International Journal of Geosciences, 2012, 3, 44-49 Published Online February 2012 (
A Comparative Study of Cloud Liquid Water Content from
Radiosonde Data at a Tropical Location
Swastika Chakraborty1, Animesh Maitra2
1Department of ECE, JIS College of Engineering, Kalyani, India
2S. K. Mitra Centre for Research in Space Environment, Institute of Radiophysics and Electronics,
University of Calcutta, Kolkata, India
Received June 10, 2011; revised August 16, 2011; accepted October 25, 2011
In this paper, some features of cloud liquid water content with respect to rain and water vapor are presented. Cloud liq-
uid water density profile is obtained from radiosonde observation with Salonen’s model and Karsten’s model at Kolkata,
a tropical location in the Indian region. Cloud liquid water contents (LWC) are obtained from these profiles which show
a prominent seasonal variation. The monsoon months exhibit much higher values of LWC than in other months. How-
ever Salonen’s model yields higher LWC values than that obtained with Karsten’s model. The variation of daily total
rainfall with LWC shows a positive relationship indicating the role of LWC in controlling the rainfall. Also the varia-
tion pattern of LWC with integrated water vapor (IWV) content of the atmosphere indicates that a threshold value of
water vapor is required for cloud to form and once cloud is formed LWC increases with IWV.
Keywords: Cloud Liquid Water Contents (LWC); Integrated Water Vapor (IWV)
1. Introduction
The study of cloud properties is increasingly important in
the context of climate research of troposphere. One of the
sources of global warming is the cloud feedback and wa-
ter vapour feedback. Again as relative humidity has a
greater impact on cloud formation, knowledge of mois-
ture distribution of troposphere is necessary to know the
cloud process [1]. Parameterization of cloud component
is very much necessary as cloud plays a dual role in af-
fecting outgoing long wave radiation (OLR) as well as
reflecting incoming solar radiation [2]. Cloud Liquid
water content (LWC) plays also a dominant role in att-
enuating electromagnetic signal [3]. Stability of air is
another important matter of concern as cloud develop-
ment is associated with it. As air parcel is very large, it is
realistically considered that it does not exchange any heat
with surrounding as it rises and due to the expansion in
volume it cools at a relatively constant rate. Depending
on whether the air is saturated or unsaturated, the impor-
tant parameter of cloud formation i.e. moist adiabatic
lapse rate (MALR) or dry adiabatic lapse rate (DALR)
comes into the picture. To know the profile of liquid wa-
ter content and thereby total liquid water content for a
particular day and also the amount of water vapour in the
atmosphere the knowledge of humidity profile is also
important. Water vapour can be related to low level hu-
midity and low atmospheric humidity can be obtained as
a function of surface temperature [4]. Retrieval of water
vapour by Special Sensor Microwave Imager (SSMI)
shows the dependence of water vapour is not only on
humidity but also on atmospheric circulation [5]. Over
the past two decades many retrieval methods have been
obtained for water vapour and cloud LWC.
In case of water vapour no method is identified as the
most accurate as there is a mismatch between satellite
footprint and in situ measurement. In addition, for LWC
cloud shape and structure causes the unreliability of the
measured data.
Retrieval of the profile of cloud LWC by Radiometer
shows the result in clear and cloudy condition. A com-
parison of radiometric profile is done with sounding from
super cooled cloud liquid sensor carried by radiosonde.
But not more than 50% agreement was observed between
the two processes [6]. Though the profile of temperature
and water vapour can be given by Laser radar (Lidar) and
Fourier Transform Infrared Spectrometer (FTIR), but in
presence of cloud neither the Lidar nor the FTIR will
work. In a different approach, as cloud reflectivity is
proportional to cloud drop radius and as cloud LWC is
proportional to the volume of the cloud drops, LWC can
be derived from reflectivity factor. But an error more
than one order of magnitude of actual value is obtained
as the relation is not linear. There are some other meth-
ods also for retrieving cloud LWC. For the methods us-
opyright © 2012 SciRes. IJG
ing radiosonde data have to rely on a relative humidity
(RH) threshold. When RH exceeds a threshold value
cloud layers are supposed to be formed. In another ap-
proach it is assumed that each of the cloud layers satis-
fies the following equations.
dT 0
dz (1)
dRH 0
where T denotes temperature [7]. Here saturation region
is taken as region of RH maximum and a region of
weaker temperature decrease is considered for pseudo-
adiabatic lapse rate within the cloud. Again there is an-
other microphysical dynamic cloud model (DCM) where
convection is initialized diabatically [8]. Here humidity
and temperature profile are physically consistent with
LWC profile but the clear sky condition can not be de-
scribed by this model as the model always generates
cloud. In the present study LWC profile obtained from
Salonen’s [9] model and Karsten’s [10] model are com-
pared. Integrated value of LWC is calculated throughout
the year from the above two models and compared also
for a tropical location, Kolkata (22˚C 34N, 88˚C 29E),
for a period of three years.
