOO EST, May 11, 1972 (in degrees Celsius)

[Control run: r=0.95, bi =0.62, / air+14°C: r=0.95, bl =1.09 (Lewis and others, 1975)]

Land use

LDR ___________ .

MDR ___________ .

HDR ___________ .

COMM _________ .

TRANS ______ .

PARKS ________ .

Observed

. ___ 32.9

. ___ 40.0

. ___ 47.9

. ___ 40.0

. ___ 40.0

. ___ 28.0

Taurf

Simulated 

(Control)

20.8

24.2

29.0

27.5

27.1

17.4

Simulated

CGfr + 14° C

31.4

37.1

47.4

44.2

40.0

26.7

TABLE 17.—Simulation of global incoming solar radiation

[Measured on the parking lot at the State fairgrounds, Timonium, Md. 

on May 11, 1972 (Lewis and others, 1975)]

Radiation (W/nM)

was doubled. The resulting surface temperatures 

average 55 percent higher than the control temperatures (see table 16).

This second set of predicted temperatures is 

much closer to the observed values than those of 

the control run. If surface temperature was a linear 

function of air temperature, the slope of the regression line for the control data would be expected to 

be the same as the slope for the Tair +14° C data. 

These two slopes are 0.62 for the control run and 

1.09 for the increased air temperature. Unfortunately, there are not sufficient degrees of freedom to 

test the hypothesis that the two slopeirare equal.

A secondary check on the model is provided by 

global radiation data collected at Timonium, Md., 

on May 11,1972, (Lewis, Outcalt, and Pease, 1975). 

The location of Timonium, about 12 miles north of

0600

0700

0800

0900

1000

1100

1200

1300

1400

1500

Measured

84

286

481

725

809

941

997

955

851

662

Simulated

141

338

541

722

865

955

986

955

865

724

Baltimore, is depicted in figure 5. The observed 

data shown in table 17 compares quite closely to 

simulated global radiation, particularly between 

0900 and 1400. The maximum deviation between 

those hours is 6.9 percent.

CONCLUSION

The application of an energy-balance simulation 

model to urban area analyses requires recognition 

of the tremendous variety of surfaces, heights, orientations, densities, and materials found in a city. 

Although sensitivity analysis demonstrates that 

changes on the order of 30 percent, in individual 

urban terrain parameters, have relatively little effect on surface temperatures, the effects can be
E16 THE INFLUENCES OP LAND USE AND LAND COVER IN CLIMATE ANALYSIS

51

47

O

39

cc 

2 35

I 3,

<J 27

23

5 19

15

r/M/vs.

15 19 23 27 31 35 39 43 47 51 

OBSERVED SURFACE TEMPERATURE, IN DEGREES CELSIUS

FIGURE 8.—Effect of increased air temperature on the predicted surface temperature. Control run values represented by a small circle and Tatr + 14 °C values by an 

x.

multiplied when more than one parameter are 

considered. The seven different land use types, specified by the urban terrain variables, had varied responses to the same meteorological boundary conditions.

This result suggests that further work in analyzing the URBD model could start by using each of 

the three-dimensional cells of figure 6 as a unique 

"land use." One could then test the surface-temperature response to changes in three linked parameters 

rather than just one.

The comparison of the hourly observed-value input model with the daily mean-value input model 

demonstrates the advantage of time dependent values for wind speed and air temperature. The significant differences suggest that the effects of changes 

in air relative humidity should also be tested. Although the use of an average air temperature did 

not appear to seriously affect the surface temperatures around midday, the consequences appear to 

be more serious at night. In the absence of any 

shortwave radiation, air with a temperature much 

warmer than the ground temperature provides a 

major source of heat to the system.

The sensitivity of the model to air temperature 

and wind speed calls into question the use of uniform meteorological boundary conditions for all

land use types. Characteristically, temperatures 

throughout a city are not uniform 6 feet off the 

ground. Since this value exerts such strong control 

on the sensible heat flux to the air and evaporative 

heat flux, its specification should be carefully 

studied.

While the predictive value of the URBD model 

was deemphasized in this study, the goal of accurately simulating urban meteorological processes 

provides the impetus for modeling urban climates. 

In some ways, the fortuitous results of the first attempt to verify an urban simulator (Outcalt, 1972a 

and 1972b) may have misled investigators. The 

fact that Outcalt's simulated temperatures were 

within 1.5° C of observed temperatures at Ann 

Arbor, Mich., belies the complex energy exchange 

processes occurring in a city. A unique surface 

temperature occurs for a given set of energy fluxes, 

but an observed surface temperature can result from 

a numerous set of urban terrain parameters from 

which the four components of the energy balance 

equation are calculated. It should also be pointed 

out, though, that Baltimore is a larger, more complex site than Ann Arbor. In addition, the potential 

climatic effects of Chesapeake Bay are ignored by 

the one-dimensional URBD model.

