Abstract
Despite the increased complexity in the model representations, it is often
the case that comprehensive dynamical models show poor results when compared
to observational data. In four-dimensional variational data assimilation
(4D-Var) a minimization algorithm is used to find the value of the model
parameters such that an optimal fit between the model prediction and observations,
scattered in time, is achieved. For large-scale models, the minimization
of the cost functional is a very intensive computational process.
The adjoint modeling is presented as a feasible tool to evaluate the
sensitivity of a scalar response function with respect to a large number
of model parameters. The use of a second order adjoint model to obtain
Hessian information is shown to be of benefit for ill conditioned optimization
problems.
A research area of major interest is the design of an adaptive observational
network. Expensive field-deployed resources (facilities and people) can
be utilized more effectively and science success can be maximized by an
optimal allocation of the observational resources. A new adjoint approach
to the adaptive observations problem is presented and its potential benefits
are illustrated in a comparative analysis with traditional methods based
on singular vectors and gradient sensitivity.
Numerical results are shown for nonlinear chemical reactions systems
and atmospheric circulation models.