| Abstract |
Global and regional circulation models incorporate our knowledge of the dynamics of the
Earth's atmosphere. They are used to predict the evolution of the weather and climate.
Mathematically, this system is represented by a set of partial differential equations
whose solution requires initial and boundary conditions. Limitations in the accuracy and
geographical distribution of these constraints, and intrinsic mathematical sensitivity to
these conditions do not allow the identification of a unique solution (prediction).
Additional observations on the system are thus used to constrain the forecasts of the
mathematical model to remain close to the observed state of the system. Ultimately,
these models are useful mainly to predict the future values of environmental variables or
to estimate these variables wherever and whenever they are not observed directly.
Current validation of global and regional climate models is based on comparison
between models outputs of standard meteorological fields and meteorological
observations. The main problem with traditional meteorological observations when used
to validate models is their poor representation of the grid-point average simulated by a
model. Now that new fields (radiative measurements made from space) are available,
models should produce the same fields as standard outputs to be compared with these
new observations. Remote sensing from space platforms provides a unique opportunity
to provide reliable accurate information in support of global and regional weather or
climate models. Space based platforms permit the systematic and repetitive observation
of the surface and the atmosphere, at resolutions generally higher than those used in
modeling. Remote sensing data can be exploited to provide the initial and boundary
conditions required to run climate models, to force these models to remain close to the
real atmospheric situation, or to evaluate the accuracy
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