04182nam 22006975 450 991013597480332120220330190108.010.1007/978-3-319-39997-3(CKB)3710000000911456(DE-He213)978-3-319-39997-3(MiAaPQ)EBC4722672(PPN)196324343(EXLCZ)99371000000091145620161021d2017 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierForecast error correction using dynamic data assimilation /by Sivaramakrishnan Lakshmivarahan, John M. Lewis, Rafal Jabrzemski1st ed. 2017.Cham :Springer International Publishing :Imprint: Springer,2017.1 online resource (XVI, 270 p. 125 illus., 104 illus. in color.)Springer Atmospheric Sciences,2194-52173-319-39995-0 3-319-39997-7 Includes bibliographical references and index.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. .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. .Springer Atmospheric Sciences,2194-5217Data miningComputer simulationComputersAtmospheric scienceGeology—Statistical methodsData Mining and Knowledge Discoveryhttps://scigraph.springernature.com/ontologies/product-market-codes/I18030Simulation and Modelinghttps://scigraph.springernature.com/ontologies/product-market-codes/I19000Models and Principleshttps://scigraph.springernature.com/ontologies/product-market-codes/I18016Atmospheric Scienceshttps://scigraph.springernature.com/ontologies/product-market-codes/G36000Quantitative Geologyhttps://scigraph.springernature.com/ontologies/product-market-codes/G17030Data mining.Computer simulation.Computers.Atmospheric science.Geology—Statistical methods.Data Mining and Knowledge Discovery.Simulation and Modeling.Models and Principles.Atmospheric Sciences.Quantitative Geology.004Lakshmivarahan Sivaramakrishnanauthttp://id.loc.gov/vocabulary/relators/aut543455Lewis John Mauthttp://id.loc.gov/vocabulary/relators/autJabrzemski Rafalauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910135974803321Forecast Error Correction using Dynamic Data Assimilation2517109UNINA