EPA-650/4-75-017
May 1975
Environmental Monitoring Series
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ERRATA
Page 6, line 7 should read "is the Stefan-Boltzman constant and T is the
absolute temperature measured"
Page 15 (Figure 3-1) and page 22 (Figure 3-2) "(/*)" should be deleted
from the abscissa labels
Page 26, line 2 of Table 3-2 legend should read "READINGS (T/km) AND
TURBIDITY (I/optical air mass)"
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EPA-650/4-75-017
EFFECTS OF ATMOSPHERIC AEROSOLS
ON INFRARED IRRADIANCE
AT THE EARTH'S SURFACE
IN A NONURBAN ENVIRONMENT
by
Michael R. Riches
Department of Geosciences
North Carolina State University
Raleigh, North Carolina
James T. Peterson and Edwin C. Flowers
Meteorology Laboratory
Program Element No. 1AA009
ROAP No. 26AAS
Prepared for
ENVIRONMENTAL PROTECTION AGENCY
Office of Research and Development
National Environmental Research Center
Research Triangle Park, N. C. 27711
May 1975
-------
James T. Peterson and Edwin C. Flowers are on assignment from the
National Oceanic and Atmospheric Administration, U.S . Department
of Commerce.
EPA REVIEW NOTICE
This report has been reviewed by the National Environmental Research
Center - Research Triangle Park, Office of Research and Development,
EPA, and approved for publication. Approval does not signify that the
contents necessarily reflect the views and policies of the Environmental
Protection Agency, nor does mention of trade names or commercial
products constitute endorsement or recommendation for use.
RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, U.S. Environ-
mental Protection Agency, have been grouped into series. These broad
categories were established to facilitate further development and applica-
tion of environmental technology. Elimination of traditional grouping was
consciously planned to foster technology transfer and maximum interface
in related fields. These series are:
1. ENVIRONMENTAL HEALTH EFFECTS RESEARCH
2. ENVIRONMENTAL PROTECTION TECHNOLOGY
3. ECOLOGICAL RESEARCH
4. ENVIRONMENTAL MONITORING
5. SOCIOECONOMIC ENVIRONMENTAL STUDIES
6. SCIENTIFIC AND TECHNICAL ASSESSMENT REPORTS
9. MISCELLANEOUS
This report has been assigned to the ENVIRONMENTAL MONITORING
series. This series describes research conducted to develop new or
improved methods and instrumentation for the identification and quanti-
fication of environmental pollutants at the lowest conceivably significant
concentrations. It also includes studies to determine the ambient concen-
trations of pollutants in the environment and/or the variance of pollutants
as a function of time or meteorological factors.
This document is available to the public for sale through the National
Technical Information Service, Springfield, Virginia 22161.
Publication No. EPA-650/4-75-017
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CONTENTS
Page
LIST OF FIGURES iv
LIST OF TABLES iv
ABSTRACT v
1. INTRODUCTION AND LITERATURE REVIEW 1
INTRODUCTION 1
LITERATURE REVIEW 1
2. EXPERIMENTAL DESIGN 5
INTRODUCTION 5
RADIATION INSTRUMENTS 5
CALIBRATION OF RADIATION SENSORS 6
MEASUREMENT OF AEROSOLS 7
CALCULATION OF THE DOWNWARD-DIRECTED INFRARED IRRADIANCE .... 8
DATA COLLECTION AND SITE LOCATION 11
3. RESULTS 13
OBSERVED-MINUS-CALCULATED IRRADIANCE AS A FUNCTION OF
NEPHELOMETER READINGS AND TURBIDITY 13
RELATIVE HUMIDITY RESULTS 24
4. DISCUSSION 30
5. LIST OF REFERENCES 33
TECHNICAL REPORT DATA SHEET 36
ni
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LIST OF FIGURES
Figure Page
3-1 Scatter Diagram for Observed-Minus-Calculated Irradiance
Versus Nephelometer Readings (Extinction Coefficient)
for all Data (Calculated Irra,diance from the
Yamamoto Chart) 15
3-2 Scatter Diagram for Observed-Minus-Calculated Irradiance
Versus Nephelometer Readings (Extinction Coefficient)
for the Hazy Season (calculated irradiance from the
Yamamoto Chart) 22
3-3 Scatter Diagram for Observed-Minus-Calculated Irradiance
Versus Turbidity for the Hazy Season (Calculated
Irradiance from the Yamamoto Chart) 23
3-4 Scatter Diagram for Observed-Minus-Calculated Irradiance
Versus Relative Humidity for all Data (Calculated
Irradiance from the Yamamoto Chart) 28
LIST OF TABLES
Table Table
3-1 Regression Analysis and Analysis of Variance for Observed-
Minus-Calculated Irradiances (l.y/nrin) Versus Nephelometer
Readings (I/km x 10) and Turbidity (1/optical air mass)
for all Data and for Clean and Hazy Seasons 17
3-2 Regression Analysis and Analysis of Variance for
Nephelometer Readings (I/km x 10) and Turbidity (I/optical
air mass) Versus Relative Humidity (percent) for the
Three Stratifications 26
3-3 Regression Analysis and Analysis of Variance for Observed-
Minus-Calculated Irradiances (ly/min) Versus Relative
Humidity (percent) for all Data and for Clean and
Hazy Seasons 27
IV
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ABSTRACT
Atmospheric aerosols can affect the surface radiative energy
budget through their effects on solar (0.3 to 3.0 micrometers) and
infrared (3 to 50 micrometers) radiative transfer. While many studies
have focused on the relation between aerosols and observed solar radia-
tion, very few in situ measurements have been simultaneously made of
aerosol amounts and infrared radiation. This report describes a study
designed to measure hemispheric infrared downward-directed irradiance
at the earth's surface and ambient aerosol concentrations at Research
Triangle Park, North Carolina. A Funk type net radiometer (with a
blackened cavity on the underside) was used to measure the incident
all-wave energy. From the value obtained, the observed solar radiation
was subtracted to determine the infrared component. The expected
incident infrared irradiance was calculated from prevailing atmospheric
conditions. Six methods were used for these calculations: four
empirical equations based on surface conditions, the Yamamoto chart,
and a radiative transfer program using vertical profiles of temperature
and moisture.
The observed-minus-calculated downwelling irradiances were then
compared to concurrent measurements of the turbidity obtained with a
Volz sunphotometer, nephelometer-indicated atmospheric extinction
coefficient, and relative humidity. These measurements were analyzed
by least-squares regression to determine the extent to which incident
hemispheric infrared radiation is affected by varying amounts of
-------
atmospheric aerosols and relative humidity. The results suggested
that for a typical hazy summer afternoon with 00250 turbidity and
0.2 km~ extinction coefficient the excess downward-directed irradiance
was approximately 0.03 langley per minute, which is some 6 percent of
typical downward infrared irradiances. A nonlinear dependence of excess
downwelling irradiance on relative humidity was also suggested.
