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Nonlinear Data Assimilation
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Book Synopsis Nonlinear Data Assimilation by : Peter Jan Van Leeuwen
Download or read book Nonlinear Data Assimilation written by Peter Jan Van Leeuwen and published by Springer. This book was released on 2015-07-22 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis An Investigation of Linear and Nonlinear Data Assimilation Methods in the Presence of Model Error by : Paul Kirchgessner
Download or read book An Investigation of Linear and Nonlinear Data Assimilation Methods in the Presence of Model Error written by Paul Kirchgessner and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Ocean Forecasting by : Nadia Pinardi
Download or read book Ocean Forecasting written by Nadia Pinardi and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Advanced Study Courses in the field of Marine Science and Technology were part of the training programme developed from 1989 until 1999 within MAST, the Marine Science and Technology Programme of the European Union. They were related to the core topics of MAST Programme, marine systems research, extreme marine environments, regional Sea research, coastal systems research and engineering, and marine technology. The main objectives of these study courses were to further advance education in topics at the forefront of scien tific and technological development in Europe, and to improve the communication between students and experienced scientists on a European and international level. Over the years the Programme sponsored around 30 Advanced Study Courses. They took place in ten different member states of the European Union and their contribution in the formation of a European scientific community was significant. They also encouraged exchanges and contacts with several countries around the world such as United States, the third Mediterranean countries and others. The Course on Ocean Forecasting was one ofthe most successful with regard to its con tent, number of applications for participation and students satisfaction. When considering the need for the Advanced Study Course on Ocean Forecast ing, it is important to remember that the Oceans and Seas have always played a central role throughout the history of mankind. This is seen from the times of the ancient civilizations ofEgypt and Greece with the Phoenician traders, to the Viking voyages of exploration and discovery in medieval times.
Book Synopsis Particle Filters for Nonlinear Data Assimilation by : Daniel Berg
Download or read book Particle Filters for Nonlinear Data Assimilation written by Daniel Berg and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Nonlinear data assimilation using synchronisation in a particle filter by : Flávia Rodrigues Pinheiro
Download or read book Nonlinear data assimilation using synchronisation in a particle filter written by Flávia Rodrigues Pinheiro and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Statistical Physics of Data Assimilation and Machine Learning by : Henry D. I. Abarbanel
Download or read book The Statistical Physics of Data Assimilation and Machine Learning written by Henry D. I. Abarbanel and published by Cambridge University Press. This book was released on 2022-02-17 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: The theory of data assimilation and machine learning is introduced in an accessible manner for undergraduate and graduate students.
Book Synopsis Data Assimilation: Methods, Algorithms, and Applications by : Mark Asch
Download or read book Data Assimilation: Methods, Algorithms, and Applications written by Mark Asch and published by SIAM. This book was released on 2016-12-29 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Dynamic Data Assimilation by : John M. Lewis
Download or read book Dynamic Data Assimilation written by John M. Lewis and published by Cambridge University Press. This book was released on 2006-08-03 with total page 601 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher description
Book Synopsis Nonlinear Data Assimilation Using Synchronisation in a Particle Filter by : Flavia Rodrigues Pinheiro
Download or read book Nonlinear Data Assimilation Using Synchronisation in a Particle Filter written by Flavia Rodrigues Pinheiro and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Data Assimilation for the Geosciences by : Steven J. Fletcher
Download or read book Data Assimilation for the Geosciences written by Steven J. Fletcher and published by Elsevier. This book was released on 2022-11-16 with total page 1130 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Data Assimilation written by Kody Law and published by Springer. This book was released on 2015-09-05 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Data Assimilation for a Nonlinear, Wind-driven, Shallow-water Model in a Rectangular Midlatitude Basin by : Shi Jiang
Download or read book Data Assimilation for a Nonlinear, Wind-driven, Shallow-water Model in a Rectangular Midlatitude Basin written by Shi Jiang and published by . This book was released on 1994 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Data Assimilation written by Geir Evensen and published by Springer Science & Business Media. This book was released on 2006-12-22 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Data Assimilation and Precision Annealing Monte Carlo Method in Nonlinear Dynamical Systems by : Kangbo Hao
Download or read book Data Assimilation and Precision Annealing Monte Carlo Method in Nonlinear Dynamical Systems written by Kangbo Hao and published by . This book was released on 2020 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the study of data assimilation, people focus on estimating state variables and parameters of dynamical models, and make predictions forward in time, using given observations. It is a method that has been applied to many different fields, such as numerical weather prediction and neurobiology. To make successful estimations and predictions using data assimilation methods, there are a few difficulties that are often encountered. First is the quantity and quality of the data. In some of the typical problems in data assimilation, the number of observations are usually a few order of magnitude smaller than the number of total variables. Considering this and the fact that almost all the data gathered are noisy, how to estimate the observed and unobserved state variables and make good predictions using the noisy and incomplete data is one of the key challenge in data assimilation. Another issue arises from the dynamical model. Most of the interesting models are non-linear, and usually chaotic, which means that a small error in the estimation will grow exponentially over time. This property of the chaotic system addresses the necessity of accurate estimations of variables. In this thesis, I will start with an overview of data assimilation, by formulating the problem that data assimilation tries to solve, and introducing several widely used methods. Then I will explain the Precision Annealing Monte Carlo method that has been developed in the group, as well as its variation using Hamiltonian Monte Carlo. Finally I will demonstrate a few example problems that can be solved using data assimilation methods, varying from a simple but instructional 20-dimension Lorenz 96 model, to a complicated ocean model named Regional Ocean Modeling System.
