Indian Journal of Radio & Space Physics
Vol 40, April 2011, pp 85-94
Atmospheric turbidity over a continental station Mysore, India
K E Ganesh1,$,*, T K Umesh2 & B Narasimhamurthy2
1
PES Institute of Technology, Department of S&H, BSK III Stage, Bengaluru 560 085, India
Department of Studies in Physics, University of Mysore, Manasagangotri, Mysore 570 006, India
$
E-mail: [email protected], [email protected]
2
Received 26 July 2010; revised 15 February 2011; accepted 21 February 2011
The solar radiation is collected at five optical channels at a continental station Mysore, India during December 2003 June 2006 using a portable sunphotometer MICROTOPS II. Angstrom’s turbidity parameters, α and β, have been calculated
and analysed on daily, monthly, seasonal and annual basis. The observations show that α and β vary throughout on
individual day because of changes in the atmospheric meteorological parameters. It is observed that β is highest during
summer and lowest during winter. An anti-correlation between α and β is observed throughout the day during all seasons
indicating continuous redistribution of fine and coarse particles under the influence of meteorological parameters. The
yearly comparison of α and β along with the atmospheric visibility is also analysed and presented in the paper.
Keywords: Atmospheric aerosol, Atmospheric water vapour, Aerosol optical thickness, Atmospheric turbidity
PACS No.: 92.60.Mt
1 Introduction
The solar radiation gets attenuated while passing
through the earth‘s atmosphere. The radiation
extinction is mainly due to scattering by air molecules
and aerosol particles. On clear sky days, aerosols are
the atmospheric constituents that dominate the
attenuation of solar radiation at visible and nearinfrared wavelengths1. The scattering of solar
radiation by matter other than dry air molecules is
referred as turbidity of the atmosphere2. The
atmospheric turbidity is a measure of total vertically
integrated particulate load in the atmosphere.
Turbidity is a dimensionless quantity, which gives the
measure of opacity of a vertical column of the
atmosphere3. Atmospheric turbidity is an important
factor influencing the energetic of solar radiation in
the earth’s atmosphere. It is a useful index of
atmospheric pollution particularly in studies of longterm changes in the composition of the atmosphere
and the resultant global climatic changes. The
seasonal variation of Angstroms parameters with
atmospheric water vapour and temperature along with
the causes for higher values of β during summer and
lower values during other seasons have been
discussed in the present study Despite of its
importance, there is no universally adopted definition
of turbidity or universally accepted technique for its
measurement. Some indices of turbidity were
proposed and several methods were developed to
determine their values4-7. However, Angstrom’s
turbidity parameters have been widely adopted4.
Mysore (12°19ʹN and 76°39ʹE), a low latitude
station, is situated at an altitude of 767 m above the
sea level on the Deccan plateau of peninsular India.
About 300-500 km away in the East, West and South
of Mysore, there is water spreads of Bay of Bengal,
Arabian Sea and Indian Ocean, respectively. There is
the landmass of Asiatic continent in the North. The
geographic climate of Mysore is moderate. The
monsoon rains, accounting for 73% of the average
annual rainfall of 760 mm, are received during June –
November every year. Following the rainy season, the
winter prevails during December - February8. During
winter, the temperature is low and the rainfall is about
3%. An overall temperature lies in the range of 1836°C throughout the year. The hot months during
March - May constitute the summer and account for
24% rainfall9.
2 Methodology
2.1 Data collection
In order to collect solar radiation, a hand-held
multi-band
sunphotometer
MICROTOPS
II
10
developed by Solar Light Company, USA has been
86
INDIAN J RADIO & SPACE PHYS, APRIL 2011
used. The instrument is equipped with five accurately
aligned optical collimators with a full field view of
2.5°. The internal baffles are also integrated into the
device to eliminate internal reflections. Each channel
is fitted with a narrow band interference filter and a
photodiode suitable for the particular wavelength
range. The MICROTOPS II, used in the present study,
has optical filters transmitting the radiation centered
at wavelengths of 440, 500, 675, 936 and 1020 nm.
