Full Description
This book introduces the reader to a new method of data assimilation with deterministic constraints (exact satisfaction of dynamic constraints)—an optimal assimilation strategy called Forecast Sensitivity Method (FSM), as an alternative to the well-known four-dimensional variational (4D-Var) data assimilation method. 4D-Var works with a forward in time prediction model and a backward in time tangent linear model (TLM). The equivalence of data assimilation via 4D-Var and FSM is proven and problems using low-order dynamics clarify the process of data assimilation by the two methods. The problem of return flow over the Gulf of Mexico that includes upper-air observations and realistic dynamical constraints gives the reader a good idea of how the FSM can be implemented in a real-world situation.
Contents
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.