VI
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EFFECTS OF ATMOSPHERIC AEROSOLS
ON INFRARED IRRADIANCE
AT THE EARTH'S SURFACE
IN A NONURBAN ENVIRONMENT
SECTION 1. INTRODUCTION AND LITERATURE REVIEW
INTRODUCTION
Atmospheric aerosols can influence the surface radiative energy
budget through their effects on solar (003 to 3»0 micrometers (ym)
wavelength) and terrestrial infrared (3 to 50 ym wavelength) radiative
transfer. Many studies have focused on the relation between aerosol
concentrations and observed solar radiation; for example, Robinson (1962),
Flowers and Viebrock (1965), McCornrick and Ludwig (1967), Paltridge and
Platt (1972), and Idso (1972b). Investigators have, however, made very
few in situ measurements simultaneously of aerosol concentrations and
infrared radiation. The study covered in this report was designed to
provide insight into the relation between ambient aerosol concentrations
and hemispheric infrared irradiance incident at the earth's surface.
Measurements of hemispheric infrared downward-directed radiation
(HIDR) at the earth's surface, along with turbidity and nephelometer
readings (indicators of atmospheric aerosol concentrations), were taken
during cloud-free conditions. The expected HIDR for an aerosol-free
1
-------
atmosphere was also calculated at each observation time. On the basis
of a comparison of the observed-minus-calculated irradiances with the
turbidity and nephelometer readings, the interdependence of these
quantities was studied.
LITERATURE REVIEW
One of the first references to the effect of aerosols on the infrared
irradiance may be found in the literature of more than 20 years ago, as
Robinson (1950) noted a variable component of up to 10 percent in his
measurements of HIDR. He ascribed this variation to atmospheric
aerosols. Increased aerosol concentrations were associated with increased
HIDR. Sheppard (1958) calculated the expected HIDR for an aerosol-
free atmosphere and compared these calculated values to measured
infrared irradiances. His calculations showed about a 10 percent
excess in the HIDR as compared to an aerosol-free atmosphere, which
agreed with Robinson's conclusions.
More recently, measurements and theoretical studies of the
influence of atmospheric dust on infrared radiation over Northwest
India (Peterson and Bryson, 1968; Sargent and Beckman, 1973; and
Lai, 1973) have shown an increase in infrared irradiance at the
earth's surface apparently due to the dust. Using dust profiles and
meteorological data obtained over the Rajasthan Desert, Sargent and
Beckman found as much as a 20 percent increase in HIDR as compared
to that calculated for a dust-free atmosphere. They also found that
the increase shown by the model was highly dependent on the amounts
and vertical distribution of the aerosol. The basic results of all
three studies indicated that the three primary effects of the aerosol
2
-------
were a decrease in the upward infrared flux, an increase in downward
infrared flux, and a decrease in the net infrared flux, compared to
a dust-free atmosphere.
Other studies on wind-blown dust (Idso, 1972a; 1973) at Phoenix,
Arizona, also have shown a significant increase in HIDR apparently due
to the dust. Idso found a 12 percent increase for a winter dust storm
and a 4.3 percent increase for a summer dust storm, reemphasizing
Robinson's earlier conclusions. Staley and Jurica (1972) computed
the effective atmospheric emissivity for an aerosol-free atmosphere.
Measurements made in conjunction with their study suggested additional
HIDR from aerosols. From aircraft spectral measurements at 8.5 ym to
16 ym over desert terrain, Hovis et al. (1968) also determined that
aerosols have a significant effect on the emissivity of the atmos-
phere.
Not all researchers agree that increased concentrations of
atmospheric aerosols result in significantly increased HIDR. Primarily,
the lack of data on aerosol absorption and scattering coefficients in
the infrared region have handicapped modelers and forced assumptions
that may not be totally realistic. Rasool and Schneider (1971)
included aerosols in their climatic model, but found little effect
on the infrared flux. Other climatic modelers (e.g., Mitchell, 1971)
have chosen to assume that aerosols have little or no effect on
infrared radiative transfer. In a dense haze (continental origin)
over the sea at Coff's Harbour, Australia, Paltridge and Platt (1972)
found no increase in the HIDR as compared to clear-sky data taken on
an earlier expedition (Platt, 1972). Their computations also verified
this result.
-------
Recently, two studies of urban-rural variations in HIDR have
been reported in the literature. Oke and Fuggle (1972) measured this
parameter at night, but ascribed the excess HIDR in the urban environ-
ment to warmer atmospheric temperatures, instead of to aerosols.
At Hamilton, Ontario, Canada, Rouse et al. (1973) measured the HIDR
at a rural and an urban site over a 3-year period. Their data
indicated a significant increase in the HIDR during the day at the
urban site as compared to the rural site. Little difference was
found at night. The incident all-wave (solar plus infrared) radiation
was about the same for both stations. The authors ascribed these
observations to higher relative emissivities because of heavy particulate
loading in the urban atmosphere.
The absorption and emission of infrared radiation by different
substances are wavelength dependent. This wavelength dependence
results in the so-called atmospheric window (8 ym to 12 jam). In this
region the two major absorbers of infrared radiation, water vapor and
carbon dioxide, do not influence infrared radiation,, Thus, it is
in the atmospheric window that aerosols can most readily affect
infrared radiative transfer. On the basis of their spectral absorption
characteristics, there is reason to expect that aerosols will influence
infrared transfer. For example, clay minerals (Flanigan and Delong,
1970) and silica (Peterson and Weinman, 1969) are natural substances
that have absorption bands in the atmospheric window,, Man-made
substances such as ammonium sulfate (Neumann, 1972) and carbonaceous
materials (Twitty and Weinman, 1971) also have absorption bands in the
window region. For aerosols collected mainly from precipitation
samples, containing natural and man-made substances, Volz (1972a; 1972b)
found the maximum absorption to be in the infrared at 9 ym.
4
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SECTION 2. EXPERIMENTAL DESIGN
INTRODUCTION
The basic experiment consisted of coincident measurements, during
cloud-free conditions, of hemispheric infrared (3 to 50 pm) downward-
directed radiation (HIDR) at the earth's surface, and of measurements of
indicated atmospheric aerosol concentrations from turbidity and nephelometer
readings. At each observation time, the expected HIDR for an aerosol-free
atmosphere was also calculated from surface temperature and dew point or
radiosonde data as appropriate for the calculation scheme. The observed
turbidity and nephelometer readings were then compared to the observed-
minus-calculated irradiances, and thus the interdependence of these
quantities was studied.
