Probabilistic Forecasting and Bayesian Data Assimilation

Author: Sebastian Reich,Colin Cotter

Publisher: Cambridge University Press

ISBN: 1107069394

Category: Computers

Page: 306

View: 2836

Covers key ideas and concepts. Ideal introduction for graduate students in any field where Bayesian data assimilation is applied.
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Data Assimilation

The Ensemble Kalman Filter

Author: Geir Evensen

Publisher: Springer Science & Business Media

ISBN: 3642037119

Category: Science

Page: 307

View: 8891

This book reviews popular data-assimilation methods, such as weak and strong constraint variational methods, ensemble filters and smoothers. The author shows how different methods can be derived from a common theoretical basis, as well as how they differ or are related to each other, and which properties characterize them, using several examples. Readers will appreciate the included introductory material and detailed derivations in the text, and a supplemental web site.
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An Introduction to Bayesian Scientific Computing

Ten Lectures on Subjective Computing

Author: Daniela Calvetti,E. Somersalo

Publisher: Springer Science & Business Media

ISBN: 0387733949

Category: Computers

Page: 202

View: 6104

This book has been written for undergraduate and graduate students in various disciplines of mathematics. The authors, internationally recognized experts in their field, have developed a superior teaching and learning tool that makes it easy to grasp new concepts and apply them in practice. The book’s highly accessible approach makes it particularly ideal if you want to become acquainted with the Bayesian approach to computational science, but do not need to be fully immersed in detailed statistical analysis.
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Data Assimilation

Making Sense of Observations

Author: William Lahoz,Boris Khattatov,Richard Menard

Publisher: Springer Science & Business Media

ISBN: 9783540747031

Category: Science

Page: 718

View: 9175

Data assimilation methods were largely developed for operational weather forecasting, but in recent years have been applied to an increasing range of earth science disciplines. This book will set out the theoretical basis of data assimilation with contributions by top international experts in the field. Various aspects of data assimilation are discussed including: theory; observations; models; numerical weather prediction; evaluation of observations and models; assessment of future satellite missions; application to components of the Earth System. References are made to recent developments in data assimilation theory (e.g. Ensemble Kalman filter), and to novel applications of the data assimilation method (e.g. ionosphere, Mars data assimilation).
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Data Assimilation

A Mathematical Introduction

Author: Kody Law,Andrew Stuart,Konstantinos Zygalakis

Publisher: Springer

ISBN: 3319203258

Category: Mathematics

Page: 242

View: 6000

This book provides a systematic treatment of the mathematical underpinnings of work in data assimilation, covering both theoretical and computational approaches. Specifically the authors develop a unified mathematical framework in which a Bayesian formulation of the problem provides the bedrock for the derivation, development and analysis of algorithms; the many examples used in the text, together with the algorithms which are introduced and discussed, are all illustrated by the MATLAB software detailed in the book and made freely available online. The book is organized into nine chapters: the first contains a brief introduction to the mathematical tools around which the material is organized; the next four are concerned with discrete time dynamical systems and discrete time data; the last four are concerned with continuous time dynamical systems and continuous time data and are organized analogously to the corresponding discrete time chapters. This book is aimed at mathematical researchers interested in a systematic development of this interdisciplinary field, and at researchers from the geosciences, and a variety of other scientific fields, who use tools from data assimilation to combine data with time-dependent models. The numerous examples and illustrations make understanding of the theoretical underpinnings of data assimilation accessible. Furthermore, the examples, exercises and MATLAB software, make the book suitable for students in applied mathematics, either through a lecture course, or through self-study.
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Data Assimilation: Methods, Algorithms, and Applications

Author: Mark Asch,Marc Bocquet,Maelle Nodet

Publisher: SIAM

ISBN: 1611974542

Category: Mathematics

Page: 306

View: 9268

Data assimilation is an approach that combines observations and model output, with the objective of improving the latter. This book places data assimilation into the broader context of inverse problems and the theory, methods, and algorithms that are used for their solution. It provides a framework for, and insight into, the inverse problem nature of data assimilation, emphasizing ?why? and not just ?how.? Methods and diagnostics are emphasized, enabling readers to readily apply them to their own field of study. Readers will find a comprehensive guide that is accessible to nonexperts; numerous examples and diverse applications from a broad range of domains, including geophysics and geophysical flows, environmental acoustics, medical imaging, mechanical and biomedical engineering, economics and finance, and traffic control and urban planning; and the latest methods for advanced data assimilation, combining variational and statistical approaches.
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An Introduction to Stochastic Dynamics