2. Theoretical Basis
As cloud formation is associated with high relative
humidity, radiosonde data can indicate the presence of
cloud liquid water content depending on whether relative
humidity exceeds a critical value. According to Karsten’s
model, cloud is formed when the relative humidity
exceeds 95%. Again the phase of the water is determined
by its temperature profile. If temperature is greater than
0°C liquid water is formed. From the adiabatic concept
of thermodynamics, the cloud liquid water content (LWC)
can be calculated at each height level by the relation
 
add s
LWCh = ρ(z) ΓΓdz
where ρ(z) = air density, Cp = specific heat at constant
pressure, L=latent heat of vaporization, Гd = dry adia-
batic lapse rate, Гs = moist adiabatic lapse rate. In the fo-
rmula of LWC, Гs varies from 4˚C/km to 9.8˚C/km
depending on the seasonal variation of temperature. The
air density is calculated from the ideal gas equation. Also
considered is Cp = 1.0035 J·g–1·k –1, L = 80 cal/gm. The
adiabatic condition gives maximum value of LWC which
is reduced due to circulation of air mass accompanied by
precipitation and freezing. The modified LWC is given by
LWC=LWC1.239 0.145lnΔhkgm (4)
calculated at each pressure level at a particular radio-
sonde ascent. Integrating the LWC profile over height,
the total value of LWC is obtained at each ascent. Acco-
rding to Salonen’s model also when relative humidity ex-
ceeds the critical humidity, cloud is formed. But critical
humidity is calculated from Geleyn’s formula
 
U=1 σ1σ1+ σ0.5αβ 
where α = 1.0, β = 3, σ is the ratio of pres
sure at the
considered level andessure at the surface level [11].
Again the phase of the liquid water is determined on the
basis of temperature profile. When temperature is greater
than 0˚C, contribution of liquid water content of cloud is
significant. Liquid water content w (g/m3) as a function
of temperature t (˚C) and height hc from the base has
been calculated by the relation
 
W=W 1+ctρt
 (6)
where a = 1.4, c = 0.041/˚C, W0 = 0.14 gm/m3 for each
e of water vapour satura-
radiosonde ascents at each pressure level. Integrating the
profile of liquid water content over height, the value of
cloud liquid water content (LWC) for each radiosonde
ascents has been obtained. The total variation of LWC is
observed throughout the year.
The temperature dependenc
n pressure esw (100% RH) is approximated and in turn,
expressed as vapour concentration,
v=7.223e=1.739 10θ3
wu gm/mθ (7)
300 T273θ, Tt = dry bulb temperat
3. Data
e balloon is released from a location over
4. Results
profiles of a particular day obtained using
which characteristics of the troposphere are desired to be
known. Radiosonde measurements are obtained twice a
day at around 00 and 12 GMT (1830 and 0630 IST) by
the Indian Meteorological Department at Kolkata, India
(22˚C 34N, 88˚C 29E). The data from the period January
to December of the year 2005 to 2007 have been used in
this study. The data of temperature, pressure and dew
point temperature at different height with a resolution of
few tens of meters to few hundreds of meters up to a
height of 15 km is measured. Temperature is measured
by the carbon rod thermistor which measures the tem-
perature from –90˚C to 60˚C with a resolution of 0.1˚C.
Pressure is measured by an aneroid barometer with a
resolution of 1 mb. Dew point temperature is obtained
from relative humidity measured by a carbon hygristor
with a resolution of 2% RH.
Cloud LWC
where Δh = height above the cloud base. LWC is then
Copyright © 2012 SciRes. IJG
Karsten’s model and Salonen’s model are shown in Fig-
ure 1. These two profiles show same nature; however the
values of LWC are greater with the Salonen’s model than
with the Karsten’s model as the adiabatic LWC is re-
duced due to circulation of air mass accompanied by pre-
cipitation and ice particles. Integrated value of LWC is
obtained by integrating the profile of cloud LWC for
each day of the year from Salonen’s and Karsten’s model.
