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010   $a3-319-22410-7
024 7 $a10.1007/978-3-319-22410-7
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035   $a(DE-He213)978-3-319-22410-7
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035   $a(PPN)190523867
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100   $a20150916d2016      u|         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aData assimilation: mathematical concepts and instructive examples /$fby Rodolfo Guzzi
205   $a1st ed. 2016.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016.
215   $a1 online resource (140 p.)
225 1 $aSpringerBriefs in Earth Sciences,$x2191-5369
300   $aDescription based upon print version of record.
311 08$a3-319-22409-3 
320   $aIncludes bibliographical references at the end of each chapters and index.
327   $aPreface -- 1. Introduction through historical perspective -- 2. Representation of the physical system -- 3. Sequential interpolation -- 4. Advanced data assimilation methods -- 5. Applications -- A. Appendix.
330   $aThis book endeavours to give a concise contribution to understanding the data assimilation and related methodologies. The mathematical concepts and related algorithms are fully presented, especially for those facing this theme for the first time.  The first chapter gives a wide overview of the data assimilation steps starting from Gauss' first methods to the most recent as those developed under the Monte Carlo methods.  The second chapter treats the representation of the physical  system as an ontological basis of the problem. The third chapter deals with the classical Kalman filter, while the fourth chapter deals with the advanced methods based on  recursive Bayesian Estimation. A special chapter, the fifth, deals with the possible applications, from the first Lorenz model, passing trough the biology and medicine up to planetary assimilation, mainly on Mars. This book serves both teachers and college students, and other interested parties providing the algorithms and formulas to manage the data assimilation everywhere a dynamic system is present.
410  0$aSpringerBriefs in Earth Sciences,$x2191-5369
606   $aComputer simulation
606   $aPhysical geography
606   $aMathematical physics
606   $aEnvironmental sciences
606   $aWater quality
606   $aWater$xPollution
606   $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000
606   $aEarth System Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/G35000
606   $aTheoretical, Mathematical and Computational Physics$3https://scigraph.springernature.com/ontologies/product-market-codes/P19005
606   $aEnvironmental Science and Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/G37000
606   $aWater Quality/Water Pollution$3https://scigraph.springernature.com/ontologies/product-market-codes/212000
615  0$aComputer simulation.
615  0$aPhysical geography.
615  0$aMathematical physics.
615  0$aEnvironmental sciences.
615  0$aWater quality.
615  0$aWater$xPollution.
615 14$aSimulation and Modeling.
615 24$aEarth System Sciences.
615 24$aTheoretical, Mathematical and Computational Physics.
615 24$aEnvironmental Science and Engineering.
615 24$aWater Quality/Water Pollution.
676   $a519.5
700   $aGuzzi$b Rodolfo$4aut$4http://id.loc.gov/vocabulary/relators/aut$032371
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910255012503321
996   $aData Assimilation: Mathematical Concepts and Instructive Examples$92007734
997   $aUNINA