RADIATION INSTRUMENTS
To determine the HIDR at the earth's surface, two types of instruments
were used. The net all-wavelength radiation was measured by the Funk (CSIRO)
type net all-wavelength radiometer (Funk, 1959), with the polyethylene dome
from the bottom side replaced by a blackened cavity. The inside temperature
of the cavity was continuously monitored with a thermocouple so that the
instrument effectively measured the unidirectional irradiance. Three such
instruments were used during the course of study„ The downward-directed
solar irradiance was measured independently by an Eppley Precision
Spectral Pyranometer with a WG7 clear-glass dome, transparent from a
wavelength of about 0.3 ym to 3.0 pm. The downward-directed infrared
-------
component was then determined from the following relation:
NET = SWi - SW+ + LWi - TC (2.1)
where NET is the reading from the Funk instrument, Sw> is the downward-
directed solar irradiance from the Eppley instrument, and LWi is the
HIDR to be determined. TC is the upward-directed infrared irradiance
measured by the thermocouple in the blackened cavity (i.e., aT , where a
is the Stefan-Bo!tzman constant and absolute T is the temperature measured
by the thermocouple), and SWi is the upward-directed solar radiation, which
is zero (cavity covering lower sensor). Thus, the HIDR was calculated
as follows:
LW4- = NET - SW+ + TC (2.2)
CALIBRATION OF RADIATION SENSORS
To field check the calibration of the Eppley Pyranometer, the direct
component of the solar beam was shaded with a disc designed to shade out a
solid angle of 5° 43'. The change in output of the pyranometer (millivolts)
was then divided by the output of the Eppley Normal Incidence Pyrheliometer
2 -1
(Cal/cm -min~ ) with a similar aperture.
The sun shade method was also used to calibrate the CSIRO radiometers
(Funk, 1961; and Latimer, 1963). The error associated with the calibration
constant thus determined was on the order of ±5 percent. This value was
estimated to include ±1.5 percent error for non-cosine instrument response*
*An instrument is said to have perfect cosine response if the energy
from the direct solar beam falling on the horizontal sensing surface of
area A is equal to the energy in the area normal to the direct solar beam
multiplied by cos 0. The angle 0 is the solar zenith angle. If the
instrument cosine response is perfect, this relation will hold for all
0 and for all possible solar paths across the sensing surface of the
instrument.
-------
(Funk, 1959), ±1 percent error for reading stripcharts, and a cumulative ±2.5
percent error for the shade technique (such as instrument overshoot and
lag errors (Latimer, 1963), errors in the Pyrheliometer and errors in the
mechanics of shading the instrument). A complete discussion of calibration
techniques and results was presented by Riches (1974).
MEASUREMENT OF AEROSOLS
The atmospheric aerosol content was estimated in two ways: by
measurements with a sunphotometer (Flowers et al0, 1969) and an integrat-
ing nephelometer (Charlson et a!., 1969). Both devices are based on light-
scattering principles, and have an effective wavelength of approximately
0.5 ym for the sunphotometer and the nephelometer. If the ambient aerosol
followed a Junge (1955) size distribution, few particles outside the
range OJ to 1.0 ym in radius would affect the sunphotometer or nephelo-
meter measurements. Even though the most efficient particles for infrared
emission would likely be somewhat larger than this effective size range
because of the longer wavelengths of the emitted energy, these aerosol
monitoring techniques were selected because of their ease of operation and
instantaneous output.
The sunphotometer measures the solar intensity at Oo5 ym to yield the
atmospheric turbidity, or aerosol extinction coefficient, through the relation
T T IA~(T , + T0, + B. ) m /o 0\
I, = I . 10 v rx 3x A (2.3]
A 0 A
where I is the irradiance at wavelength, A, at the observing point; I
A OA
is the extraterrestrial irradiance at wavelength, A; T , is the (known)
r A
scattering coefficient for air molecules; TO, is the (known) absorption
coefficient for ozone; B.. is the turbidity coefficient (to be determined);
A
-------
and m is the optical air mass (path length of the direct solar beam in
the atmosphere) adjusted for atmospheric pressure at the observer's
location. The atmospheric turbidity thus obtained is representative of
the entire vertical extent of the atmosphere above the observer.
The nephelometer measures the aerosol extinction coefficient by
continuously drawing ambient air into a chamber where the extinction
coefficient is determined by light scatter. Thus, this measurement
is representative of very local conditions. Actually, the extinction
coefficient is dependent on scattering and absorption by both gases and
aerosols. At 0.55 vim (the effective wavelength of the nephelometer),
however, aerosol scattering is almost always the dominant factor affecting
the extinction coefficient.
CALCULATION OF THE DOWNWARD-DIRECTED INFRARED IRRADIANCE
There are basically three ways to calculate the expected HIDR. The
first method is to use an empirical equation obtained from a regression
analysis of actual incoming infrared flux measurements. Usually tempera-
ture and water vapor pressure (e.g., Brunt, 1932) or temperature alone
(e.g., Swinbank, 1963) are used as independent variables in the regression
analysis. Temperature is suggested as a variable by the Stefan-Boltzmann
law, which states that a perfect absorber-emitter should emit a radiant
flux proportional to the fourth power of its temperature. The exact relation
B = EaT4 (2.4)
/
where: B is the radiant flux, E is the emissivity, a is the Stefan-
Boltzmann constant, and T is the temperature of the emitter; for a = 0.817
x 10" ly/(min - °K ), where T is in degrees Kelvin. Water vapor pressure
-------
is also important since water vapor is the major absorber and emitter of
atmospheric infrared radiation.
The second and third methods involve the solution of the equations
of radiative transfer either by a chart (e.g., Elsasser, 1942; Yamamoto,
1952) or by computer (e.g., Atwater, 1966). The derivation of the equa-
tions of radiative transfer is available from several sources (e.g.,
Kondratyev, 1969). These methods usually treat absorption by water
vapor, carbon dioxide, and possibly ozone. The vertical distribution
of temperature and water vapor is obtained from radiosonde data, and
carbon dioxide is usually considered well-mixed throughout the entire
atmospheric column. The resultant infrared irradiances are for an
aerosol-free atmosphere.
For this study, six schemes were used to calculate the expected
HIDR, since there is no standard method. Four of the methods were
semi-empirical equations of Swinbank (1963), Idso and Jackson (1969),
Brunt (1932), and Geiger (1965). The equations of Swinbank and of Idso
and Jackson are based on surface temperature only, while those of Brunt
and Geiger have both surface temperature and water vapor pressure as the
independent variables. The original derivations of these four equations
were based on best-fit regression curves to actual infrared measurements
and thus were representative of an atmosphere containing some aerosols.
