831
As mentioned in the Introduction, the split-window approach is currently utilized to produce
surface temperature retrievals in terms of a linear combination of brightness temperatures measured in AVHRR
Channels 4 and 5, according to, T s = T 4 + a (T 4 -T 5 ) + p, where a and P are the split-window coefficienst,
which depend only on spectral emissivities and not on atmospheric conditions (Becker and Li, 1990). However
this is only true for a relatively dry atmosphere since the linear approximations with respect to water vapor
content introduced in the derivation of local split-window method (Becker 1987) are true in this case but not in
case of a wet atmosphere. In order to correct for the nonlinear effects and make the split-window method
applicable to most atmospheric situations encountered in the world, the split-window coefficients could be varied
with the classes of atmospheric humidity. This can be made, dividing the 60 atmospheres extracted from TIGR
(see above section) into 4 classes with respect to channel 5 transmission ( 15 ), and using a least-squares fit
method for each class. In this way the coefficients for each class of atmosphere can be obtained, leading to an
improved split-window with water vapour corrections (Li et al., 1994).
To use this improved split window method, we have to classify the atmosphere, with the help
of the covariance and the variance of brightness temperatures measured in channels 4 and 5 of AVHRR. It should
be noted that, if emissivity is known, using any of the more recent split-window algorithms (Sobrino et al.,
1993; Coll et al., 1994, Li et al., 1994) one can obtain the land surface temperature to an accuracy of 1 K.
5-CONCLUSIONS
For both prediction and detection, the general circulation models need ST data that cannot be given by the
traditional techniques. The main problems are the lack of measurements and the low accuracy of the
observations. Substantial differences exist between coincident observations from VOP (Voluntary Observing
Program) ships of the World Meteorological Organization (WMO) and from NOAA moored buoys even when
they are in close proximity to one another (Wilkerson and Earle, 1990). To solve for these shortcomings the best
solution is to obtain this data (ST) from thermal infrared satellite imagery. However this constitutes a difficult
task that has been severely limited because of the difficulty in evaluating the effects of the atmosphere. The
present paper should help solving this drawback through the development of a technique that permits modeling
the transmittance ratio, the channel atmospheric transmittance and the total water vapor from brightness
temperatures measured at Channels 4 and 5 of AVHRR on board NOAA-11 satellite alone. The technique relies
on the simple condition that the atmosphere is unchanged over the neighboring points where the surface
temperature and emissivity changes. This permits derive a split-window algorithm which results in an
improvement over the standard algorithms for relating ST and the brightness temperatures measured by the
satellite borne sensor (T 4 and T 5 ).
Although it is clear that even more statistics will be necessary to test the technique, the
applications to real and simulated data given in this paper show that the technique is promising and could be
implemented in a production environment necessary to document global climate change.
ACKNOWLEDGMENTS
The authors wish to express their gratitude to the Laboratoire de Météorologie Dynamique (Paris, France) for
supplying the TIGR data sets, and to the Air Force Geophysics Laboratory (Massachusetts, USA) for supplying
the LOWTRAN-7 computer code. We also thank M. Arbelo for providing the TOVS data and C. Badenas for
assistance in the processing of AVHRR data. The work was supported in part by the Commission of the
European Communities (Projects No. EV5V-CT91-0033/0035).
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