Atmospheric and Climate Sciences, 2011, 1, 225-234
doi:10.4236/acs.2011.14025 Published Online October 2011 (http://www.SciRP.org/journal/acs)
Surface Freshwater Flux Estimation Using TRMM
Measurements over the Tropical Oceans
Satya Prakash1*, Mahesh C1, Rakesh Mohan Gairola1, Samir Pokhrel2
1Atmospheric and Oceanic Sciences Group, Space Applications Centre, ISRO, Ahmedabad, India
2Indian Institute of Tropical Meteorology, Pune, India
Received August 8, 2011; revised September 12, 2011; accepted September 24, 2011
Abstract
The exchange of surface freshwater, heat and moisture fluxes across the air-sea interface strongly influences
the oceanic circulation and its variability at all time scales. The goal of this paper is to estimate and examine
surface freshwater flux at monthly scale exclusively from the Tropical Rainfall Measuring Mission (TRMM)
measurements over the tropical oceans for the period of 1998-2010. The monthly mean fields of TRMM Mi-
crowave Imager (TMI) sea surface temperature (SST), wind speed (WS), and total precipitable water (W) are
used to estimate the surface evaporation utilizing the bulk aerodynamics parameterization formula. The
merged TRMM Multisatellite Precipitation Analysis (TMPA)-3B43 product is combined with the estimated
evaporation to compute the surface freshwater flux. A preliminary comparison of the satellite derived
evaporation, precipitation and freshwater flux has been carried out with the Hamburg Ocean Atmosphere
Parameters and Fluxes (HOAPS-3) datasets. Also, the estimated evaporation and TMPA-3B43 precipitation
are validated with in-situ observations from the moored buoys in the different oceans. The results suggest
that the TRMM has great potential to estimate surface freshwater flux for climatological and oceanic hydro-
logical applications.
Keywords: Evaporation, Precipitation, Freshwater Flux, Tropical Oceans, Oceanic Circulation
1. Introduction
Short-term climate changes are believed to be strongly
influenced by large scale ocean-atmosphere interactions
through exchanges of momentum, heat and water. One of
the strongest links between ocean, land and atmosphere
is the freshwater fluxes due to evaporation (E) and pre-
cipitation (P) processes. Evaporation controls the loss of
freshwater and precipitation governs most of the gain of
freshwater. Inputs from rivers and melting ice can also
contribute to freshwater gains. Evaporation connects the
energy to the hydrological cycles of ocean and atmos-
phere. In the ocean, evaporation cools the upper layer
and increases the salinity; thus it has a direct impact on
the thermohaline circulation of the ocean, which is rec-
ognized as a key element of the climate system for varia-
tions on decadal to millennial time scales. Precipitation
also affects the height of the ocean surface indirectly via
salinity and density. Evaporation minus precipitation
(E-P) is usually referred to as the net flux of freshwater
or the total freshwater in or out of the oceans. E-P deter-
mines surface salinity of the ocean, which helps in de-
termining the stability of the water column. Since the
distribution of evaporation, precipitation, ice and conti-
nental runoff is the primary factor in the determination of
surface salinity, it is essential to quantify it to adequately
understand the ocean hydrological cycle [1].
Direct observations of evaporation and precipitation
and thus freshwater flux over the global oceans are very
sparse. Hence, most surface flux estimates are based on
in-situ observations, satellite measurements and atmos-
pheric analyses rely on bulk formulae [2-8]. Although,
the main deficiency of this method is the difficulty of the
estimation of the near-surface specific humidity (Qa)
from the satellite based total precipitable water (W).
Some empirical and statistical relationships have been
developed to estimate Qa from W in the past years [9-14].
However, the comparison of different flux datasets indi-
cates large deviations from one another [15-18]. From a
comparison with buoys data, Bourras [16] demonstrated
that the overall regional accuracy of satellite-derived
fluxes is of the order of 20% - 30% whereas these errors

S. PRAKASH ET AL.
226
need to be 5% - 10% lower for the quantitative analyses.
In past few years, optical and microwave (MW) sen-
sors onboard meteorological satellites have tremendously
improved the precipitation estimation at different spatial
and temporal scales. Some algorithms to integrate mi-
crowave and infrared (IR) measurements for accurate
precipitation estimation over land and oceanic regions
have been developed in the recent years which utilize the
advantage of the relative accuracy of the MW based es-
timates and the relatively low sampling errors of the IR
based estimates [19-22]. To further improve the rainfall
estimation, some algorithms to merge the in-situ observa-
tions with satellite-retrieved rainfall to tap the excellent
spatial coverage by the satellite measurements and the
good bias characteristics of the rain gauge data have been
developed [23-25].