Aerosol optical thickness (AOT) was determined by
Langley method assuming the validity of the
Bouguer-Lambert-Beer law. The optical depth due to
Rayleigh scattering has been subtracted from the total
optical depth to obtain AOT. Initially, several
MICROTOPS II settings have been made with the
help of GPS receiver, which include universal date
and time, geographic coordinates, altitude and
atmospheric pressure of the measurement site.
For the observations, the MICROTOPS II was
mounted on a tripod in order to minimize the Sun
targeting error and was stationed on the open terrace
of a two-storey building located in an unpolluted area.
The measurements have been made on the days of
clear sky, viz. when there are no clouds. The data has
been collected during 04:30:00 - 11:00:00 hrs UT at
15 min interval.
0.5. The wavelength, λ, is in micrometers. It is called
turbidity because of scattering of solar radiation by
matter other than dry air molecules of the atmosphere
in the optical sense. Consequently, τpλ includes
attenuation due to dry as well as wet dust particles.
The wavelength exponent, α, is related to the size
distribution of the aerosol particles. Large values of α
indicate a relatively high ratio of small particles to
large particles. It appears obvious that α varies in the
range 0-4 when the aerosol particles are very small, of
the order of air molecules; α should approach 4 and it
should approach zero for very large particles2.
The wavelength exponent, α, is not a constant of
nature but simply a parameter defined by the arbitrary
procedure chosen for its determination12. Generally, α
has a value between 0.5 and 2.5; a value of 1.3 is
commonly employed, since it was originally
suggested by Angstrom22. A good average value of α
for most natural atmospheres is 1.3 ± 0.5.
However, this assumption has a rather limited
validity since many researchers have found that α is a
variable13-16. Throughout an individual day, changes
in temperature cause evaporation or condensation of
moisture in the atmosphere. The aerosols get affected
both in number and size. Consequently, there is a
variation in the values of turbidity parameters2.
2.2 Calibration
2.4 Procedure for determining the values of Angstrom
turbidity parameters, α and β
The instrument was calibrated at regular intervals.
The degradation of the filters or the drift in the
calibration values has been found to be marginal.
Using the standard Langley technique, the instrument
was calibrated atop Sri Chamundeshwari Hills, which
is at about 300 m from the ground level. The
calibration constants obtained from the data collected
atop the hill did not show large variations from the
values obtained from the calibrations at factory11.
2.3 Angstrom turbidity formula
Angstrom suggested an empirical formula by
considering the attenuation effects of scattering and
absorption by aerosols. According to his formulation,
the aerosol optical thickness (τpλ) is related to
wavelength (λ) through the equation
τpλ = βλ-α
… (1)
where, α and β, are known as Angstrom parameters.
The index, α, is the wavelength exponent; and β,
turbidity coefficient representing the amount of
aerosols present in the atmosphere in the vertical
direction. The value of β generally varies from 0.0 to
The Angstrom’s equation [Eq. (1)] upon linearizing
on a logarithmic format takes the form as17-20
ln τλ = ln β - α ln λ
… (2)
This method yields the values of α and β by least
squares analysis of spectral optical thickness. The
values of α and β determined by this procedure are
most adequate17.
In the present investigation, the Angstrom turbidity
parameters have been determined for each scan. The
precision limit of 90-98% in the correlation
coefficient is set to estimate the values of α and β. By
this procedure, a three thousand sets of α and β have
been obtained. These values form the data base for
further analysis of diurnal, monthly, seasonal and
inter-annual features.
3 Discussion
3.1 Diurnal behaviour of α and β
Like many other climatic variables, the values of α
and β undergo a change throughout an individual day
GANESH et al.: ATMOSPHERIC TURBIDITY OVER MYSORE
because of changes in the atmospheric meteorological
parameters. These changes may either decrease or
increase the values of the Angstrom parameters. In the
present study, the variations in α and β have been
examined.
Figures 1-3 show the panel diagrams of the diurnal
plots of α and β for the three aerosol years 2003-04,
2004-05 and 2005-06, each one covering June to May
of the next year. In each of these years, a day in
87
monsoon (except monsoon 2003), a day in winter and
a day in summer are shown as typical ones depicting
the diurnal changes.