Author: Jinqiao Duan

Publisher: Cambridge University Press

ISBN: 1107075394

Category: Mathematics

Page: 307

View: 5984

An accessible introduction for applied mathematicians to concepts and techniques for describing, quantifying, and understanding dynamics under uncertainty.
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Atmospheric Modeling, Data Assimilation and Predictability

Author: Eugenia Kalnay

Publisher: Cambridge University Press

ISBN: 9780521796293

Category: Mathematics

Page: 341

View: 1745

This book, first published in 2002, is a graduate-level text on numerical weather prediction, including atmospheric modeling, data assimilation and predictability.
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Data-Driven Computational Methods

Parameter and Operator Estimations

Author: John Harlim

Publisher: Cambridge University Press

ISBN: 1108472478

Category: Computers

Page: 169

View: 7083

Describes computational methods for parametric and nonparametric modeling of stochastic dynamics. Aimed at graduate students, and suitable for self-study.
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A First Course in Continuum Mechanics

Author: Oscar Gonzalez,Andrew M. Stuart

Publisher: Cambridge University Press

ISBN: 0521886805

Category: Science

Page: 394

View: 1045

A concise account of classic theories of fluids and solids, for graduate and advanced undergraduate courses in continuum mechanics.
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Lectures on Vector Bundles

Author: J. Le Potier

Publisher: Cambridge University Press

ISBN: 9780521481823

Category:

Page: N.A

View: 1533

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Statistical Analysis in Climate Research

Author: Hans von Storch,Francis W. Zwiers

Publisher: Cambridge University Press

ISBN: 1139425099

Category: Science

Page: N.A

View: 1392

Climatology is, to a large degree, the study of the statistics of our climate. The powerful tools of mathematical statistics therefore find wide application in climatological research. The purpose of this book is to help the climatologist understand the basic precepts of the statistician's art and to provide some of the background needed to apply statistical methodology correctly and usefully. The book is self contained: introductory material, standard advanced techniques, and the specialised techniques used specifically by climatologists are all contained within this one source. There are a wealth of real-world examples drawn from the climate literature to demonstrate the need, power and pitfalls of statistical analysis in climate research. Suitable for graduate courses on statistics for climatic, atmospheric and oceanic science, this book will also be valuable as a reference source for researchers in climatology, meteorology, atmospheric science, and oceanography.
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A First Course in Combinatorial Optimization

Author: Jon Lee

Publisher: Cambridge University Press

ISBN: 9780521010122

Category: Business & Economics

Page: 211

View: 4234

A First Course in Combinatorial Optimization is a text for a one-semester introductory graduate-level course for students of operations research, mathematics, and computer science. It is a self-contained treatment of the subject, requiring only some mathematical maturity. Topics include: linear and integer programming, polytopes, matroids and matroid optimization, shortest paths, and network flows. Central to the exposition is the polyhedral viewpoint, which is the key principle underlying the successful integer-programming approach to combinatorial-optimization problems. Another key unifying topic is matroids. The author does not dwell on data structures and implementation details, preferring to focus on the key mathematical ideas that lead to useful models and algorithms. Problems and exercises are included throughout as well as references for further study.
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Data Assimilation

