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Principles of Data Assimilation

Principles of Data Assimilation
Author: Seon Ki Park
Publisher: Cambridge University Press
Total Pages: 413
Release: 2022-09-29
Genre: Science
ISBN: 1108923895

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Data assimilation is theoretically founded on probability, statistics, control theory, information theory, linear algebra, and functional analysis. At the same time, data assimilation is a very practical subject, given its goal of estimating the posterior probability density function in realistic high-dimensional applications. This puts data assimilation at the intersection between the contrasting requirements of theory and practice. Based on over twenty years of teaching courses in data assimilation, Principles of Data Assimilation introduces a unique perspective that is firmly based on mathematical theories, but also acknowledges practical limitations of the theory. With the inclusion of numerous examples and practical case studies throughout, this new perspective will help students and researchers to competently interpret data assimilation results and to identify critical challenges of developing data assimilation algorithms. The benefit of information theory also introduces new pathways for further development, understanding, and improvement of data assimilation methods.


Data Assimilation for the Geosciences

Data Assimilation for the Geosciences
Author: Steven J. Fletcher
Publisher: Elsevier
Total Pages: 978
Release: 2017-03-10
Genre: Science
ISBN: 0128044845

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Data Assimilation for the Geosciences: From Theory to Application brings together all of the mathematical,statistical, and probability background knowledge needed to formulate data assimilation systems in one place. It includes practical exercises for understanding theoretical formulation and presents some aspects of coding the theory with a toy problem. The book also demonstrates how data assimilation systems are implemented in larger scale fluid dynamical problems related to the atmosphere, oceans, as well as the land surface and other geophysical situations. It offers a comprehensive presentation of the subject, from basic principles to advanced methods, such as Particle Filters and Markov-Chain Monte-Carlo methods. Additionally, Data Assimilation for the Geosciences: From Theory to Application covers the applications of data assimilation techniques in various disciplines of the geosciences, making the book useful to students, teachers, and research scientists. Includes practical exercises, enabling readers to apply concepts in a theoretical formulation Offers explanations for how to code certain parts of the theory Presents a step-by-step guide on how, and why, data assimilation works and can be used


Data Assimilation

Data Assimilation
Author: Kody Law
Publisher: Springer
Total Pages: 256
Release: 2015-09-05
Genre: Mathematics
ISBN: 3319203258

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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.


Principles of Data Assimilation

Principles of Data Assimilation
Author: Seon Ki Park
Publisher: Cambridge University Press
Total Pages: 413
Release: 2022-09-29
Genre: Science
ISBN: 1108831761

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A unique combination of both theoretical and practical aspects of data assimilation with examples and exercises for students.


Data Assimilation and Control: Theory and Applications in Life Sciences

Data Assimilation and Control: Theory and Applications in Life Sciences
Author: Axel Hutt
Publisher: Frontiers Media SA
Total Pages: 116
Release: 2019-08-16
Genre:
ISBN: 2889459853

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The understanding of complex systems is a key element to predict and control the system’s dynamics. To gain deeper insights into the underlying actions of complex systems today, more and more data of diverse types are analyzed that mirror the systems dynamics, whereas system models are still hard to derive. Data assimilation merges both data and model to an optimal description of complex systems’ dynamics. The present eBook brings together both recent theoretical work in data assimilation and control and demonstrates applications in diverse research fields.


Dynamic Data Assimilation

Dynamic Data Assimilation
Author: John M. Lewis
Publisher: Cambridge University Press
Total Pages: 601
Release: 2006-08-03
Genre: Mathematics
ISBN: 0521851556

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Computational Methods for Data Evaluation and Assimilation

Computational Methods for Data Evaluation and Assimilation
Author: Dan Gabriel Cacuci
Publisher: CRC Press
Total Pages: 372
Release: 2016-04-19
Genre: Mathematics
ISBN: 1584887362

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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 interdiscipli


The Statistical Physics of Data Assimilation and Machine Learning

The Statistical Physics of Data Assimilation and Machine Learning
Author: Henry D. I. Abarbanel
Publisher: Cambridge University Press
Total Pages: 207
Release: 2022-02-17
Genre: Computers
ISBN: 1316519635

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The theory of data assimilation and machine learning is introduced in an accessible manner for undergraduate and graduate students.


Probabilistic Forecasting and Bayesian Data Assimilation

Probabilistic Forecasting and Bayesian Data Assimilation
Author: Sebastian Reich
Publisher: Cambridge University Press
Total Pages: 308
Release: 2015-05-14
Genre: Computers
ISBN: 1316299422

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In this book the authors describe the principles and methods behind probabilistic forecasting and Bayesian data assimilation. Instead of focusing on particular application areas, the authors adopt a general dynamical systems approach, with a profusion of low-dimensional, discrete-time numerical examples designed to build intuition about the subject. Part I explains the mathematical framework of ensemble-based probabilistic forecasting and uncertainty quantification. Part II is devoted to Bayesian filtering algorithms, from classical data assimilation algorithms such as the Kalman filter, variational techniques, and sequential Monte Carlo methods, through to more recent developments such as the ensemble Kalman filter and ensemble transform filters. The McKean approach to sequential filtering in combination with coupling of measures serves as a unifying mathematical framework throughout Part II. Assuming only some basic familiarity with probability, this book is an ideal introduction for graduate students in applied mathematics, computer science, engineering, geoscience and other emerging application areas.


Data Assimilation

Data Assimilation
Author: Geir Evensen
Publisher: Springer Science & Business Media
Total Pages: 285
Release: 2006-12-22
Genre: Science
ISBN: 3540383018

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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.