3.2 Monthly characteristics of α and β
Over the observation period of three years, about
three thousand sets of α and β distributed over 120
clear sky days of different months have been
analyzed. In order to bring out the monthly features of
Fig. 1 — Diurnal plots of α vs β for the aerosol year 2003-04
Fig. 2 — Diurnal plots of α vs β for the aerosol year 2004-05
88
INDIAN J RADIO & SPACE PHYS, APRIL 2011
Fig. 3 — Diurnal plots of α vs β for the aerosol year 2005-06
Fig. 4 — Day average of α and β for the month of December over
three years
turbidity parameters, the data set is grouped under the
months.
Thus, the values of α and β for a month, say
December, of three years are treated as a single unit.
For each day, the ensembles of α and β are averaged
out to obtain the mean values of α and β for the day.
Such day averages in the same month of the three
years have been analysed. Months with more than five
sets of α and β are only taken into account to bring
out the monthly features.
Fig. 5 — Day average of α and β for the month of March over
three years
Figures 4 and 5 show plots of day averages of α
and β in the months December and March,
respectively during winter and summer. The trend in
α and β is not observed during the monsoon season
because of the unavailability of the data. From these
figures, the minimum and maximum values of each
year have been derived.
3.3 Seasonal variations of α and β
The varying meteorological parameters are the
characteristics of the seasons of a year. It is, therefore,
GANESH et al.: ATMOSPHERIC TURBIDITY OVER MYSORE
expected that the atmospheric aerosols be subjected to
changes in number and size. Consequent to this, the
extinction of solar radiation by aerosols is also
affected. The turbidity parameters, which depend on
the spectral aerosol optical thickness, are expected to
exhibit changes through the seasons. The values of α
and β are shown by bar graph in Fig. 6. The seasonal
values of α and β are indicated by the height of the
bars for each of the aerosol year. A slight increasing
trend in β values is observed in the winter season
from 2003-04 to 2005-06 with lowest β values during
the winter of 2003-04. The β values for the monsoon
of two years exhibit a slightly increasing trend.
During the summer of 2005-06, β is highest and it
reduces to almost half the value during the other two
summers. These variations are a reflection in the
population of coarse particles during the three years.
The high value of β indicates large number of coarse
particles. The values of α exhibit an anti-correlation
with β values. Accordingly, the variation in the
population of finer particles is opposite to that of
coarse particles. With the increase in the day
temperature, evaporation rate of water also increases,
because of which there will be rise in atmospheric
water vapour content. This increase in water vapour
content influences the particle growth. Thus, small
89
sized (~0.1 µm) particle is converted to large size
(0.1-1 µm) and a large sized particle is converted to
coarse particle (>1 µm) which settles down to the
ground under the influence of gravity. Hence, this
leads to the observed variations in the values of α and
β of the seasons.
3.4 Average seasonal trends of α and β over three years
Seasonal averages of α and β have been presented
in Fig. 7. Monsoon values are available only for two
years 2004-05 and 2005-06. During these two years,
both α and β show an increasing trend. Winter values
of β during three years exhibit a slow increasing
trend, whereas α is highest during the winter of 2003
and 2004 followed by reduced and almost constant
values during the other two years. Summer β values
have an increasing trend and the α values have a
decreasing trend. The trends of α and β are indicative
of the changes in the population of small and coarse
particles during the seasons of the years. Summer
exhibits larger changes. The β values of monsoon
and winter indicate a smaller change of
particle population. A significant change in finer
particles is indicated by a larger decrease in α from
2003-04 to 2004-05, thereafter, the change is
negligible.