The equations used with the appropriate constants (Morgan et al., 1971)
are given below:
Geiger: R = EaT4 [a - b • exp (-2.3 ce2)] (2.5)
a = 0.82 b = 0.25 c = 0.094
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Brunt: R = EaT4 (a + b vi^) , (2.6)
a = 0.605 b = 0.048
Swinbank: R = Ea a T6 (2.7)
a = 9.35 x 10"6
Idso and Jackson: R = EaT4 [1 - c • exp (-d (273.16-T)2)] (2.8)
c = 0.261 d = 0.000777
Where:
E = longwave emissivity assumed to be 1.0
a = Stefan-Boltzmann Constant = 0.817 x 10~10ly/(min-°K4)
T = air temperature (°K) at 2 m
e2 = water vapor pressure (mb) at 2 m
The fifth and sixth techniques were based on the equations of
radiative transfer. In one method, the investigators used the algorithms
of Sasamori (1968), which are based on the Yamamoto chart (Yamamoto, 1952).
The algorithms included transmission functions for water vapor, carbon
dioxide, and ozone. With the inclusion of ozone, it was believed that
the Yamamoto chart was the most theoretically correct of the chart
solutions available. This belief was the basis for its selection as one
of the six schemes for calculating the expected HIDR. The final method
involved a computer solution of the radiative transfer equation (Atwater,
1966), based on the water vapor and carbon dioxide transmission functions
of Davis and Viezee (1964). Standard atmospheric distributions of ozone
and carbon dioxide were used as input for these last two computational schemes,
10
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DATA COLLECTION AND SITE LOCATION
The infrared irradiance measurements were made continuously from
April 1972 through August 1973. Data were reduced for this study, however,
only when the sky was cloud-free or when thin cirrus did not extend
beyond 15 degrees above the horizon. Moreover, the data were evaluated
only when turbidity observations were available, which was usually once
an hour, 5 days a week. The nephelometer ran continuously from April
through July 1972 and from October 1972 through August 1973. The
nephelometer data were evaluated only when both turbidity and infrared
irradiance measurements were available. At the observation times, surface
temperature, dew point, and pressure were taken locally or, when local
equipment failed, were estimated from the National Weather Service hourly
observations at Raleigh-Durham airport about 5 miles to the east over
mostly rural countryside. The upper air profiles of pressure, tempera-
ture, and dew point (for input to the fifth and sixth computation schemes)
at Research Triangle Park, North Carolina, were estimated for each observa-
tion time from the 1200Z and OOOOZ radiosondes taken at Greensboro,
North Carolina, which is about 60 miles to the west-northwest and is the
nearest radiosonde station to Research Triangle Park. Since the radiation
measurements were reduced on "clear" days only, at each measurement time the
1200Z radiosonde data were modified in the lower layers by assuming an
adiabatic lapse rate and a constant water vapor mixing ratio through the
mixing layer. The absolute values of these profiles were determined from
observed local surface pressure, temperature, and dew point observations.
Unless otherwise indicated by the synoptic situation, a linear time
11
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interpolation for temperature and dew point was used between the 1200Z
and OOOOZ data above the mixing height.
The platform from which the radiation measurements were made is
approximately 23 feet above a mowed grass surface in a nonurban environ-
ment. There are few obstructions (trees only) above 5 degrees from
the horizon, and there are no obstructions above 10 degrees from the
horizon. The temperature, dew point, pressure, nephelometer, and
turbidity observations were made 200 feet west of the platform from
which the radiation measurements were taken.
12
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SECTION 3. RESULTS
OBSERVED-MINUS-CALCULATED IRRADIANCE AS A FUNCTION
OF NEPHELOMETER READINGS AND TURBIDITY
The observed-nit nus-calculated irradiance was analyzed as a function
of both the sunphotometer- and nephelometer-indicated atmospheric aerosol
content for all data collected from April 1972 through August 1973.
There were 296 data points for the turbidity comparisons and 231 data
points for the nephelometer comparisons. The data were stratified three
ways for each type of aerosol measurement. First, all data were considered.
Then the data were printed out chronologically by the computer, along with
the reported sky condition at the time of observation. The May-through-
September period for both 1972 and 1973 contained the majority of reported
hazy sky conditions. On this basis, May through September was designated
the "hazy" season (177 data points) and October through April was designated
the "clean" season (119 data points). For each of the three stratifications
and the six calculation schemes, the observed-minus-calculated irradiance
was compared to the surface relative humidity, turbidity, and nephelometer
readings.
Before the results were analyzed in detail, a cursory study was made
of the interrelationship between the values of the infrared irradiance
determined by the six empirical or theoretical schemes and the observed
data. Infrared irradiance determined by the two formulae based only on
surface temperature data (Swinbank and Idso) showed large diurnal variation
and highest values during midday. This problem of excessive daytime
13
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estimates has been discussed previously by Paltridge (1970). In general,
the computations from the two equations based on surface temperature and
vapor pressure and the two techniques using upper-air data were consistent.
All Data
Considering all data, investigators found that positive correlations
resulted between nephelometer readings and all six schemes of observed-
minus-calculated irradiances. This fact is exemplified by the scatter
diagram shown in Figure 3.1, which is a plot of the observed-minus-calculated
irradiance versus nephelometer readings. The calculated values were obtained
from the Yamamoto chart.
Two points should be made about the data presented in Figure 3.1.
First, 80 percent of the measured extinction coefficients are less than
0.2 per kilometer.* This bias results largely from the nonurban
environment of the study site. Second, the data show considerable
scatter about the regression line. This scatter indicates that the
nephelometer readings are not necessarily a reliable measure of the
extinction coefficient for infrared radiation. Since nephelometer read-
ings are a measure of the light scatter or extinction coefficient in
the visible range, it is possible for the particle size distribution
to include excess large (small) particles that significantly affect the
infrared (visible) but not the visible (infrared) radiation.
*With the use of the nephelometer factory calibrations, an extinction
coefficient of 0.2 knH is equivalent to a visibility of 23 km and a
mass loading of 87 yg m~3.
14
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M M I-H II 1^1 I I 1^1 I l
'(Qiivnn3iv3-a3Auasao)
15
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Other contributions to the scatter shown in Figure 3.1 (and all other
diagrams presented herein) are made from several sources. The accuracy
of the instruments and the errors introduced during the reduction of the
data from these instruments contribute through Equation 2.1. The
calculated HIDRs are in error to the extent that the meteorological input
data are in error. The formulae themselves are also a source of scatter
since they are not perfect predictors of the irradiance, as indicated by
the fact there is no standard method for calculating the expected downward
irradiance. Lastly, the aerosol measurement techniques contain instru-
ment and data reduction errors that can also contribute to the scatter.
A third point of interest in Figure 3,1 is the negative intercept
of the regression line. In Table 3.1 the regression analysis shows that
all but the Atwater program have negative intercepts for the linear
regression. The negative intercepts are the largest for the two
equations having temperature as the sole independent variable (Swinbank
and Idso formulae). Next largest are for Brunt and Geiger formulae,
which have temperature and water vapor for independent variables. The
intercepts for the theoretical schemes are the smallest, with the regres-
sion for the Yamamoto chart being negative and the Atwater scheme being
positive. Two possible explanations for the negative intercepts follow.