But, due to lack of available comprehensive in-situ
validation data, the combination of these differently cali-
brated and inhomogeneous data sources to estimate the
global freshwater flux is indeed a difficult task [26]. The
Hamburg Ocean Atmosphere Parameters and Fluxes
from Satellite Data (HOAPS-3) derives independently
the required parameters for the global ice-free ocean
surface freshwater flux from the Special Sensor Micro-
wave Imager (SSM/I) brightness temperatures and Ad-
vanced Very High Resolution Radiometer (AVHRR)-
based sea surface temperature (SST) datasets [27,28]. The
uncertainty in retrieval and biases between the different
datasets cause unspecified errors in these surface fresh-
water flux estimates which limits the applicability of
these products at global and regional scales.
The Tropical Rainfall Measuring Mission (TRMM)
particularly with the help of space-borne microwave ra-
diometer, the TRMM Microwave Imager (TMI) provides
an opportunity to estimate surface evaporation over the
tropical oceans from a single instrument which will cer-
tainly reduce the biases and uncertainties in the geo-
physical parameters. Aboard the TRMM satellite, the
10.65 GHz channels of the TMI led to accurate estimates
of SST [29], wind speed and have been extensively used
for various applications. In the present study, we empha-
size the estimation of surface freshwater flux by deriving
the evaporation from a single radiometer TMI measure-
ment and precipitation from TRMM Multisatellite Pre-
cipitation Analysis (TMPA) product. A preliminary
comparison of the satellite-derived surface freshwater
flux has been carried out with the HOAPS-3 product.
The final assessment is done on the basis of validation of
evaporation and precipitation estimates with in-situ
measurements from the moored buoys. The validation
exercise is done for the Indian, the Pacific and the Atlan-
tic Oceans separately using the RAMA, TAO/TRITON
and PIRATA buoys respectively.
2. Data Used
2.1. TMI Data
The TMI is a passive MW radiometer with nine linearly
polarized channels measuring at 10.65, 19.35, 21.3, 37.0,
and 85.5 GHz and is well calibrated similar to SSM/I.
Both vertical and horizontal polarizations are measured
at all frequencies except 21.3 GHz, where only the verti-
cal polarization is measured. The important feature of
microwave retrievals is that SST can be measured
through clouds, which are nearly transparent at 10.65
GHz. This is a distinct advantage over the traditional
infrared SST observations that require a cloud-free field
of view [30]. Furthermore, microwave retrievals are not
affected by aerosols and are insensitive to atmospheric
water vapor. However, the microwave retrievals are sen-
sitive to sea-surface roughness, while the infrared re-
trievals are not. A primary function of the TMI SST re-
trieval algorithm is the removal of surface roughness ef-
fects and shows a mean bias of 0.07˚C and a standard
deviation of 0.57˚C when compared to the TAO/TRITON
and PIRATA SSTs [29]. In addition to SST retrievals,
surface wind speeds, total precipitable water, liquid
cloud water, and rain rates are also retrieved from the
TMI using similar algorithms those used in SSM/I data
processing [31].
In the present study, the monthly SST, WS and W ver-
sion 4 (V4) datasets from 1998 to 2010 available at 0.25˚
latitude × 0.25˚ longitude are archived from the website
at http://www.remss.com and have been used to estimate
the evaporation.
2.2. TMPA Precipitation Data
The TRMM Multisatellite Precipitation Analysis (TMPA)-
3B43 Version-6 (V6) is a standard monthly precipitation
product which is derived after averaging the three-hourly
TMPA-3B42 V6 precipitation products. TMPA-3B42
combines precipitation estimates from multiple satellites
as well as gauge analyses wherever feasible [24]. TMPA
is available both after and in real time, based on calibra-
tion by the TRMM combined instruments (TMI and Pre-
cipitation Radar) and other climatological precipitation
products. Only the gauge-adjusted product incorporates
gauge data which is called TMPA-3B42 V6 data. The
data from TRMM satellite are archived and distributed
by the National Aeronautics and Space Administration
(NASA) Goddard Space Flight Centre (GSFC). The
available data from 1998 to 2010 in 0.25˚ latitude × 0.25˚
longitude have been used to estimate the surface fresh-
water flux in this study. The data for the entire study
period is archived from the website at http://disc2.nas-
com.nasa.gov/Giovanni/tovas/.