Fig. 6 — Monthly average of α and β for the period of three years
INDIAN J RADIO & SPACE PHYS, APRIL 2011
90
Fig. 7 — Seasonal averages of α and β over the years
3.5 Inter-annual trends in α and β
A year is an overall cycle of seasons combined
together. An average representation of α and β for a
year would be helpful in the study of aerosol
climatology. With this in view, the annual trend of
turbidity parameters for three years has been
presented in Fig. 8. From the figure, it is seen that β
has increased from 2003-04 to a slightly higher value
during 2004-05 and reaches high value during 200506. Similarly, α with a high value during 2003-04
decreases to a small value and remains almost same
during next two years. From these observations, it can
be said that the atmosphere is loaded with coarse
particles during 2005-06. A high concentration of
fine-particle is seen during 2003-04. The α value does
not vary significantly during the other two years
except 2003-04, with an average value of α ~ 1.7.
4 Results
On examining the
observations emerge:
panels,
the
following
1. α and β exhibit an anti-correlation throughout
the day during all the seasons. This indicates that
there is continuous redistribution of fine and
coarse particles under the influence of
meteorological parameters.
2. During 2003-04, significant diurnal variations in
α and β have been observed.
3. During the other two years, α and β do not
exhibit large variation. However, a maxima and
minima are observed in β and α, respectively at
about 1300 hrs LT on 22 November 2004 and 24
December 2004. These are due to the increased
Fig. 8 — Annual average trend of α and β for three years
concentration of coarse particles and a
simultaneous decrease in the concentration of
fine particles, respectively. Constancy of α and
β indicates no significant change in the coarse
and fine particulates concentration. For Mysore,
the average α parameter during these three years
turns out to be approximately 2. This value lies
within the range of -0.5 to 2.6 reported by
Cachorro et al.18-20.
4.1 An empirical relationship between α and β
Study of α and β, so far described, exhibits an anticorrelation between the two. However, this does not
give a definite connection between the two. In
addition, from the Angstrom’s equation, a simple
relationship between the two cannot be realized. It is
in this context that the numerical data of α and β has
been examined to arrive at a plausible empirical
relationship between the two.
Since α and β undergo a change in their values due
to changing meteorological conditions over a day, a
diurnal relationship is contemplated. By examining
the graphical plots of α vs β, it is observed that the
two exhibit a linear relationship on a large number of
days when sky conditions remained stable. It is also
evident that in general, throughout a day, β remains
either low (≤ 0.05) or high (> 0.05). On the days when
no regular trend is exhibited, the scatter may be
attributed to abrupt changes in meteorological factors
that may give rise to varying fine particle
concentration while coarse particles remain unaltered.
This means there would be an influx of fine particles.
On the days when α - β plots show good linearity, by
least squares analysis slope and intercept are
GANESH et al.: ATMOSPHERIC TURBIDITY OVER MYSORE
determined and these values have been presented in
Table 1.
Table 1 shows that the values of intercept vary
from 1.8 ± 0.2 to 4 ± 0.2. The value of 4 for the
intercept indicates that the Rayleigh scattering
prevails when the size of the particulates becomes
small of the order of molecular species2. On the other
hand, the slopes differ largely, in the range 2.3 ± 0.2
to 73 ± 4 (a linear decreasing trend is indicated by the
91
negative sign). This indicates that the rate of variation
of α with β change day-to-day. On low β-days, the
slopes are higher as compared to those of high β-days.
On a low β-day, the coarse particulates get depleted
fast resulting in larger value of the ratio of fine to
coarse particulates. This ratio is an index of α and
consequently the values of α are high. The βparameter is determined by the coarse particulates and
low β-results when the coarse particles are depleted.
