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Part I Theory
Introduction
Dynamics of evolution of first- and second-order forward sensitivity: discrete time and continuous time
Estimation of control errors using forward sensitivities: FSM with single and multiple observations
Relation to adjoint sensitivity and impact of observation
Estimation of model errors using Pontryagin?s Maximum Principle- its relation to 4-D VAR and hence FSM
FSM and predictability
Lyapunov index
Part II Applications
Mixed-layer model
the Gulf of Mexico problem
Lagrangian data assimilation
Conclusions
Appendix
Index. .

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