S. PRAKASH ET AL.
227
2.3. HOAPS-3 Data 2.4. Buoy Data
To validate the evaporation and precipitation estimates
separately, the required in-situ parameters have been used
from the global tropical moored buoy array program, con-
sisting of the Tropical Atmosphere Ocean/Triangle Trans-
Ocean Buoy Network (TAO/TRITON) in the Pacific [32],
the Prediction and Research Moored Array in the Tropical
Atlantic (PIRATA) [33], and the Research Moored Array
for African-Asian-Australian Monsoon Analysis and Pre-
diction (RAMA) in the Indian Ocean [34]. The location
maps of the buoys used for validation in the present study
are shown in Figures 1(a)-(c). The relevant data of 21
RAMA buoys, 67 TAO/TRITON buoys, and 21 PIRATA
buoys have been procured from the Tropical Atmosphere
Ocean (TAO) Project Office of the National Oceanic and
Atmospheric Administration―Pacific Marine Environ-
mental Laboratory (NOAA/PMEL).
The HOAPS-3 products derive independently the required
parameters for the global ice-free ocean surface freshwater
flux from the Special Sensor Microwave Imager (SSM/I)
satellite and Advanced Very High Resolution Radiometer
(AVHRR)-based sea surface temperature (SST) datasets
[27,28]. The wind speed and precipitation are estimated
using neural network based algorithms. The specific goal
of HOAPS is to derive the global ocean freshwater flux
consistently from satellite based data. An inter-sensor
calibration from different satellites is applied for a ho-
mogeneous and reliable spatial and temporal coverage
[28]. The available evaporation, precipitation and fresh-
water flux datasets from the mid 1987 to 2005 at 0.5˚ ×
0.5˚ resolution have been procured from the website
http://www.hoaps.org for the comparison purpose.
Figure 1. Location of the moored buoys in (a) the Indian Ocean (RAMA), (b) the Atlantic Ocean (PIRATA), and (c) the Pa-
cific Ocean (TAO/TRIRON) used for validation of the satellite products.

S. PRAKASH ET AL.
228
3. Methodology
The evaporation is estimated by the bulk aerodynamic
parameterization formula, which is suitable for both sat-
ellite and in-situ surface observations:


Ea
s
E
U Q Q
C
  (1)
where E is the evaporation, ρ is the air density taken as
1.2 kg·m–3,
E
Cis the bulk transfer coefficient for water
vapor (also called Dalton number), Qs and Qa are the
specific humidity at the sea surface and in the air, and U
is the wind speed at a height of typically 10 m.
The Dalton number
E
C is a function of wind speed
and air-sea temperature difference and computed by the
relationship [3]:

3 exp1
10 E
d
abUc
CU



(2)
where a = –0.146 785, b = –0.292 400, c = –2.206 648,
and d = 1.611 229 2.
The specific humidity at the sea surface is given by
0.622
s
s
s
s
e
e
QP
 (3)
10
C
BT
s
A
s s
eT

 


  (4)
where es is saturated vapor pressure at the sea surface, A =
–4.298, B = 23.55, C = –2937.0, Ts is the sea surface tem-
perature in K, and Ps is the sea surface pressure in hPa.
Since, direct measurement of the near-surface air
temperature from space is not possible; the surface level
humidity is given in terms of a fifth order polynomial
relationship with total precipitable water (W) [14] as
23 4
aaW bcde
QWWWW
 5 (5)
where a = 3.818724, b = 0.1897219, c = 0.1891893, d =
–0.07549036, and e = 0.006088244.
The evaporation is computed using the TMI derived
finished data of SST, WS, and W for each 0.25˚ × 0.25˚
grid points and subtracting the TMPA-3B43 precipitation
from it, the surface freshwater flux is estimated for each
month from 1998-2010.
4. Results and Discussions
As mentioned earlier, the monthly evaporation has been
initially estimated from the TMI measurements follow-
ing the bulk aerodynamic formulations [35-36] from the
period of 1998-2010. The surface freshwater flux is
computed from the TMPA-3B43 precipitation and the
present evaporation estimates and compared with another
independent satellite derived HOAPS-3 datasets. The
final assessment has been done on the basis of validation
with the moored buoys in the Indian Ocean, the Pacific
Ocean, and the Atlantic Ocean separately.