Table 1 — Slope and intercept values for α - β plots
Date
Slope
Error
Intcpt
Error
Date
Slope
Error
Intcpt
Error
06 Dec 2003
07 Dec 2003
09 Dec 2003
10 Dec 2003
11 Dec 2003
16 Dec 2003
17 Dec 2003
21 Dec 2003
23 Dec 2003
17 Dec 2004
18 Dec 2004
25 Dec 2004
26 Dec 2004
01 Dec 2005
06 Dec 2005
08 Dec 2005
09 Dec 2005
13 Feb 2004
23 Feb 2004
24 Feb 2004
09 Feb 2005
10 Feb 2005
11 Feb 2005
12 Feb 2005
13 Feb 2005
19 Feb 2005
28 Feb 2005
03 Feb 2006
04 Feb 2006
07 Feb 2006
10 Feb 2006
11 Feb 2006
15 Feb 2006
16 Feb 2006
17 Feb 2006
18 Feb 2006
19 Feb 2006
20 Feb 2006
-20.72
-39.79
-21.06
-39.27
-21.22
-41.06
-9.91
-37.31
-12.50
-16.83
-19.07
-14.89
-42.29
-3.61
-44.13
-8.62
-18.06
-39.73
-54.26
-26.98
-33.49
-21.48
-18.88
-33.79
-29.92
-21.07
-19.22
-6.84
-10.76
-4.63
-5.62
-13.46
-3.91
-12.12
-5.12
-6.39
-13.65
-12.42
3.13
5.17
2.25
6.06
2.10
8.60
0.89
4.00
1.38
0.82
2.10
1.32
2.62
1.01
6.90
1.27
2.13
3.35
3.73
2.44
3.08
0.98
1.42
1.72
2.12
0.98
1.40
0.77
1.65
1.16
0.66
1.08
0.63
1.24
0.51
0.47
1.00
1.98
2.90
3.68
3.23
3.70
3.33
3.03
2.54
4.03
2.90
2.78
3.11
2.76
2.88
2.09
3.15
2.39
2.53
3.75
4.11
3.63
3.09
2.78
2.68
2.96
3.16
2.81
2.69
2.33
2.54
1.89
2.04
2.47
2.11
2.65
2.32
2.31
2.76
2.65
0.15
0.15
0.18
0.18
0.11
0.24
0.08
0.18
0.10
0.04
0.07
0.06
0.06
0.08
0.19
0.05
0.07
0.11
0.10
0.11
0.09
0.04
0.06
0.05
0.07
0.04
0.05
0.06
0.09
0.10
0.06
0.07
0.05
0.08
0.05
0.04
0.06
0.14
22 Feb 2006
24 Feb 2006
25 Feb 2006
27 Feb 2006
10 Jan 2004
12 Jan 2004
13 Jan 2004
15 Jan 2004
11 Jan 2006
12 Jan 2006
18 Jan 2006
19 Jan 2006
20 Jan 2006
23 Jan 2006
26 Jan 2006
30 Jan 2006
31 Jan 2006
14 Apr 2004
17 Apr 2004
12 Apr 2006
05 Mar 2004
01 Mar 2005
03 Mar 2005
14 Mar 2005
17 Mar 2005
18 Mar 2005
19 Mar 2005
26 Mar 2005
05 Mar 2006
07 Mar 2006
08 Mar 2006
15 Mar 2006
19 Mar 2006
17 May 2005
07 Oct 2004
08 Oct 2004
12 Jun 2006
-12.84
-31.30
-12.36
-6.46
-16.64
-13.07
-25.73
-52.31
-10.10
-22.80
-6.88
-37.28
-53.29
-6.14
-2.32
-8.28
-6.54
-6.83
-4.70
-5.95
-39.18
-9.68
-18.95
-8.42
-6.63
-29.11
-35.58
-9.06
-16.24
-11.78
-11.22
-6.56
-11.86
-5.87
-8.36
-72.63
-32.73
1.56
2.85
2.73
0.84
1.04
1.70
2.64
6.37
1.29
1.80
0.54
2.43
2.48
0.47
0.22
3.05
0.55
1.16
1.23
0.09
2.99
0.79
1.93
0.65
1.18
1.98
2.07
0.58
1.84
0.79
0.59
0.61
2.42
0.37
0.48
3.84
2.32
2.44
3.29
2.69
2.60
2.91
2.73
3.66
4.30
2.44
3.01
2.49
3.37
3.62
2.31
2.02
2.39
2.32
2.32
1.78
2.47
4.11
2.41
2.76
2.63
2.46
3.66
3.98
2.56
3.18
2.96
2.79
2.53
2.76
1.96
1.82
3.31
3.71
0.10
0.09
0.12
0.08
0.07
0.09
0.13
0.23
0.12
0.06
0.04
0.07
0.07
0.04
0.02
0.12
0.03
0.13
0.18
0.01
0.09
0.05
0.09
0.05
0.09
0.09
0.08
0.07
0.08
0.05
0.04
0.04
0.14
0.05
0.05
0.09
0.15
INDIAN J RADIO & SPACE PHYS, APRIL 2011
92
This phenomena explains large value of slope on low
β-day. Under the influence of suitable meteorological
parameters, the coarse particles may remain in the
atmosphere. This results in large values of β, and α
value will be low leading to smaller slopes, thus
production-depletion processes control the values of α
and β, and hence, the rate of variation between the
two parameters.