First, for summer afternoons, Paltridge (1970) proposed a negative
correction of Oo043 ly/min for the Swinbank formula, based on comparisons
between that formula and observed data. Paltridge's hypothesis was that
the correction is needed because the original formula was obtained from
measurements made at night. During daytime, with low-level temperature
lapse conditions, the surface temperature is an overestimate of the
16
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Table 3-1. REGRESSION ANALYSIS AND ANALYSIS OF VARIANCE FOR OBSERVED-MINUS-
CALCULATED IRRADIANCES (ly/min) VERSUS NEPHELOMETER READINGS
(I/km x 10) AND TURBIDITY (I/optical air mass) FOR ALL
DATA AND FOR CLEAN AND HAZY SEASONS
Scheme
Irradiance
Idso
Swinbank
Brunt
Geiger
Yamamoto
Atwater
Irradiance
Idso
Swinbank
Brunt
Geiger
Yamamoto
Atwater
Irradiance
Idso
Swinbank
Brunt
Geiger
Yamamoto
Atwater
Irradiance
Idso
Swinbank
Brunt
Geiger
Yamamoto
Atwater
Slope
vs. neph
0.0179
0.0105
0.0058
0.0089
0.0079
0.0048
vs. turb
0.0566
0.0508
0.0057
0.0552
0.0498
0.0212
Intercept
jlometer fo
-0.0676
-0.0614
-0.0384
-0.0528
-0.0283
0.0030
Std error
of slope
r all data
0.0381
0.0385
0.0331
0.0340
0.0309
0.0272
idity for all data
-0.0583
-0.0516
-0.0311
-0.0466
-0.0230
0.0067
vs. nephelometer fo
0.0296
0.0353
0.0122
0.0145
0.0135
-0.0010
vs. turb
-0.2664
-0.3181
-0.3270
-0.3762
-0.3165
-0.3329
-0.0664
-0.0639
-0.0255
-0.0403
-0.0196
0.0178
0.5722
0.5768
0.4982
0.5147
0.4705
0.4169
r clean sea
0.1890
0.1943
0.1631
0.1735
0.1536
0.1440
idity for clean season
-0.0190
-0.0077
0.0150
0.0013
0.0162
0.0422
1 . 5440
1.6020
1.2947
1.3779
1.2073
1.0893
Correlation
coefficient
0.27
0.26
0.17
0.25
0.25
0.17
0.10
0.09
0.01
0.11
0.11
0.05
son
0.15
0.18
0.07
0.08
0.09
0.01
0.17
0.19
0.24
0.26
0.25
0.29
Std error of
estimate
0.0646
0.0653
0.0561
0.0577
0.0523
0.0462
0.0745
0.0751
0.0648
0.0670
0.0612
0.0543
0.0548
0.0563
0.0473
0.0503
0.0445
0.0417
0.0559
0.0580
0.0469
0.0499
0.0437
0.0395
F test
18. 4a
17. Oa
6.9b
15. 7a
14. 8a
7.lb
2.9C
2.3
<1
3.4C
3.3C
<1
2.6
3.5C
<1
<1
-------
Table 3-1.(continued). REGRESSION ANALYSIS AND ANALYSIS OF VARIANCE FOR
OBSERVED-MINUS-CALCULATED IRRADIANCES (ly/min) VERSUS NEPHELOMETER
READINGS (I/km x 10) AND TURBIDITY (1/optical air mass) FOR ALL
DATA AND FOR CLEAN AND HAZY SEASONS
Scheme
Irradiance
Idso
Swinbank
Brunt
Geiger
Yamamoto
Atwater
Slope
vs. neph
0.0154
0.0154
0.0110
0.0138
0.0116
0.0078
Intercept
jlometer fo
-0.0926
-0.0882
-0.0660
-0.0788
-0.0484
-0.0123
Std error
of slope
r hazy seas
0.0328
0.0325
0.0276
0.0266
0.0266
0.0230
Irradiance vs. turbidity for hazy season
Idso
Swinbank
Brunt
Geiger
Yamamoto
Atwater
0.1464
0.1490
0.1038
0.1535
0.1263
0.0838
-0.0879
-0.0839
-0.0636
-0.0785
-0.0476
-0.0131
0.5814
0.5762
0.5004
0.5128
0.4823
0.4269
Correlation
coefficient
Dn
0.42
0.43
0.37
0.44
0.40
0.32
0.24
0.25
0.20
0.29
0.25
0.19
Std error of
estimate
0.0683
0.0678
0.0576
0.0590
0.0554
0.0480
0.0818
0.0811
0.0704
0.0721
0.0679
0.0601
F test
26. 9a
27. 3a
19. 2a
29. la
23. 4a
14. Oa
ll.la
11. 7a
7.5^
15. 7a
12. Oa
6.7b
Significant at 99.5 percent confidence level.
Significant at 95 percent confidence level.
GSignificant at 90 percent confidence level.
effective radiating temperature of the atmosphere. He also suggests
applying the correction to other formulae calibrated with night measure-
ments of infrared radiation. Although this correction can vary with time
of day and with season, the magnitude of the proposed correction is large
enough to decrease significantly or change the sign on the intercepts
presented.
The second possible explanation stems from the empirical nature of
the first four computational schemes. Since they were derived from
HIDR observations, these schemes represent an atmosphere in which the
18
-------
aerosol content is not zero, but some higher value. Thus, one would not
expect the observed-minus-calculated differences to be zero at zero aerosol
concentration, but at some higher value. In other words, a negative
intercept for the empirical schemes is realistic if aerosols do, in fact,
significantly alter the HIDR.
The slopes, or coefficients of the independent variable, for all
six linear regressions presented in Table 3.1 (irradiance vs. nephelometer
for all data) are positive and statistically significant at least at the
95 percent confidence level as determined by a standard F-test. Four
of the schemes (Idso, Swinbank, Geiger, and Yamamoto) are significant at
the 99.5 percent confidence level. This fact suggests an excess of HIDR
over that expected for an aerosol-free atmosphere., The correlation
coefficients range from 0.17 for the Atwater and Brunt schemes to 0.27 for
the Idso scheme. Note also that in each case the standard error of the slope
and the standard error of estimate are large. This is a reflection of the
scatter of the data, as pointed out earlier in the discussion.
Similar regression statistics for the observed-minus-calculated
irradiances as a function of turbidity are presented in Table 3.1
(irradiance vs. turbidity'for all data). The positive slopes of the
regression lines are significantly different from zero at the 90 percent
level for only three of the schemes, which are the formulae of Idso and
Geiger and the Yamamoto chart. Moreover, all the schemes have low correla-
tion coefficients and large standard errors of slope. The large scatter
results in part from the fact that turbidity, like the nephelometer
scattering coefficient, is not necessarily a reliable measure of the
extinction coefficient in the infrared and in part from the calculation
19
-------
and observational errors discussed previously. The negative intercepts
(Table 3.1 - irradiance vs. turbidity for all data) again suggest an over-
estimate for the calculated daytime HIDR.
