January 27, 2021

Download Ebook Free Data Assimilation For The Geosciences

Data Assimilation for the Geosciences

Data Assimilation for the Geosciences
Author : Steven James Fletcher
Publisher : Elsevier
Release Date : 2017-03-10
Category : Science
Total pages :976
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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

Advanced Data Assimilation for Geosciences

Advanced Data Assimilation for Geosciences
Author : Eric Blayo
Publisher : Oxford University Press, USA
Release Date : 2014-10-30
Category : Science
Total pages :576
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In many applications of geophysics (weather forecast, study of climate evolution and variability), it is necessary to get the best possible estimate of the state of the system under study. In general, information about this system comes from observations and numerical models. However, none of these sources is perfect. Data assimilation designates the set of mathematical methods used to optimally combine observations with models, to fulfil the need of an accurateestimate of the system state. Because of the weather forecast problem in particular, the geophysical sciences have shaped a long history and a strong background on data assimilation, particularly with big and complex systems such as the atmosphere and the ocean. This book gathers notes from lecturesgiven during a three-week summer school on the fundamentals and the most recent developments of geophysical data assimilation.

Improvements to Ensemble Methods for Data Assimilation in the Geosciences

Improvements to Ensemble Methods for Data Assimilation in the Geosciences
Author : Patrick Nima Raanes
Publisher : Unknown
Release Date : 2015
Category :
Total pages :129
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Data Assimilation

Data Assimilation
Author : Kody Law,Andrew Stuart,Konstantinos Zygalakis
Publisher : Springer
Release Date : 2015-09-05
Category : Mathematics
Total pages :242
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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.

Data Assimilation: Methods, Algorithms, and Applications

Data Assimilation: Methods, Algorithms, and Applications
Author : Mark Asch,Marc Bocquet,Maelle Nodet
Publisher : SIAM
Release Date : 2016-12-29
Category : Mathematics
Total pages :306
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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 : Geir Evensen
Publisher : Springer Science & Business Media
Release Date : 2006-12-22
Category : Science
Total pages :280
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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.

Probabilistic Forecasting and Bayesian Data Assimilation

Probabilistic Forecasting and Bayesian Data Assimilation
Author : Sebastian Reich,Colin Cotter
Publisher : Cambridge University Press
Release Date : 2015-05-14
Category : Computers
Total pages :306
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Covers key ideas and concepts. Ideal introduction for graduate students in any field where Bayesian data assimilation is applied.

Nonlinear Data Assimilation

Nonlinear Data Assimilation
Author : Peter Jan Van Leeuwen,Yuan Cheng,Sebastian Reich
Publisher : Springer
Release Date : 2015-07-22
Category : Mathematics
Total pages :118
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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.

Data Assimilation

Data Assimilation
Author : Geir Evensen
Publisher : Springer Science & Business Media
Release Date : 2009-08-17
Category : Science
Total pages :307
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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.

Semi-Lagrangian Advection Methods and Their Applications in Geoscience

Semi-Lagrangian Advection Methods and Their Applications in Geoscience
Author : Steven J. Fletcher
Publisher : Elsevier
Release Date : 2019-11-18
Category : Science
Total pages :624
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Semi-Lagrangian Advection Methods and Their Applications in Geoscience provides a much-needed resource on semi-Lagrangian theory, methods, and applications. Covering a variety of applications, the book brings together developments of the semi-Lagrangian in one place and offers a comparison of semi-Lagrangian methods with Eulerian-based approaches. It also includes a chapter dedicated to difficulties of dealing with the adjoint of semi-Lagrangian methods and illustrates the behavior of different schemes for different applications. This allows for a better understanding of which schemes are most efficient, stable, consistent, and likely to introduce the minimum model error into a given problem. Beneficial for students learning about numerical approximations to advection, researchers applying these techniques to geoscientific modeling, and practitioners looking for the best approach for modeling, Semi-Lagrangian Advection Methods and Their Applications in Geoscience fills a crucial gap in numerical modeling and data assimilation in geoscience. Provides a single resource for understanding semi-Lagrangian methods and what is involved in its application Includes exercises and codes to supplement learning and create opportunities for practice Includes coverage of adjoints, examining the advantages and disadvantages of different approaches in multiple coordinate systems and different discretizations Includes links to numerical datasets and animations to further enhance understanding

Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications

Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications
Author : Seon Ki Park,Liang Xu
Publisher : Springer
Release Date : 2016-12-26
Category : Science
Total pages :553
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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.