A Mathematical Introduction

Author: Kody Law,Andrew Stuart,Konstantinos Zygalakis

Publisher: Springer

ISBN: 3319203258

Category: Mathematics

Page: 242

View: 9290

This book provides a systematic treatment of the mathematical underpinnings of work in data assimilation, covering both theoretical and computational approaches. Specifically the authors develop a unified mathematical framework in which a Bayesian formulation of the problem provides the bedrock for the derivation, development and analysis of algorithms; the many examples used in the text, together with the algorithms which are introduced and discussed, are all illustrated by the MATLAB software detailed in the book and made freely available online. The book is organized into nine chapters: the first contains a brief introduction to the mathematical tools around which the material is organized; the next four are concerned with discrete time dynamical systems and discrete time data; the last four are concerned with continuous time dynamical systems and continuous time data and are organized analogously to the corresponding discrete time chapters. This book is aimed at mathematical researchers interested in a systematic development of this interdisciplinary field, and at researchers from the geosciences, and a variety of other scientific fields, who use tools from data assimilation to combine data with time-dependent models. The numerous examples and illustrations make understanding of the theoretical underpinnings of data assimilation accessible. Furthermore, the examples, exercises and MATLAB software, make the book suitable for students in applied mathematics, either through a lecture course, or through self-study.
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Microhydrodynamics, Brownian Motion, and Complex Fluids

Author: Michael D. Graham

Publisher: Cambridge University Press

ISBN: 110861440X

Category: Science

Page: N.A

View: 7973

This is an introduction to the dynamics of fluids at small scales, the physical and mathematical underpinnings of Brownian motion, and the application of these subjects to the dynamics and flow of complex fluids such as colloidal suspensions and polymer solutions. It brings together continuum mechanics, statistical mechanics, polymer and colloid science, and various branches of applied mathematics, in a self-contained and integrated treatment that provides a foundation for understanding complex fluids, with a strong emphasis on fluid dynamics. Students and researchers will find that this book is extensively cross-referenced to illustrate connections between different aspects of the field. Its focus on fundamental principles and theoretical approaches provides the necessary groundwork for research in the dynamics of flowing complex fluids.
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Nonlinear Data Assimilation

Author: Peter Jan Van Leeuwen,Yuan Cheng,Sebastian Reich

Publisher: Springer

ISBN: 3319183478

Category: Mathematics

Page: 118

View: 5262

This book contains two review articles on nonlinear data assimilation that deal with closely related topics but were written and can be read independently. Both contributions focus on so-called particle filters. The first contribution by Jan van Leeuwen focuses on the potential of proposal densities. It discusses the issues with present-day particle filters and explorers new ideas for proposal densities to solve them, converging to particle filters that work well in systems of any dimension, closing the contribution with a high-dimensional example. The second contribution by Cheng and Reich discusses a unified framework for ensemble-transform particle filters. This allows one to bridge successful ensemble Kalman filters with fully nonlinear particle filters, and allows a proper introduction of localization in particle filters, which has been lacking up to now.
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Mathematical Demography

Selected Papers

Author: David P. Smith,Nathan Keyfitz

Publisher: Springer Science & Business Media

ISBN: 3642358586

Category: Social Science

Page: 335

View: 1143

Mathematical demography is the centerpiece of quantitative social science. The founding works of this field from Roman times to the late Twentieth Century are collected here, in a new edition of a classic work by David R. Smith and Nathan Keyfitz. Commentaries by Smith and Keyfitz have been brought up to date and extended by Kenneth Wachter and Hervé Le Bras, giving a synoptic picture of the leading achievements in formal population studies. Like the original collection, this new edition constitutes an indispensable source for students and scientists alike, and illustrates the deep roots and continuing vitality of mathematical demography.
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Computational Methods for Data Evaluation and Assimilation

Author: Dan Gabriel Cacuci,Ionel Michael Navon,Mihaela Ionescu-Bujor

Publisher: CRC Press

ISBN: 1584887362

Category: Mathematics

Page: 373

View: 611

Data evaluation and data combination require the use of a wide range of probability theory concepts and tools, from deductive statistics mainly concerning frequencies and sample tallies to inductive inference for assimilating non-frequency data and a priori knowledge. Computational Methods for Data Evaluation and Assimilation presents interdisciplinary methods for integrating experimental and computational information. This self-contained book shows how the methods can be applied in many scientific and engineering areas. After presenting the fundamentals underlying the evaluation of experimental data, the book explains how to estimate covariances and confidence intervals from experimental data. It then describes algorithms for both unconstrained and constrained minimization of large-scale systems, such as time-dependent variational data assimilation in weather prediction and similar applications in the geophysical sciences. The book also discusses several basic principles of four-dimensional variational assimilation (4D VAR) and highlights specific difficulties in applying 4D VAR to large-scale operational numerical weather prediction models.
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