Another reason for the large data scatter is that turbidity is a measure
of the extinction in the entire atmospheric column, whereas the nephelometer
readings represent the local ground-level extinction coefficient. Since
about 75 percent of the HIDR typically originates in the lowest 400 meters of
the atmosphere (Sellers, 1965), nephelometer readings might be expected to
be better correlated to the excess irradiance than turbidity is.
Clean Season Data
Data for the clean season (October through April) were generally
inconclusive. The regression analyses for the observed-minus-calculated
irradiances as a function of extinction coefficient and turbidity are
presented in Table 3.1 (irradiance vs. nephelometer for the clean season
and irradiance vs. turbidity for the clean season, respectively). The
data had a strong bias toward low values of turbidity and extinction
coefficient and large scatter about the regression lines. The generally
poor results were likely because the data clustered about low aerosol
values resulting from the generally clean, dry atmospheric conditions.
Moreover, the large scatter also resulted from (1) the use of turbidity
and the extinction coefficient in the visible range to represent the
infrared extinction coefficient; (2) the fact that turbidity is a weak
function of the lower atmospheric layer responsible for the majority of
the HIDR, as discussed earlier; and (3) the observational and calculation
errors.
20
-------
Hazy Season Data
The regression analysis and the analysis of variance for the
observed-calculated irradiance as a function of nephelometer readings
for the hazy season (May through September) are presented in Table 3.1
(irradiance vs. nephelometer for the hazy season). Note that the positive
slopes for all six schemes are significantly different from zero at the
99.5 percent confidence level and that the standard error of the slope
is smaller than that for the slope in the cases discussed previously.
Correlation coefficients are about three times larger than those obtained
for the clean season data, and twice that obtained for the total data
set. The more significant statistical results from the hazy season suggest
that the narrow range of aerosol concentrations during the clean period
was not sufficient to elucidate an aerosol-irradiance relation.
The intercept for all six regression equations is negative (see
Table 3.1 - irradiance vs. nephelometer for the hazy season). As discussed
previously, this fact is not unrealistic for the empirical schemes, but
could be because of an overestimate of the infrared irradiance by the
schemes for daytime observations. The scatter of the data about the
regression as shown in Figure 3.2 is also large for these hazy-season
measurements.
In contrast to the statistics presented for the clean season, the
statistics for the irradiance differences as a function of turbidity
during the hazy months (Table 3.1 - irradiance vs. turbidity for the hazy
season) show a positive slope that is significantly different from zero
at least at the 95 percent confidence level for all six schemes„ The
standard error of the slope is the smallest yet found for turbidity
regressions. The wider and more evenly distributed range of turbidity
21
-------
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values (see example scatter diagram in Figure 3.3) allows more physical
significance to be given to the positive slopes found for the hazy
season, even though considerable scatter about the regression line is
still present. Similar to the regression on nephelometer readings for
the hazy season, the correlation coefficients are about twice as large
as those found for the total data set, averaging about 0.24. They are
smaller, however, than those found for the nephelometer readings. These
facts show that turbidity is more indicative of the lower-atmospheric
aerosol concentration during the hazy season than during the clean season,
but that it is not as reliable a measure as the nephelometer readings.
The wider and more evenly distributed range of turbidity values and
nephelometer readings found for the hazy-season data allow a discussion
of the physical meaning of the regression equations derived earlier. If
the six results of the observed-minus-calculated irradiance are used as
0.2
a
UJ
0.1
cc
LLJ
<
a
<
0.1
0.2
0.3 0.4 0.5
TURBIDITY, I/optical air mass
0.6
0.7
0.8
Figure 3-3. Scatter diagram for observed-minus-calculated irradiance versus
turbidity for the hazy season (calculated irradiance from the Yamamoto chart).
23
-------
a function of nephelometer readings (extinction coefficient [knf ])
during the hazy season, the average slope of the regression line is
0.125 (ly/km-min). Thus, the results presented herein indicate that
an ambient low-level atmospheric aerosol concentration with extinction
coefficient of 0.1 km would increase the HIDR by 0.0125 ly/min. For a
typical summer afternoon with an extinction coefficient of 0.2 km" , the
excess irradiance is 0.025 ly/min. The average slope from the six schemes
for the data of all seasons (with nephelometer readings considered) is
0.093 ly/km-min.
The average slope of the linear regression line between the irradiance
difference during the hazy season and the turbidity coefficient is 0.127
ly/optical air mass-min. Thus, for a typical summer afternoon in North
Carolina with a turbidity of 0.250, which is similar to a nephelometer
extinction coefficient of 0.2 km" , the excess downward irradiance would
be 0.032 ly/min. This value (0.032 ly/min) represents about 6 percent
of the typical HIDR total.
RELATIVE HUMIDITY RESULTS
It has been established that relative humidity is an important
parameter in aerosol growth. For example, Covert et al. (1972) and
Winkler (1973) have shown that ambient aerosols grow as relative humidity
increases. Since the absorption coefficient for aerosols depends
directly on its radius (Deirmendjian, 1969), the increased particle size
should increase the particle absorption (or emission) in the infrared.
The adsorbed or absorbed water should also increase the particle absorp-
tion (or emission) since water is an effective absorber in the infrared
(Kondratyev, 1969). Thus, relative humidity should correlate with the
observed-minus-calculated HIDR. Since scatter by aerosols is proportional
24
-------
to particle size (Kondratyev, 1969), nephelometer readings and turbidity
should also correlate with the surface relative humidity. To test these
hypotheses, the observed-minus-calculated HIDR, turbidity, and nephelometer
data were analyzed by least-square linear regression as a function of
surface relative humidity.
In Table 3.2, the regression analyses and analyses of variance are pre--
sented for all data, and for clean and hazy stratifications for nephelometer
readings and turbidity as a function of relative humidity. The results
are as suggested above. The nephelometer readings are positively
correlated with relative humidity for all three stratifications, with
correlation coefficients of about 0.55; and the positive slopes are
statistically significantly different from zero at the 99.5 percent
confidence level, with relatively small standard error of the slopes.
Note that the slopes for the clean season are about four times larger
than those for the hazy season. This fact may be the result of the bias
toward low nephelometer readings during the clean months as discussed
previously. This large change in slope is most likely not physically
significant. The results for the "hazy" data set are similar to those
obtained for all the data.
The results for turbidity as a function of relative humidity are
similar to those presented for nephelometer readings. The slopes are
positive and significantly different from zero, at least at the 95 percent
confidence level; but the correlation coefficients are lower, and the
standard errors of slope are larger than the corresponding statistics
for the nephelometer readings. Again, the slope for the clean season
differs from those obtained for the two other stratifications, and this
difference is attributed to the bias toward low turbidity during the
clean season.