4.1. Comparison with HOAPS-3 Products
The monthly evaporation fields using the TMI measure-
ments for January and July, 2005 are shown in Figures
2(a)-(c) and the same using HOAPS-3 datasets are given
in Figures 2(b)-(d). Both the datasets show similar pat-
terns of evaporation qualitatively. The minimum evapo-
ration zones are situated in the southern oceans during
January, whereas in July the minimum zones are shifted
in the northern oceans. The tropical Atlantic Ocean gets
low evaporation (less than 2 mm·day–1) during both the
periods. The monthly precipitation fields for the same
period using TMPA-3B43 (Figures 3(a)-(c)) and HO-
APS-3 (Figures 3(b)-(d)) datasets show the maximum
precipitation (more than 13 mm·day–1) occurs over the
inter-tropical convergence zones (ITCZ). The general
circulation of the ocean and atmosphere has several fea-
tures, and most dominant are the convergence zones.
These are the zones where surface is convergent; surface
temperature, cloudiness and rainfall are high; and rapidly
rise to the middle of the troposphere or higher. Depend-
ing on the inter-annual variations, the ITCZ migrates
throughout the year between 5˚ N and 20˚ N being far-
thest from equator in July and closest to the equator in
January [37] which are clearly seen in both the precipita-
tion products. However, the HOAPS-3 dataset show
slightly more precipitation than the TMPA-3B43 product
over the precipitation dominant regions. The TMPA-
3B43 precipitation dataset is supposed to be more accu-
rate because it utilizes the advantage of the relative ac-
curacy of the microwave based estimates and the rela-
tively low sampling errors of the infrared based estimates
[24].
Figures 4(a)-(c) shows the monthly surface freshwater
fields using the TRMM measurements for January and
July, 2005 whereas the same using HOAPS-3 datasets are
shown in Figures 4(b)-(d). The E-P fields are generally
quite similar to that of precipitation fields qualitatively,
except that the magnitudes are reduced due to the effect of
evaporation. The patterns are matches well in both the
products, but the absolute values differ slightly over the
extreme flux zones. In the Arabian Sea, the present esti-
mate shows E-P of 8 - 10 mm·day–1 whereas the HOAPS-3
shows 6 - 8 mm·day–1 whereas in the southern equatorial
Oceans the HOAPS-3 dataset shows 8 - 10 mm·day–1 and
the present estimate shows 6 - 8 mm·day–1 during January,
2005. The moderate E-P fields (–4 to 4 mm·day–1) are
matches well in both the datasets. In July, 2005 due to the
marginal difference in absolute values of both the precipi-
tation products, similar discrepancies are observed.

229 S. PRAKASH ET AL.
Figure 2. Monthly evaporation fields (mm·day–1) for January and July, 2005 from TMI (a, c) and HOAPS-3 (b, d) datasets.
Figure 3. Monthly precipitation fields (mm·day–1) for January and July, 2005 from TMPA-3B43 (a, c) and HOAPS-3 (b, d)
datasets.
4.2. Climatology of E, P and Freshwater flux
The mean climatologies of the evaporation, precipita-
tion and freshwater flux are computed from the 1998 to
2010 monthly datasets. The mean global tropical ocean
evaporation (Figure 5(a)) shows the well-known cli-
matological distributions with strong maxima over the
either sides of equator with values upto 8 mm·day–1.
The lower evaporation zones (less than 4 mm·day–1) are
the equatorial belts consistent with the high precipita-
tion zones (7 - 10 mm·day–1) in the influence of the
trade winds (Figure 5(b)). Also, the regional maxima
of precipitation (upto 10 mm·day–1) are over the tropical
Indian Ocean and the South Pacific conversion zone
(SPCZ). The surface freshwater flux which is the dif-
ference between evaporation and precipitation is domi-
nant over the either high evaporation or precipitation
zones (Figure 5(c)). The tropical Indian Ocean and
SPCZ along with the equatorial belts receive low E-P
(–2 to –6 mm·day–1) due to high rain rates whereas the
northern Arabian Sea, the southern Indian Ocean, the
south-eastern Pacific, and the southern Atlantic Ocean
receive more E-P (6 mm·day–1 - 8 mm·day–1) due to
intense evaporation.

S. PRAKASH ET AL.
230
Figure 4. Monthly surface freshwater flux fields (mm·day–1) for January and July, 2005 from TRMM (a, c) and HOAPS-3 (b,
d) datasets.
Figure 5. Climatological mean fields (mm·day–1) of the (a) TMI evaporation, (b) TMPA-3B43 precipitation, and (c) TRMM
freshwater flux for the year 1998 to 2010.

231 S. PRAKASH ET AL.
4.3. Validation with Buoy Measurements
The final assessment is done on the basis of validation of
the TMI evaporation and TMPA-3B43 precipitation
products with the in-situ measurements of the moored
buoys in the Indian, the Pacific, and the Atlantic Oceans
separately. The evaporation is computed from the buoys
using the wind speed, surface air temperature, sea sur-
face temperature, and relative humidity observations.