Dynamic Data Assimilation

Dynamic Data Assimilation
Author : John M. Lewis,S. Lakshmivarahan,Sudarshan Dhall
Publisher : Cambridge University Press
Release Date : 2006-08-03
Category : Mathematics
Total pages :654
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Dynamic data assimilation is the assessment, combination and synthesis of observational data, scientific laws and mathematical models to determine the state of a complex physical system, for instance as a preliminary step in making predictions about the system's behaviour. The topic has assumed increasing importance in fields such as numerical weather prediction where conscientious efforts are being made to extend the term of reliable weather forecasts beyond the few days that are presently feasible. This book is designed to be a basic one-stop reference for graduate students and researchers. It is based on graduate courses taught over a decade to mathematicians, scientists, and engineers, and its modular structure accommodates the various audience requirements. Thus Part I is a broad introduction to the history, development and philosophy of data assimilation, illustrated by examples; Part II considers the classical, static approaches, both linear and nonlinear; and Part III describes computational techniques. Parts IV to VII are concerned with how statistical and dynamic ideas can be incorporated into the classical framework. Key themes covered here include estimation theory, stochastic and dynamic models, and sequential filtering. The final part addresses the predictability of dynamical systems. Chapters end with a section that provides pointers to the literature, and a set of exercises with instructive hints.

Nonlinear Systems

Nonlinear Systems
Author : Dongbin Lee,Christos Volos,Timothy Burg
Publisher : BoD – Books on Demand
Release Date : 2016-10-19
Category : Mathematics
Total pages :364
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The book consists mainly of two parts: Chapter 1 - Chapter 7 and Chapter 8 - Chapter 14. Chapter 1 and Chapter 2 treat design techniques based on linearization of nonlinear systems. An analysis of nonlinear system over quantum mechanics is discussed in Chapter 3. Chapter 4 to Chapter 7 are estimation methods using Kalman filtering while solving nonlinear control systems using iterative approach. Optimal approaches are discussed in Chapter 8 with retarded control of nonlinear system in singular situation, and Chapter 9 extends optimal theory to H-infinity control for a nonlinear control system.Chapters 10 and 11 present the control of nonlinear dynamic systems, twin-rotor helicopter and 3D crane system, which are both underactuated, cascaded dynamic systems. Chapter 12 applies controls to antisynchronization/synchronization in the chaotic models based on Lyapunov exponent theorem, and Chapter 13 discusses developed stability analytic approaches in terms of Lyapunov stability. The analysis of economic activities, especially the relationship between stock return and economic growth, is presented in Chapter 14.

Four-Dimensional Model Assimilation of Data

Four-Dimensional Model Assimilation of Data
Author : National Research Council,Division on Earth and Life Studies,Commission on Geosciences, Environment and Resources,Panel on Model-Assimilated Data Sets for Atmospheric and Oceanic Research
Publisher : National Academies Press
Release Date : 1991-02-01
Category : Science
Total pages :88
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This volume explores and evaluates the development, multiple applications, and usefulness of four-dimensional (space and time) model assimilations of data in the atmospheric and oceanographic sciences and projects their applicability to the earth sciences as a whole. Using the predictive power of geophysical laws incorporated in the general circulation model to produce a background field for comparison with incoming raw observations, the model assimilation process synthesizes diverse, temporarily inconsistent, and spatially incomplete observations from worldwide land, sea, and space data acquisition systems into a coherent representation of an evolving earth system. The book concludes that this subdiscipline is fundamental to the geophysical sciences and presents a basic strategy to extend the application of this subdiscipline to the earth sciences as a whole.

Assimilation of Remote Sensing Data into Earth System Models

Assimilation of Remote Sensing Data into Earth System Models
Author : Jean-Christophe Calvet,Patricia De Rosnay,Stephen G. Penny
Publisher : MDPI
Release Date : 2019-11-20
Category : Science
Total pages :236
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In the Earth sciences, a transition is currently occurring in multiple fields towards an integrated Earth system approach, with applications including numerical weather prediction, hydrological forecasting, climate impact studies, ocean dynamics estimation and monitoring, and carbon cycle monitoring. These approaches rely on coupled modeling techniques using Earth system models that account for an increased level of complexity of the processes and interactions between atmosphere, ocean, sea ice, and terrestrial surfaces. A crucial component of Earth system approaches is the development of coupled data assimilation of satellite observations to ensure consistent initialization at the interface between the different subsystems. Going towards strongly coupled data assimilation involving all Earth system components is a subject of active research. A lot of progress is being made in the ocean–atmosphere domain, but also over land. As atmospheric models now tend to address subkilometric scales, assimilating high spatial resolution satellite data in the land surface models used in atmospheric models is critical. This evolution is also challenging for hydrological modeling. This book gathers papers reporting research on various aspects of coupled data assimilation in Earth system models. It includes contributions presenting recent progress in ocean–atmosphere, land–atmosphere, and soil–vegetation data assimilation.