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Data Assimilation for the Geosciences

Data Assimilation for the Geosciences
Author: Steven J. Fletcher
Publisher: Elsevier
Total Pages: 1130
Release: 2022-11-16
Genre: Science
ISBN: 0323972535

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Data Assimilation for the Geosciences: From Theory to Application, Second Edition brings together all of the mathematical and statistical background knowledge needed to formulate data assimilation systems into one place. It includes practical exercises enabling readers to apply theory in both a theoretical formulation as well as teach them how to code the theory with toy problems to verify their understanding. It also demonstrates how data assimilation systems are implemented in larger scale fluid dynamical problems related to land surface, the atmosphere, ocean and other geophysical situations. The second edition of Data Assimilation for the Geosciences has been revised with up to date research that is going on in data assimilation, as well as how to apply the techniques. The new edition features an introduction of how machine learning and artificial intelligence are interfacing and aiding data assimilation. In addition to appealing to students and researchers across the geosciences, this now also appeals to new students and scientists in the field of data assimilation as it will now have even more information on the techniques, research, and applications, consolidated into one source. Includes practical exercises and solutions enabling readers to apply theory in both a theoretical formulation as well as enabling them to code theory Provides the mathematical and statistical background knowledge needed to formulate data assimilation systems into one place New to this edition: covers new topics such as Observing System Experiments (OSE) and Observing System Simulation Experiments; and expanded approaches for machine learning and artificial intelligence


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.


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.


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 for Atmospheric, Oceanic and Hydrologic Applications (Vol. II)

Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II)
Author: Seon Ki Park
Publisher: Springer Science & Business Media
Total Pages: 736
Release: 2013-05-22
Genre: Science
ISBN: 3642350887

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This book contains the most recent progress in data assimilation in meteorology, oceanography and hydrology including land surface. It spans both theoretical and applicative aspects with various methodologies such as variational, Kalman filter, ensemble, Monte Carlo and artificial intelligence methods. Besides data assimilation, other important topics are also covered including targeting observation, sensitivity analysis, and parameter estimation. The book will be useful to individual researchers as well as graduate students for a reference in the field of data assimilation.


Data Assimilation: Methods, Algorithms, and Applications

Data Assimilation: Methods, Algorithms, and Applications
Author: Mark Asch
Publisher: SIAM
Total Pages: 306
Release: 2016-12-29
Genre: Mathematics
ISBN: 1611974542

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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.


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.