The method of evaporation computation is followed by
Simon and Desai [7]. The available observations of 21
RAMA buoys in the Indian Ocean (Figure 1(a)), 67
TAO/TRITON buoys in the Pacific Ocean (Figure 1(b)),
and 21 PIRATA buoys in the Atlantic Ocean (Figure
1(c)) are used for the validation purpose. During the
processing of the buoys data, all the missing values are
eliminated and only relevant observations are used for
the validation. The relatively less number of data points
in the Indian Ocean is due to the fact that the RAMA was
initiated in 2004 for improved description, and prediction
of the east Africa, Asian and Australian monsoon sys-
tems [34].
The scatter plots between the TMI based and the buoy
estimated evaporation in the three oceans are shown in
Figures 6(a)-(c). In the Indian Ocean, the correlation
coefficient between the TMI and the buoys evapora-
tion is 0.64, bias is 1.01 mm·day–1, and root-mean-
square error (RMSE) is 1.32 mm·day–1 which shows the
present method slightly overestimates evaporation rates.
In the Pacific Ocean, due to the sufficient number of
buoys a good number of data points are obtained. The
correlation of 0.79, bias of 1.04 mm·day–1, and RMSE
of 1.50 mm·day–1 is observed between the TMI and the
buoys evaporation. Similarly, in the Atlantic Ocean, the
correlation of 0.85, bias of 1.10 mm·day–1, and RMSE
of 1.60 mm·day–1 is obtained. The overall statistics
shows the systematic overestimation of evaporation by
the TMI estimates. The possible reason behind this
overestimation is the error in the global Qa-W relation-
ship. The regional formulation of this relationship will
certainly improve the evaporation estimates signify-
cantly [11,12]. The scatter plots between the
TMPA-3B43 and the buoy precipitation rates in the
three oceans are shown in Figures 7 (a)-(c). In the In-
dian Ocean, the correlation coefficient between the TMI
and the buoys precipitation is 0.73, bias is 0.26
mm·day–1, and RMSE is 3.27 mm·day–1. The correlation
of 0.87, bias of –0.02 mm·day–1, and RMSE of 2.47
mm·day–1 is observed between the TMI and the buoys
precipitation in the Pacific Ocean. Similarly, in the At-
lantic Ocean, the correlation of 0.91, bias of –0.16
mm·day–1, and RMSE of 1.73 mm·day–1 is obtained.
The results show the reasonable compliance between
the TMPA-3B43 and buoy precipitation, but more ad-
vancement in precipitation retrieval algorithms are
necessary for the precise local quantification.
5. Conclusions
The TRMM satellite based geophysical parameters has
been used for the surface freshwater flux estimation over
the tropical oceans for the period of 1998-2010 at
monthly scale and 0.25˚ × 0.25˚ resolution. Aboard the
TRMM satellite, a single multi-channel passive micro-
wave TMI instrument package provides a more consis-
tent estimate of the surface evaporation fields. The com-
parison results with another independent satellite derived
HOAPS-3 products are encouraging, showing the good
Figure 6. Scatter plots between the TMI evaporation and moored buoys evaporation in (a) the Indian Ocean, (b) the Pacific
Ocean, and (c) the Atlantic Ocean. The number of data points (N), correlation coefficient (R), bias, and root-mean-square
error (RMSE) values are given in the upper-left corner of the each plot.

S. PRAKASH ET AL.
232
Figure 7. Same as Figure 5, but for the TMPA-3B43 and moored buoys precipitation.
agreement and consistency. The climatology of the
freshwater flux estimate is consistent with the evapora-
tion and precipitation climatologies. The validation of
the TMI evaporation with buoys datasets shows a con-
stant positive bias which suggests a systematic overesti-
mation by the present estimate. The TMPA-3B43 pre-
cipitation product is in considerably good agreement
with the buoys measured precipitation. The present study
has clearly shown that there is a need of extensive effort
for the development of regional Qa-W relationship to
further improvement in the evaporation estimates. Also,
the further refinement in precipitation estimates at re-
gional scale would certainly improve the freshwater flux
estimates considerably in conjunction with the evapora-
tion estimates.