For a better comparison of LWC obtained by the above
two methods, integrated value of LWC is plotted for the
year 2005 to 2007 (Figures 2-4). Figure 5 shows the
variation in monthly mean value from the month of
May to October of each year of cloud LWC obtained
using Salonen’s as well as Karsten’s model from the
year 2005 to the year 2007. It is obvious from these two
figures that the two models are in good agreement in the
tropical location with respect to the nature of variation
but always Salonen’s model gives a higher value. Varia-
tion of monthly mean value shows a strong seasonal
variation of cloud LWC. Cloud LWC obtained from
Karsten’s model is plotted in Figure 6 against cloud
LWC obtained from Salonen’s model. LWC obtained
from Karsten’s maintains a linear relation with that ob-
tained from Salonen’s model indicating again these two
models are in good agreement regarding the nature of varia-
tion in tropical location. Difference of cloud LWC obtained
from the models of Salonen and Karsten obtained for
each day of the year of 2007 is plotted in Figure 7.
The variation of difference throughout the year shows,
5. Conclusions
e to Salonen and Uppala [9] and Kar-
acceptable prediction of the
ere is a difference between amounts of cloud LWC
obtained from two models, but the difference is reduced
by only 10% of the maximum value. The variation of
daily total rain amount with cloud LWC is plotted for the
year 2005-2007 in Figures 8-10. The figures show that
the rain amount increases with the amount of cloud LWC.
The variation of amount of cloud LWC in Figures 11
and 12 along with water vapour indicates a threshold
value of water vapour that is required for the formation
of liquid water.
The two models, du
sten et al. [10], have been used to obtain cloud LWC at
temperature above 0˚C and also in the mixed layer in the
temperature range from –20˚C to 0˚C. The result shows a
strong seasonal pattern of cloud LWC which is a charac-
teristic feature of tropical region. The analysis of data
during the period 2005 to 2007 confirms that a threshold
value of water vapour is required for the cloud to form.
Once cloud is formed, daily total rain amount is found to
increase with cloud LWC.
Salonen model gives an
ount of cloud LWC from Radiosonde data. As mon-
sten model is more relevant than the Salonen model as it
Figure 1. A typical profile of cloud liquid water density obt-
ained from radiosonde measurements using Salonen’ s model
and Karsten’s model of relative humidity and critical hu-
midity at Kolkata on 4 July 2007.
Figure 2. Comparison between the values of cloud LWC
obtained from Salonen’s and Karsten’s model at different
days during 2005.
Figure 3. Comparison between the values of cloud LWC
obtained from Salonen’s and Karsten’s model at different
days during 2006.
Copyright © 2012 SciRes. IJG
Figure 4. Comparison between the values of cloud LWC
obtained from Salonen’s and Karsten’s model at different
days during 2007.
Figure 5. Comparison of monsoonal variation of monthly
mean value of Cloud LWC obtained from Salonen’s and
Karsten’s model for a period of 2005 to 2007.
Figure 6. A plot between LWC obtained from Salonen’s
and Karsten’s model during 2005.
Figure 7. A plot of difference in the values of LWC obtained
with Salonen’s and Karsten’s model (LWCsal – LWCkar)
regarding integrated value of cloud LWC obtained per day.
Figure 8. Variation of rain amount with cloud LWC for the
year 2005.
Figure 9. Variation of rain amount with cloud LWC for the
year 2006.
Copyright © 2012 SciRes. IJG
Figure 10. Variation of rain amount with cloud LWC for
the year 2007.
Figure 11. Variation of liquid water content (LWC) and
integrated water vapour (IWV) during 2005.
Figure 12. Variation of liquid watered content (LWC) and
mosphere, Kar-
d by the project “Studies on
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sten model is more relevant than the Salonen model as it
considers adiabatic LWC in addition to temperature pro-
file while calculating LWC of cloud. It also considers
circulation of air mass accompanied by precipitation and
freezing to deliver a realistic picture of cloud LWC. Our
further investigation will indicate if the picture is true for
all other tropical location like Kolkata.
6. Acknowledgements
This work has been supporte
tropical rain and atmospheric water content using ground
based measurements and satellite data related to Megha
Tropiques Mission” funded by Space Application Centre,
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