25
-------
Table 3-2. REGRESSION ANALYSIS AND ANALYSIS OF VARIANCE FOR NEPHELOMETER
READINGS (I/km x 10) AND TURBIDITY (I/optical air mass)
VERSUS RELATIVE HUMIDITY (percent) FOR THE THREE STRATIFICATIONS
Scheme
Nephelometer
All data
Clean season
Hazy season
Turbidity
All data
Clean season
Hazy season
Slope
5.81
28.90
4.54
59.29
97.45
42.73
Intercept
44.6
22.3
50.2
47.4
41.5
53.0
Std error
of slope
8.78
43.55
7.08
125.5
458.0
111.6
Correlation
coefficient
0.55
0.55
0.54
0.43
0.21
0.36
Std error of
estimate
14.9
12.6
14.8
16.3
16.6
15.7
F test
100.3a
46. 2a
50. 3a
65. 6a
5.3b
25. 6a
Significant at 99.5 percent confidence level.
^Significant at 95 percent confidence level.
The regression analyses and analyses of variance for the observed-
minus-calculated HIDR as a function of relative humidity for all six
schemes are presented in Table 3.3 for the total data set, clean season,
dnd hazy season, respectively. All 18 slopes are positive and
significantly different from zero at the 99.5 percent confidence level.
The correlation coefficients are highest for the Idso and Swinbank
schemes and lowest for the Atwater scheme, with the other three schemes
in the middle. Since Idso and Swinbank methods do not contain water
< r/or as an independent variable, the higher correlations may reflect a
need to include water vapor as a parameter, rather than a better correlation
with particle growth. The seasonal (clean versus hazy) difference in
correlation coefficients is the result of the dryer air dominating the
clean season. In other words, during the clean season, the absolute
26
-------
humidity is less than that found during the hazy season, which is fre-
quently influenced by a warm, moist air mass of maritime origin. Thus,
high relative humidities during the clean season may not be associated
with enough water vapor for significant particle growth.
Table 3-3. REGRESSION ANALYSIS AND ANALYSIS OF VARIANCE FOR OBSERVED-
MINUS-CALCULATED IRRADIANCES (ly/min) VERSUS RELATIVE HUMIDITY
(percent) FOR ALL DATA AND FOR CLEAN AND HAZY SEASONS
Scheme
All data
Idso
Swinbank
Brunt
Geiger
Yamamoto
Atwater
Clean seas
Idso
Swinbank
Brunt
Geiger
Yamamoto
Atwater
Hazy seaso
Idso
Swinbank
Brunt
Geiger
Yamamoto
Atwater
Slope
0.0025
0.0025
0.0017
0.0020
0.0017
0.0012
Dn
0.0019
0.0020
0.0012
0.0012
0.0010
0.0006
1
0.0039
0.0039
0.0029
0.0033
0.0030
0.0022
Intercept
-0.1922
-0.1863
-0.1240
-0.1485
-0.1139
-0.0599
-0.1319
-0.1300
-0.0720
-0.0856
-0.0547
-0.0132
-0.3014
-0.2953
-0.2240
-0.2534
-0.2062
-0.1331
Std error
of slope
0.0033
0.0033
0.0032
0.0032
0.0029
0.0027
0.0027
0.0028
0.0026
0.0028
0.0025
0.0023
0.0031
0.0031
0.0031
0.0030
0.0029
0.0029
Correlation
coefficient
0.61
0.60
0.46
0.52
0.51
0.41
0.57
0.59
0.43
0.40
0.37
0.27
0.79
0.79
0.69
0.75
0.71
0.61
Std error of
estimate
0.0594
0.0601
0.0575
0.0574
0.0529
0.0495
0.0464
0.0480
0.0438
0.0474
0.0420
0.0398
0.0521
0.0517
0.0522
0.0501
0.0490
0.0485
F test9
173.5
169.1
79.8
111.2
104.4
59.9
58.1
61.2
25.8
22.6
18.7
8.9
283.5
284.6
157.4
220.5
184.4
104.0
aAll data in this column significant at 99.5 percent confidence level.
27
-------
Secondly, notice the large negative intercept for all 18 cases.
This can be accounted for in part by the tendency of the calculation
schemes to overestimate the irradiance for daytime observations.
The negative intercept may also result from the mechanics of particle
growth. Covert et al . (1972) show that particle growth for relative
humidities below 60 percent is very small. For relative humidities
greater than 60 percent, particle growth increases very raoidly with
relative humidity. Figure 3.4 shows the positive corre'iati! i between
observed-minus-calculated irradiance and relative hum'uity, which
presumably results from aerosol growth dependence on relative humidity.
Because the aerosol growth is strongly nonlinearly dependent on humidity,
however, a nonlinear curve should be used to fit the data of Figure 3.4.
These data do suggest such a curve with wide scatter and little apparent
slope at relative humidities less than about 60 percent.
0.24-
c
1
"
0.14-
>
cc
CO
00
cc
cc
iiNiiHniMiiiiiiiiintMnniiiiiiiiiiiHlniiiiuiMiiiiitMiiiMiiiiniiiiuiiiiiiniiiinHMiHiiiiniiniiniiiiMiuiiiiiiiiiitiiiiiiiiiiiiiiiiiniiiiMiiiiiiiiiitiitiiiiiiiiii
0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0
RELATIVE HUMIDITY, percent
Figure 3-4. Scatter diagram for observed-minus-calculated irradiance versus relative humidity
for all data (calculated irradiance from the Yamamoto chart).
28
-------
SECTION 4. DISCUSSION
Analysis of the data suggests that for a nonurban atmosphere,
aerosols can contribute a significant excess of hemispheric infrared
downward-directed radiation, particularly in a hazy atmosphere. The
results of averaging all six computational schemes together indicated
that 0.013 ly/min is produced for each 10th of turbidity, and 0.0125
ly/min is produced for an aerosol concentration equivalent to a nephelo-
meter-indicated extinction coefficient of 0.1 km" . For a typical
summer day with 0.250 turbidity and 0.2 km" extinction coefficient, the
excess irradiance suggested is 0.033 and 0.025, respectively, or approxim-
ately 0.03 ly/min on the average. During several summer days in St. Louis,
Missouri, rviciaun ana Mowers (1974) measured urban-rural differences in
turbidity of about 0.05, which corresponds to a 0.007 ly/min urban-rural
difference in down-welling infrared irradiance. In Los Angeles, however,
with considerably higher urban pollution concentrations, they measured
turbidity differences through the lowest 1700 meters of the atmosphere
of more than 0.2, which corresponds to 0.026 ly/min of excess irradiance.
For the significance of these results to be interpreted in terms
of surface energy budgets, they have to be put in proper perspective.
During midday in summer, with cloudless conditions in the central U.S.,
for example, the incident solar irradiance would approximate 1.3 ly/min.