6. Acknowledgements
The authors would like to thank the Director, Space Ap-
plications Centre, the Deputy Director, EPSA and the
Group Director, AOSG for the encouragement and sup-
port. The TMPA-3B43 data used in this study were ac-
quired using the GES-DISC Interactive Online Visuali-
zation ANd aNalysis Infrastructure (Giovanni) as part of
the NASA’s Goddard Earth Sciences (GES) Data and
Information Services Center (DISC), TMI data are pro-
duced by Remote Sensing Systems and sponsored by the
NASA Earth Science MEaSUREs DISCOVER Project,
HOAPS-3 data from http://www.hoaps.org, and buoy
data from the Tropical Atmosphere Ocean (TAO) Project
Office of NOAA/PMEL are thankfully acknowledged.
7. References
[1] P. J. Webster, A. M. Moore, J. P. Loschnigg and R. R.
Leben, “Coupled Ocean-Atmosphere Dynamics in the In-
dian Ocean during 1997-1998,” Nature, Vol. 401, 1999,
pp. 356-360. doi:10.1038/43848
[2] L. Yu and R. A. Weller, “Objectively Analyzed Air-Sea
Heat Fluxes for the Global Ice-Free Oceans (1981-
2005),” Bulletin of American Meteorological Society, Vol.
88, 2007, pp. 527-539. doi:10.1175/BAMS-88-4-527
[3] A. Bentamy, K. B. Katsaros, A. M. Mestas-Nunez, W. M.
Drennan, E. B. Forde and H. Roquet, “Satellite Estimates
of Wind Speed and Latent Heat Flux over the Global
Oceans,” Journal of Climate, Vol. 16, 2003, pp. 637-656.
doi:10.1175/1520-0442(2003)016<0637:SEOWSA>2.0.
CO;2
[4] P. Schlussel and A. Albert, “Latent Heat Flux at the Sea
Surface Retrieved from Combined TMI and VIRS Meas-
urements of TRMM,” International Journal of Remote
Sensing, Vol. 22, No. 10, 2001, pp. 1975-1998.
[5] C. W. Fairall, E. F. Bradley, D. P. Rogers, J. B. Edson
and G. S. Young, “Bulk Parameterization of Air-Sea
Fluxes for Tropical Ocean-Global Atmosphere Cou-
pled-Ocean Atmosphere Research Experiment,” Journal
of Geophysical Research, Vol. 101, No. C2, 1996, pp.
3747-3764. doi:10.1029/95JC03205
[6] R. M. Gairola, N. Gautam and P. C. Pandey, “Study of
Latent Heat Flues and Associated Oceanic and Atmos-
pheric Parameters over the Arabian Sea and Bay of Ben-
gal Using Seasat-SMMR Data,” Indian Journal of Radio
and Space Physics, Vol. 21, 1992, pp. 143-148.
[7] B. Simon and P. S. Desai, “Equatorial Indian Ocean
Evaporation Estimates from Operational Meteorological
Satellites and Some Inferences in the Context of Mon-
soon Onset and Activity,” Boundary-Layer Meteorology,
Vol. 37, 1986, pp. 37-52.
doi:10.1007/BF00122755
[8] W. T. Liu, K. B. Katsaros and J. A. Businger, “Bulk
Parameterization of Air-Sea Exchanges of Heat and Wa-
ter Vapor Including the Molecular Constraints at the In-
terface,” Journal of Atmospheric Sciences, Vol. 36, 1979,
pp. 1722-1735.
doi:10.1175/1520-0469(1979)036<1722:BPOASE>2.0.C
O;2
[9] R. Singh, C. M. Kishtawal, P. K. Pal and P. C. Joshi,
“Surface Heat Fluxes over Global Oceans Exclusively

233 S. PRAKASH ET AL.
from Satellite Observations,” Monthly Weather Review,
Vol. 134, 2006, pp. 965-980. doi:10.1175/MWR3119.1
[10] R. Singh, P. C. Joshi and C. M. Kishtawal, “A New Tech-
nique for Estimation of Surface Latent Heat Fluxes Using
Satellite-Based Observations,” Monthly Weather Review,
Vol. 133, 2005, pp. 2692-2710.
doi:10.1175/MWR2993.1
[11] N. Gautam, S. Basu, R. M. Gairola and P. C. Pandey, “A
Curiosity Regarding the Relation between Surface Level
Humidity and Precipitable Water,” Boundary-Layer Me-
teorology, Vol. 60, 1992, pp. 179-184.
doi:10.1007/BF00122067
[12] N. Gautam, S. Basu, C. M. Kishtawal, R. M. Gairola and
P. C. Pandey, “A Critical Assessment of the Q-W Rela-
tion and a Parameterization Relation for Computing La-
tent Heat Fluxes over the Indian Ocean,” International
Journal of Remote Sensing, Vol. 16, No. 18, 1995, pp.
