Forecast error correction using dynamic data assimilation / / by Sivaramakrishnan Lakshmivarahan, John M. Lewis, Rafal Jabrzemski

• Record Nr.: UNINA9910135974803321
• Autore: Lakshmivarahan Sivaramakrishnan
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017
• Edizione: [1st ed. 2017.]
• Descrizione fisica: 1 online resource (XVI, 270 p. 125 illus., 104 illus. in color.)
• Collana: Springer Atmospheric Sciences, , 2194-5217
• Disciplina: 004
• Soggetti: Data mining; Computer simulation; Computers; Atmospheric science; Geology—Statistical methods; Data Mining and Knowledge Discovery; Simulation and Modeling; Models and Principles; Atmospheric Sciences; Quantitative Geology
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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. .
• Sommario/riassunto: 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. .