Data Assimilation By Reconstructing Time Series Observation
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| Author | : Geir Evensen |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 285 |
| Release | : 2006-12-22 |
| Genre | : Science |
| ISBN | : 3540383018 |
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.
| Author | : Henry D. I. Abarbanel |
| Publisher | : Cambridge University Press |
| Total Pages | : 208 |
| Release | : 2022-02-17 |
| Genre | : Science |
| ISBN | : 1009021702 |
Data assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine learning and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and machine learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.
| Author | : Mark Asch |
| Publisher | : SIAM |
| Total Pages | : 310 |
| Release | : 2016-12-29 |
| Genre | : Mathematics |
| ISBN | : 1611974542 |
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.
| Author | : Simona Masina |
| Publisher | : Frontiers Media SA |
| Total Pages | : 219 |
| Release | : 2022-08-04 |
| Genre | : Science |
| ISBN | : 2889767108 |
| Author | : Qihao Weng |
| Publisher | : CRC Press |
| Total Pages | : 264 |
| Release | : 2020-06-30 |
| Genre | : |
| ISBN | : 9780367571795 |
This book explores the current state of knowledge on remote sensing time series image processing and addresses all major aspects and components of time series image analysis with ample examples and applications.
| Author | : Eugenia Kalnay |
| Publisher | : Cambridge University Press |
| Total Pages | : 368 |
| Release | : 2003 |
| Genre | : Mathematics |
| ISBN | : 9780521796293 |
This book, first published in 2002, is a graduate-level text on numerical weather prediction, including atmospheric modeling, data assimilation and predictability.
| Author | : Sebastian Reich |
| Publisher | : Cambridge University Press |
| Total Pages | : 308 |
| Release | : 2015-05-14 |
| Genre | : Computers |
| ISBN | : 1316299422 |
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.
| Author | : Peter Jan Van Leeuwen |
| Publisher | : Springer |
| Total Pages | : 130 |
| Release | : 2015-07-22 |
| Genre | : Mathematics |
| ISBN | : 3319183478 |
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.
| Author | : Axel Hutt |
| Publisher | : Frontiers Media SA |
| Total Pages | : 116 |
| Release | : 2019-08-16 |
| Genre | : |
| ISBN | : 2889459853 |
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.
| Author | : Alan E. Gelfand |
| Publisher | : CRC Press |
| Total Pages | : 876 |
| Release | : 2019-01-15 |
| Genre | : Mathematics |
| ISBN | : 1498752128 |
This handbook focuses on the enormous literature applying statistical methodology and modelling to environmental and ecological processes. The 21st century statistics community has become increasingly interdisciplinary, bringing a large collection of modern tools to all areas of application in environmental processes. In addition, the environmental community has substantially increased its scope of data collection including observational data, satellite-derived data, and computer model output. The resultant impact in this latter community has been substantial; no longer are simple regression and analysis of variance methods adequate. The contribution of this handbook is to assemble a state-of-the-art view of this interface. Features: An internationally regarded editorial team. A distinguished collection of contributors. A thoroughly contemporary treatment of a substantial interdisciplinary interface. Written to engage both statisticians as well as quantitative environmental researchers. 34 chapters covering methodology, ecological processes, environmental exposure, and statistical methods in climate science.
