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[null,"1.","Record Nr.",null,"UNINA9910135974803321"]
[null,null,"Autore",null,"Lakshmivarahan Sivaramakrishnan"]
[null,null,"Titolo",null,"Forecast error correction using dynamic data assimilation / / by Sivaramakrishnan Lakshmivarahan, John M. Lewis, Rafal Jabrzemski"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017"]
[null,null,"Edizione",null,"[1st ed. 2017.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XVI, 270 p. 125 illus., 104 illus. in color.)"]
[null,null,"Collana",null,"Springer Atmospheric Sciences, , 2194-5217"]
[null,null,"Disciplina",null,"004"]
[null,null,"Soggetti",null,"Data mining"]
[{"$nulls":4},"Computer simulation"]
[{"$nulls":4},"Computers"]
[{"$nulls":4},"Atmospheric science"]
[{"$nulls":4},"Geology—Statistical methods"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Simulation and Modeling"]
[{"$nulls":4},"Models and Principles"]
[{"$nulls":4},"Atmospheric Sciences"]
[{"$nulls":4},"Quantitative Geology"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Part I Theory -- Introduction -- Dynamics of evolution of first- and second-order forward sensitivity: discrete time and continuous time -- Estimation of control errors using forward sensitivities: FSM with single and multiple observations -- Relation to adjoint sensitivity and impact of observation -- Estimation of model errors using Pontryagin’s Maximum Principle- its relation to 4-D VAR and hence FSM -- FSM and predictability - Lyapunov index -- Part II Applications -- Mixed-layer model - the Gulf of Mexico problem -- Lagrangian data assimilation -- Conclusions -- Appendix -- Index. ."]
[null,null,null,"Sommario/riassunto","This book introduces the reader to a new method of data assimilation with deterministic constraints (exact satisfaction of dynamic constraints)—an optimal assimilation strategy called Forecast Sensitivity Method (FSM), as an alternative to the well-known four-dimensional variational (4D-Var) data assimilation method. 4D-Var works with a forward in time prediction model and a backward in time tangent linear "]
[{"$nulls":4},"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. ."]