Obviously, an excess infrared irradiance of up to 0.03 ly/min would have
minor significance during these hours. During nighttime, however, a much
different picture emerges. Then, with no solar component, the net radiative
29
-------
flux at the surface would typically be about -0.1 ly/min (a negative value
means a net flux away- from the earth.}. An excess infrared flux resulting from
aerosols of 0.03, or even 0.007, ly/min now becomes an important factor.
In terms of a daily radiative energy budget, the following example
for the central U. S. in July is illuminating. The average daily
receipt of solar radiation is about 600 ly/day. With an albedo of
15 percent, 510 ly/day are absorbed by the ground. A typical net
infrared flux of -0.1 ly/min (144 ly/day} would yield a net all-wave
radiation receipt of 366 ly/day. The data from this study suggest that
an aerosol loading equivalent to a turbidity of 0.2 would cause an
additional downward infrared flux of 37 ly/day. A turbidity of 0.05
corresponds to 9 ly/day. Thus, for a clean atmosphere or small urban-
rural turbidity difference, on a daily basis, the excess infrared flux
would have minor significance. In contrast, higher turbidities, as in
the example above, could alter the net radiative flux by some 10 percent.
The data presented herein showed a statistically significant
correlation between relative humidity and atmospheric aerosol concentra-
tions as indicated by nephelometer and sunphotometer. This finding presumably
reflected the effect of humidity on aerosol growth, especially at higher
Humidities. The data also showed a significant correlation between
observed-calculated HIDR and relative humidity. A nonlinear dependence
was suggested by the results. Large scatter and little trend were
evident at humidities less than 60 percent; at higher humidities, the
excess HIDR increased noticeably as a function of humidity. Thus, relative
numidity could also be used as an indicator of excess infrared irradiance.
30
-------
It is possible, however, that the equations used to calculate the HIDR
underestimate the flux at high humidities and the derived relation:;
should be checked further during these conditions.
In summary, the results of this study do indicate that atmospheric
aerosols can measurably influence downward-directed infrared radiation.
Thus, their infrared radiative effects should be included in certain
surface energy budget studies, especially in areas with high aerosol
concentrations.
31
-------
5. LIST OF REFERENCES
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and on dust in the air. Geograph. Ann., Vol. 11, 156-166.
Atwater, M. A., 1966: Comparison of numerical methods for computing
radiative temperature changes in the atmospheric boundary layer.
J. Appl. Meteor., Vol. 5, No. 6, 824-831.
Brunt, D., 1932: Notes on radiation in the atmosphere. Quart. J. Roy.
Meteor. Soc., Vol. 58, No. 247, 389-420.
Charlson, R. J., Ahlquist, N. C., Selvidge, H., and MacCready, P. B.,
1969: Monitoring of atmospheric aerosol parameters with the
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Covert, D. S., Charlson, R. J., and Ahlquist, N. C., 1972: A study of
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Davis, P. A., and W. Viezee, 1964: A model for computing infrared
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J. Geophys. Res., Vol. 69, No. 18, 3785-3794.
Deirmendjian, D., 1969: Electromagnetic Scattering on Spherical
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Elsasser, U. M., 1942: Heat Transfer by Infrared Radiation in the
Atmosphere. Harvard Meteor. Studies, No. 6, Harvard Univ. Press,
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Flanigan, D. R., and DeLong, H. P., 1970: Spectral absorption
characteristics of the major components of dust clouds. Edgewood
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Flowers, E. C., McCormick, R. A., and Kurfis, K. R., 1969: Atmospheric
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Flowers, E. C., and Viebrock, H. J., 1965: Solar radiation: an anomalous
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32
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Funk, J. P., 1961: A note on the long-wave calibration of convectively
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Geiger, R., 1965: The climate near the ground. Harvard Univ. Press,
Cambridge, Mass.
Hovis, to. A., Blaine, L. R., and Callahan, W. R., 1968: Infrared
aircraft spectra over desert terrain 8.5 p to 16 p. Appl. Optics,
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Idso, S. B., 1972a: Radiation fluxes during a dust storm. Weather,
Vol. 27, No. 5, 204-208.
Idso, S. B., 1972b: Solar radiation measurements augment air pollution
studies. J. APCA., Vol. 22, No. 5, 364-368.
Idso, S. B., 1973: Thermal radiation from a tropospheric dust suspen-
sion. Nature, Vol. 241, No. 5390, 448-449.
Idso, S. B., and Jackson, R. D., 1969: Thermal radiation from the
atmosphere. J. Geophys. Res., Vol. 74, No. 23, 5397-5403.
Junge, C., 1955: The size distribution and aging of natural aerosols
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35
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing]
1. REPORT NO.
EPA-650/4--75-017
3. RECIPIENT'S >CCESSIOWNO.
4. TITLE AND SUBTITLE
Effects of Atmospheric Aerosols on Infrared
Irradiance at the Earth's Surface in a
Nonurban EnvironrrBnt
5. REPORT DATE
May 1975
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
8. PERFORMING ORGANIZATION REPORT NO.
M.R. Riches, J.T. Peterson, and E.G. Flowers
9. P£RFORMING ORGANIZATION NAME AND ADDRESS
Meteorology Laboratory
Environmental Protection Agency
Research Triangle Park, N.C. 27711
10. PROGRAM ELEMENT NO. 1AA009
ROAP No. 26AAS
11. CONTRACT/GRANT NO.
12. SPONSORING AGENCY NAME AND ADDRESS
13. TYPE OF REPORT AND PERIOD COVERED
Same
14. SPONSORING AGENCY CODE
15. SUPPLEMENTARY NOTES
16. ABSTRACT
This report describes a study designed to measure hemispheric infrared
downward-directed irradiance at the earth's surface and ambient aerosol
concentrations at Research Triangle Park, North Carolina. A Funk-type
net radiometer (with a blackened cavity on the underside) was used to
measure the incident all-wave energy. From the value obtained, the observed
solar radiation was subtracted to determine the infrared component. The ex-
pected incident infrared irradiance was calculated from prevailing atmospheric
conditions. Six methods were used for these calculations: four empirical
equations based on surface conditions, the Yamamoto chart, and a radiative
transfer program using vertical profiles of temperature and moisture. The
observed-minus-calculated downwelling irradiances were then compared to
concurrent measurements of turbidity obtained with a Volz sunphotometer,
nephelometer-indicated atmospheric extinction coefficient, and relative humidity.
These measurements were analyzed by least-squares regression to determine
the extent to which incident hemispheric infrared radiation is affected by vary-
ing amounts of atmospheric aerosols and relative humidity.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.IDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
Infrared irradiance
aerosols
turbidity
solar radiation
atmospheric extinction coefficient
relative humidity
13. DISTRIBUTION STATEMENT
Release unlimited
19. SECURITY CLASS (ThisReport)
none
21. NO. OF PAGES
42
20. SECURITY CLASS (This page)
none
22. PRICE
EPA Form 2220-1 (9-73)
36
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