3649-3664. doi:10.1080/01431169508954652
[13] S. A. Hsu and B. W. Blanchard, “The Relationship be-
tween Total Precipitable Water and Surface Level Hu-
midity over the Sea Surface: A Further Evaluation,”
Journal of Geophysical Research, Vol. 94, 1989, pp.
14539-14545. doi:10.1029/JC094iC10p14539
[14] W. T. Liu, “Statistical Relation between Monthly Mean
Precipitable Water and Surface Level Humidity over
Global Oceans,” Monthly Weather Review, Vol. 114,
1986, pp. 1591-1602.
doi:10.1175/1520-0493(1986)114<1591:SRBMMP>2.0.
CO;2
[15] S. R. Smith, P. J. Hughes and M. A. Bourassa, “A Com-
parison of Nine Monthly Air-Sea Flux Products,” Inter-
national Journal of Climatology, Vol. 31, 2011, pp.
1002-1037. doi:10.1002/joc.2225
[16] D. Bourras, “Comparison of Five Satellite-Derived Latent
Heat Flux Products to Moored Buoy Data,” Journal of
Climate, Vol. 19, 2006, pp. 6291-6313.
doi:10.1175/JCLI3977.1
[17] S. H. Chou, E. Nelkin, J. Ardizzone and R. M. Atlas, “A
Comparison of Latent Heat Fluxes over Global Oceans
for Four Flux Products,” Journal of Climate, Vol. 17,
2004, pp. 3973-3989.
doi:10.1175/1520-0442(2004)017<3973:ACOLHF>2.0.C
O;2
[18] J. Schulz, J. Meywerk, S. Ewald and P. Schlussel,
“Evaluation of Satellite-Derived Latent Heat Fluxes,”
Journal of Climate, Vol. 10, 1997, pp. 2782-2795.
doi:10.1175/1520-0442(1997)010<2782:EOSDLH>2.0.C
O;2
[19] S. Prakash, C. Mahesh, R. M. Gairola and P. K. Pal, “Es-
timation of Indian Summer Monsoon Rainfall Using
Kalpana-1 VHRR Data and Its Validation Using Rain
Gauge and GPCP Data,” Meteorology and Atmospheric
Physics, Vol. 110, 2010, pp. 45-57.
doi:10.1007/s00703-010-0106-8
[20] A. Mishra, R. M. Gairola, A. K. Varma and V. K. Agar-
wal, “Remote Sensing of Precipitation over Indian Land
and Oceanic Regions by Synergistic Use of Multi-Satel-
lite Sensors,” Journal of Geophysical Research, Vol. 115,
2010, D08106.
doi:10.1029/2009JD012157
[21] K. Aonashi, J. Awaka, M. Hirose, T. Kozu, T. Kubota, G.
Liu, S. Shige, S. Kida, S. Seto, N. Takahashi and Y. N.
Takayabu, “GSMaP Passive Microwave Precipitation Re-
trieval Algorithm: Algorithm Description and Valida-
tion,” Journal of Meteorological Society of Japan, Vol.
87A, 2009, pp. 119-136. doi:10.2151/jmsj.87A.119
[22] C. K. Kidd, D. R. Kniveton, M. C. Todd and T. J. Bel-
lerby, “Satellite Rainfall Estimation Using Combined
Pas- sive Microwave and Infrared Algorithms,” Journal
of Hydrometeorology, Vol. 4, 2003, pp. 1088-1104.
doi:10.1175/1525-7541(2003)004<1088:SREUCP>2.0.C
O;2
[23] A. K. Mitra, A. K. Bohra, M. N. Rajeevan and T. N.
Krishnamurti, “Daily Indian Precipitation Analysis
Formed from a Merge of Rain-Gauge Data with the
TRMM TMPA Satellite-Derived Rainfall Estimates,”
Journal of the Meteorological Society of Japan, Vol. 87A,
2009, pp. 265-279. doi:10.2151/jmsj.87A.265
[24] G. J. Huffman, R. F. Adler, D. T. Bolvin, G. Gu, E. J.
Nelkin, K. P. Bowman, Y. Hong, E. F. Stocker and D. B.