In this case, the real benefit of an urban climate 

simulator lies not in the predicted values but in 

the ability to examine relationships between components. An understanding of the sensitivity of the 

model, in turn, can be used to improve simulation 

techniques and field verification.

REFERENCES CITED

Alexander, R. A., Lewis, J. E., Lins, H. F., Jenner, C. B., 

Outcalt, S. I., and Pease, R. W., 1976, Application of 

Skylab data to land use and climatological analysis: 

Final Report SKLAB/EREP Investigation No. 469, 210 p.

Bornstein, R. D., 1968, Observations of the urban heat island 

effect in New York City: Journal of Applied Meteorology, 

v. 7, no. 4, p. 575-582.

———— 1975, The two-dimensional URBNET urban boundary 

layer model: Journal of Applied Meteorology, v. 14, no. 8, 

1459-1477.

Gutman, D. P. and Torrance, K. E., 1975, Response of the 

urban boundary layer to heat addition and surface 

roughness: Boundary Layer Meteorology: v. 9. no. 2, p. 

217-233.

Hage, K. D., 1975, Urban-rural humidity differences: Journal 

of Applied Meteorology, v. 14, no. 7, 1277-1283.

Leahey, D. M. and Friend, J. P., 1971, A model for predicting 

the depth of the mixing layer over an urban heat island 

with application to New York City: Journal of Applied 

Meteorology, v. 10, no. 6, p. 1162-1173.

Lettau, H., 1969, Note on aerodynamic roughness parameter 

estimation on the basis of roughness element description:
TESTING AN URBAN CLIMATE SIMULATOR E17

Journal of Applied Meteorology, v. 8, p. 828-832.

Lewis, J. E., and Outcalt, S. I., 1976, Verifying an urban 

surface climate simulation model: paper submitted to be 

read at the International Geographical Union held in 

Moscow, July-August, 1976.

Lewis, J. E., Outcalt, S. I., and Pease, P. W., 1975, urban 

surface thermal response associated with land use: unpublished paper presented at the Symposium on Meteorology as Related to Urban and Regional Land-Use 

Planning, Asheville, North Carolina, November 1975.

Marotz, G. A. and Coiner, J. C., 1973, Acquisition and characteristics of surface material data for urban climatological 

studies: Journal of Applied Meteorology, v. 12, no. 6, p. 

919-923.

McElroy, J. L., 1973, A numerical study of the nocturnal 

heat island over a medium-sized mid-latitude city 

(Columbus, Ohio): Boundary Layer Meteorology, v. 3, p. 

442-453.

Myrup, L. 0., 1969, A numerical model of the urban heat 

island: Journal of Applied Meteorology, v. 8, no. 6, p. 

908-918.

Myrup, L. 0., and Morgan, D. L., 1972, A numerical model of 

the urban atmosphere: Contributions in Atmospheric 

Science, v. 1, no. 4, University of California at Davis, 

237 p.

Outcalt, S. I., 1972a, A reconnaissance experiment in mapping 

and modeling the effect of land use on urban thermal

regimes: Journal of Applied Meteorology, v. 11, no. 8, 

p. 1369-1373.

1972b, A synthetic analysis of seasonal influences in

the effects of land use on the urban thermal regime: 

Archiv fur Meteorologie, Geophysik und Bioklimatologie, 

ser. A, v. 20, p. 253-260.

Outcalt, S. L, and Carlson, J., 1975, A coupled soil thermal 

regime surface energy budget simulator: Conference on 

Soil Water Problems in Cold Regions, Calgary, Canada, 

proceedings, 211 p.

Schneider, S. H. and Dickinson, R. E., 1974, Climate modeling: Review of geophysics and space physics, v. 12, no. 3, 

p. 447-493.

Sellers, W. D., 1965, Physical Climatology: Chicago, University of Chicago Press, 272 p.

Terjung, W. H., 1970, Urban energy balance climatology: 

Geographical Review, v. 60, no. 1, p. 31-53.

Terjung, W. H. and Collaborators, 1970, The energy-balance 

climatology of a city-man system, Annals Association 

of American Geographers, v. 60, no. 3, p. 466-492.

Tuller, S. E., 1973, Microclimatic variations in a downtown 

urban environment: Geografiska Annaler, v. 55A, no. 3, 

p. 123-136.

The Washington Post, Daily weather data, May 10-12, 1972.

Yu, T. W. and Wagner, N. K., 1975, A numerical study of the 

nocturnal urban boundary layer: Boundary Layer Meteorology, v. 9, no. 2, p. 143-162.

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