In Fig. 9, three-year data of α and β are presented
graphically. The scatter, indicates a polynomial fit,
would be required as the day-slopes vary. On a
logarithmic format, the scatter graph shows linear
trend (Fig. 10).
By least squares fitting, the following equation is
obtained:
lnα = -[0.30 lnβ + 0.29]
… (3)
radiation reaching the ground. Further, at wavelengths
shorter than 1 µm, finer particles transmit less energy
than the coarser particles.
Selby & McClatchey21 have developed a
relationship between the visibility (Vis) and the
turbidity parameters as:
β = [0.55]α [3.912/Vis – 0.01162][0.02472(Vis – 5)
+ 1.132]
… (4)
where, Vis is in kilometers. This expression is valid
for visibilities more than 5 km. By following an
iterative procedure in which α and β are inputs, the
visibility can be determined.
From Fig. 11, it can be seen that the visibility is
highest during winter followed by monsoon and
summer.
It is found that this equation yields, for a given
value of β, an α value with an average variation of
13%. This expression is a synoptic equation based on
the 3-year data collected at the site of observation.
Whether this equation is valid for any other location
needs to be examined to ascertain the spatial
applicability.
4.2 Visibility
The turbidity parameters play a significant role in
determining the visibility of a location. It is known
that at a fixed value of β, a lower value of α signifies
higher visibility, that is higher atmospheric transparency. By inference, it can be concluded that the
lower values of α, which means larger average
particle size would result in higher amounts of solar
Fig. 9 — Mass plot of α vs β for the three years
Fig. 10 — Plot of α vs β on a logarithmic scale
Fig. 11 — Season-wise average visibility over the years
GANESH et al.: ATMOSPHERIC TURBIDITY OVER MYSORE
References
Table 2 — Seasonal average visibility
Aerosol year
2003-04
2004-05
2005-06
2006-07
Average visibility, km
Monsoon
Winter
Summer
No data
65
43
41
68
65
60
No data
93
29
35
25
No data
4.3 Seasonal trends through the years
The monsoon of 2004 shows highest visibility. A
reduction of about 30% is indicated during the other
two years. Winter exhibits a slow decreasing trend in
visibility through three years. Summer of 2004-05
shows highest visibility and slightly reduced value
during other two years with a slightly higher value in
2003-04 than in 2005-06. Table 2 presents visibility
of the seasons during the years of observation. The
values are the averages of monthly values of each
season.
5 Conclusions
Important results have emerged by analyzing threeyear data. On any day, α and β vary with respect to
the change in meteorological parameters. Seasonally,
summer records the maximum value of β followed by
winter and monsoon, thereby, indicating more number
of large sized particles. Washout and rainout of
aerosols is the cause for low concentration of
particulates in monsoon. The anti-correlation between
α and β is the common feature for all the days of
observation. An annual trend shows that β has
increased from 2003-04 to a slightly higher value
during 2004-05 and reaches a high value during 200506. Similarly, α with a high value during 2003-04
decreases to a small value and almost the same during
the next two years. From these observations, it can be
said that the atmosphere is loaded with coarse
particles during 2005-06. A high concentration of
fine-particle is seen during 2003-04. In addition, the
visibility is found to be highest during winter
followed by monsoon and summer.
Acknowledgements
The authors thank Indian Space Research
Organization for the financial assistance through the
RESPOND scheme. The authors thank the University
of Mysore for the facilities. Special thanks to Late
Prof B. Narasimhamurthy for his kind support.
Sincere thanks to the Management of PESIT and to
the colleagues in the department for their constant
encouragement.
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