Wolff, “The TRMM Multi-Satellite Precipitation Analy-
sis: Quasi-Global, Multiyear, Combined Sensor Precipi-
tation Estimation at Finer Scales,” Journal of Hydrome-
teorology, Vol. 8, 2007, pp. 38-55.
doi:10.1175/JHM560.1
[25] R. F. Adler, G. J. Huffman, A. Chang, R. Ferraro, P. Xie,
J. Janowiak, B. Rudolf, U. Schneider, S. Curtis, D.
Bolvin, A. Gruber, J. Susskind and P. Arkin, “The Ver-
sion-2 Global Precipitation Climatology Project (GPCP)
Monthly Precipitation Analysis (1979-Present),” Journal
of Hydrometeorology, Vol. 4, 2003, pp. 1147-1167.
doi:10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.C
O;2
[26] C. A. Schlosser and P. R. Houser, “Assessing a Satel-
lite-Era Perspective of the Global Water Cycle,” Journal
of Climate, Vol. 20, 2007, pp. 1316-1338.
doi:10.1175/JCLI4057.1
[27] A. Andersson, C. Klepp, K. Fennig, S. Bakan, H. Grassl
and J. Schulz, “Evaluation of HOAPS-3 Ocean Surface
Freshwater Flux Components,” Journal of Applied Mete-
orology and Climatology, Vol. 50, 2011, pp. 379-398.
doi:10.1175/2010JAMC2341.1
[28] A. Andersson, K. Fennig, C. Klepp, S. Bakan, H. Grassl
and J. Schulz, “The Hamburg Ocean Atmosphere Pa-
rameters and Fluxes from Satellite Data-HOAPS-3,”
Earth System Sciences Data and Discussions, Vol. 3,
2010, pp. 143-194. doi:10.5194/essdd-3-143-2010
[29] C. L. Gentemann, F. J. Wentz, C. A. Mears and D. K.
Smith, “In situ Validation of Tropical Rainfall Measuring
Mission Microwave Sea Surface Temperatures,” Journal
of Geophysical Research, Vol. 109, 2004, C04021.
doi:10.1029/2003JC002092
[30] F. J. Wentz, C. L. Gentemann, D. K. Smith and D. B.
Chelton, “Satellite Measurements of Sea-Surface Tem-
perature through Clouds,” Science, Vol. 288, 2000, pp.

S. PRAKASH ET AL.
234
847-850. doi:10.1126/science.288.5467.847
[31] F. J. Wentz, “A Well Calibrated Ocean Algorithm for
Special Sensor Microwave/Imager,” Journal of Geo-
physical Research, Vol. 102, No. C4, 1997, pp. 8703-
8718. doi:10.1029/96JC01751
[32] M. J. McPhaden, A. J. Busalacchi, R. Cheney, J. R.
Donguy, K. S. Gage, D. Halpern, M. Ji, P. Julian, G.
Meyers, G. T. Mitchum, P. P. Niiler, J. Picaut, R. W.
Reynolds, N. Smith and K. Takeuchi, ‘The Tropical
Ocean-Global Atmosphere (TOGA) Observing System:
A Decade of Progress,” Journal of Geophysical Research,
Vol. 103, 1998, pp. 14169-14240.
doi:10.1029/97JC02906
[33] B. Bourles, R. Lumpkin, M. J. McPhaden, F. Hernandez,
P. Nobre, E. Campos, L. Yu, S. Planton, A. Busalacchi, A.
D. Moura, J. Servain and J. Trotte, “The PIRATA Pro-
gram: History, Accomplishments and Future Directions,”
Bulletin of American Meteorological Society, Vol. 89,
1998, pp. 1111-1125. doi:10.1175/2008BAMS2462.1
[34] M. J. McPhaden, G. Meyers, K. Ando, Y. Masumoto, V.
S. N. Murty, M. Ravichandran, F. Syamsudin, J. Vialard,
L. Yu and W. Yu, ‘RAMA The Research Moored Array
for African-Asian-Australian Monsoon Analysis and Pre-
diction,” Bulletin of American Meteorological Society,
Vol. 90, 2009, pp. 459-480.
doi:10.1175/2008BAMS2608.1
[35] R. M. Gairola, S. Pokhrel, A. K. Varma, A. Sarkar and V.
K. Agarwal, “Evaporation and Precipitation Estimates
from TRMM Microwave Measurements,” International
Conference on Monsoon, Bangalore, 2007.
[36] T. A. Fan and B. Lin, “Latent Heat Flux at the Sea Surface
Estimated from the TRMM Satellite,” American Geophysi-
cal Union, Fall Meeting, Abstract #OS21C-1246, 2004.
[37] D. Halpern and C. W. Hung, “Satellite Observations of
the Southeast Pacific Inter-Tropical Convergence Zone
during 1993-1998,” Journal of Geophysical Research,
Vol. 106, No. D22, 2001, pp. 28107-28112.
doi:10.1029/2000JD000056