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Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filters and smoothers. It is demonstrated how the different methods can be derived from a common theoretical basis, as well as how they differ and/or are related to each other, and which properties characterize them, using several examples. It presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurements. The mathematics level is modest, although it requires knowledge of basic spatial statistics, Bayesian statistics, and calculus of variations. Readers will also appreciate the introduction to the mathematical methods used and detailed derivations, which should be easy to follow, are given throughout the book. The codes used in several of the data assimilation experiments are available on a web page. The focus on ensemble methods, such as the ensemble Kalman filter and smoother, also makes it a solid reference to the derivation, implementation and application of such techniques. Much new material, in particular related to the formulation and solution of combined parameter and state estimation problems and the general properties of the ensemble algorithms, is available here for the first time.

Stochastic processes --- Kalman filtering. --- Numerical weather forecasting. --- Prediction theory. --- Stochastic processes. --- Kalman filtering --- Mathematical Statistics --- Mathematics --- Physical Sciences & Mathematics --- Earth sciences. --- Mathematical models. --- Probabilities. --- Physics. --- Applied mathematics. --- Engineering mathematics. --- Earth Sciences. --- Earth Sciences, general. --- Probability Theory and Stochastic Processes. --- Theoretical, Mathematical and Computational Physics. --- Mathematical Modeling and Industrial Mathematics. --- Appl.Mathematics/Computational Methods of Engineering. --- Random processes --- Probabilities --- Filtering, Kalman --- Control theory --- Estimation theory --- Prediction theory --- Kalman, filtrage de --- Processus stochastiques --- EPUB-LIV-FT LIVTERRE SPRINGER-B --- Geography. --- Distribution (Probability theory. --- Mathematical and Computational Engineering. --- Engineering --- Engineering analysis --- Mathematical analysis --- Distribution functions --- Frequency distribution --- Characteristic functions --- Cosmography --- Earth sciences --- World history --- Mathematical physics. --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Geosciences --- Environmental sciences --- Physical sciences --- Models, Mathematical --- Simulation methods --- Physical mathematics --- Physics

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Operational research. Game theory --- Mathematics --- Mathematical physics --- Geology. Earth sciences --- Engineering sciences. Technology --- Planning (firm) --- Computer. Automation --- analyse (wiskunde) --- stochastische analyse --- informatica --- mathematische modellen --- wiskunde --- geografie --- geologie --- ingenieurswetenschappen --- fysica --- kansrekening

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• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filters and smoothers. It is demonstrated how the different methods can be derived from a common theoretical basis, as well as how they differ and/or are related to each other, and which properties characterize them, using several examples. It presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurements. The mathematics level is modest, although it requires knowledge of basic spatial statistics, Bayesian statistics, and calculus of variations. Readers will also appreciate the introduction to the mathematical methods used and detailed derivations, which should be easy to follow, are given throughout the book. The codes used in several of the data assimilation experiments are available on a web page. The focus on ensemble methods, such as the ensemble Kalman filter and smoother, also makes it a solid reference to the derivation, implementation and application of such techniques. Much new material, in particular related to the formulation and solution of combined parameter and state estimation problems and the general properties of the ensemble algorithms, is available here for the first time. The 2nd edition includes a partial rewrite of Chapters 13 an 14, and the Appendix. In addition, there is a completely new Chapter on "Spurious correlations, localization and inflation", and an updated and improved sampling discussion in Chap 11.

Operational research. Game theory --- Mathematics --- Mathematical physics --- Geology. Earth sciences --- Engineering sciences. Technology --- Planning (firm) --- Computer architecture. Operating systems --- analyse (wiskunde) --- toegepaste wiskunde --- theoretische fysica --- stochastische analyse --- mathematische modellen --- computerprogramma's --- mijnbouw --- geografie --- geologie --- ingenieurswetenschappen --- kansrekening

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