Forecast Error Correction using Dynamic Data Assimilation

Beschreibung

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.          
                                 
Introduces the reader to a new method of dynamic data assimilation called Forward Sensitivity Method (FSM) through theory and application The connection between the FSM and the well-known adjoint sensitivity or four-dimensional variational (4D-Var) method is clarified The predictability limit of a dynamical model as an outcome of FSM is established FSM and 4D-Var assimilation methods are demonstrated through problem solving with low-order dynamical systems and a real-world meteorological problem Includes supplementary material: sn.pub/extras

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Sivaramakrishnan Lakshmivarahan

Themen in »Forecast Error Correction using Dynamic Data Assimilation«

Adjoint Method Adjoint Sensitivity Analysis Data Assimilation Dynamic Predictability FSM Fitting Data Forecast Sensitivity Method Forward Sensitivity Model Errors Predictability Limits quantitative geology

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