Book Synopsis Data Assimilation in Highly Nonlinear Sytems by : Melanie Ades
Download or read book Data Assimilation in Highly Nonlinear Sytems written by Melanie Ades and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Data Assimilation and Forecasting for Complex Nonlinear Systems with Suitable Approximate Models by : Yingda Li (Ph.D.)
Download or read book Data Assimilation and Forecasting for Complex Nonlinear Systems with Suitable Approximate Models written by Yingda Li (Ph.D.) and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex nonlinear turbulent systems are ubiquitous in geoscience, engineering, neural and material sciences. This thesis focuses on three of cental topics related to complex nonlinear turbulent dynamical systems, the effective prediction, uncertainty quantification, and data assimilation.In the first part of this thesis, a simple but effective Bayesian Machine learning advanced forecast ensemble method is developed, which combines an available imperfect physics-informed model with data assimilation to facilitate the ML ensemble forecast. In the BAMCAFE framework, a Bayesian ensemble DA is applied to create the training data of the ML model, which reduces the intrinsic error in the imperfect physics-informed model simulations and provides the training data of the unobserved variables. Then a generalized DA is employed for the initialization of the ML ensemble forecast. Besides, the BAMCAFE also provides accurate quantification of the forecast uncertainty utilizing a non-Gaussian probability density function that characterizes intermittency and extreme events. In the second part, the skill of a rich class of nonlinear stochastic models, known as the "conditional Gaussian nonlinear system" (CGNS), as both a cheap surrogate model and a fast preconditioner is explored to advance many computationally challenging tasks in complex nonlinear systems. The CGNS preserves the underlying physics to a large extent and reproduces the observed intermittency, extreme events, and other non-Gaussian features of nature. Second, the CGNS allows the development of a fast algorithm for simultaneously estimating the parameters and the unobserved variables with uncertainty quantification in the presence of only partial observations. Utilizing an appropriate CGNS as a preconditioner significantly reduces the computational cost of accurately estimating the parameters in the original complex system. Finally, the CGNS advances rapid and statistically accurate algorithms for computing the probability density function and sampling the trajectories of the unobserved state variables. These fast algorithms facilitate the development of an efficient and accurate data-driven method for predicting the linear response of the original system with respect to parameter perturbations based on a suitable CGNS preconditioner. In the third part, a nonlinear optimal filter, a nonlinear optimal smoother, and the associated filter-based forward sampling and smoother-based backward sampling algorithms are developed for CGNS. The optimal nonlinear smoother also outweighs the optimal nonlinear filter in detecting the hidden mechanism of triggering observed extreme events. In addition, the optimal nonlinear smoother improves the state estimation in stochastic parameterizations and Lagrangian data assimilation. Next, an efficient and accurate online forward-in-time smoother is developed. Compared with the nonlinear filter, the sequential update of the existing state estimates using the online nonlinear smoother leads to a more accurate recovery of intermittent time series and the associated non-Gaussian features. Finally, the online nonlinear smoother is incorporated into an efficient expectation-maximization algorithm for online parameter estimation.
Book Synopsis Nonlinear Filtering with Particle Filters Data Assimilation on Convective Scale by : Mylène Haslehner
Download or read book Nonlinear Filtering with Particle Filters Data Assimilation on Convective Scale written by Mylène Haslehner and published by . This book was released on 2014 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt:
