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Author: John M. Lewis
Publisher: Cambridge University Press
ISBN: 0521851556
Category : Mathematics
Languages : en
Pages : 601
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Book Description
Publisher description
Author: John M. Lewis
Publisher: Cambridge University Press
ISBN: 0521851556
Category : Mathematics
Languages : en
Pages : 601
Get Book Here
Book Description
Publisher description
Author:
Publisher:
ISBN: 9781107390423
Category : MATHEMATICS
Languages : en
Pages : 654
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Book Description
A basic one-stop reference for graduate students and researchers.
Author: Dinesh G. Harkut
Publisher: BoD – Books on Demand
ISBN: 1839680830
Category : Computers
Languages : en
Pages : 120
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Book Description
Data assimilation is a process of fusing data with a model for the singular purpose of estimating unknown variables. It can be used, for example, to predict the evolution of the atmosphere at a given point and time. This book examines data assimilation methods including Kalman filtering, artificial intelligence, neural networks, machine learning, and cognitive computing.
Author: Seon Ki Park
Publisher: Springer Science & Business Media
ISBN: 3642350887
Category : Science
Languages : en
Pages : 736
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Book Description
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.
Author: Seon Ki Park
Publisher: Cambridge University Press
ISBN: 1108923895
Category : Science
Languages : en
Pages : 413
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Book Description
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.
Author: Haraldur Olafsson
Publisher: Elsevier
ISBN: 0128157100
Category : Computers
Languages : en
Pages : 366
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Book Description
Uncertainties in Numerical Weather Prediction is a comprehensive work on the most current understandings of uncertainties and predictability in numerical simulations of the atmosphere. It provides general knowledge on all aspects of uncertainties in the weather prediction models in a single, easy to use reference. The book illustrates particular uncertainties in observations and data assimilation, as well as the errors associated with numerical integration methods. Stochastic methods in parameterization of subgrid processes are also assessed, as are uncertainties associated with surface-atmosphere exchange, orographic flows and processes in the atmospheric boundary layer. Through a better understanding of the uncertainties to watch for, readers will be able to produce more precise and accurate forecasts. This is an essential work for anyone who wants to improve the accuracy of weather and climate forecasting and interested parties developing tools to enhance the quality of such forecasts. Provides a comprehensive overview of the state of numerical weather prediction at spatial scales, from hundreds of meters, to thousands of kilometers Focuses on short-term 1-15 day atmospheric predictions, with some coverage appropriate for longer-term forecasts Includes references to climate prediction models to allow applications of these techniques for climate simulations
Author: Sebastian Reich
Publisher: Cambridge University Press
ISBN: 1107069394
Category : Computers
Languages : en
Pages : 308
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Book Description
This book covers key ideas and concepts. It is an ideal introduction for graduate students in any field where Bayesian data assimilation is applied.
Author: Seon Ki Park
Publisher: Springer
ISBN: 3319434152
Category : Science
Languages : en
Pages : 576
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Book Description
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.
Author: Geir Evensen
Publisher: Springer Nature
ISBN: 3030967093
Category : Science
Languages : en
Pages : 251
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Book Description
This open-access textbook's significant contribution is the unified derivation of data-assimilation techniques from a common fundamental and optimal starting point, namely Bayes' theorem. Unique for this book is the "top-down" derivation of the assimilation methods. It starts from Bayes theorem and gradually introduces the assumptions and approximations needed to arrive at today's popular data-assimilation methods. This strategy is the opposite of most textbooks and reviews on data assimilation that typically take a bottom-up approach to derive a particular assimilation method. E.g., the derivation of the Kalman Filter from control theory and the derivation of the ensemble Kalman Filter as a low-rank approximation of the standard Kalman Filter. The bottom-up approach derives the assimilation methods from different mathematical principles, making it difficult to compare them. Thus, it is unclear which assumptions are made to derive an assimilation method and sometimes even which problem it aspires to solve. The book's top-down approach allows categorizing data-assimilation methods based on the approximations used. This approach enables the user to choose the most suitable method for a particular problem or application. Have you ever wondered about the difference between the ensemble 4DVar and the "ensemble randomized likelihood" (EnRML) methods? Do you know the differences between the ensemble smoother and the ensemble-Kalman smoother? Would you like to understand how a particle flow is related to a particle filter? In this book, we will provide clear answers to several such questions. The book provides the basis for an advanced course in data assimilation. It focuses on the unified derivation of the methods and illustrates their properties on multiple examples. It is suitable for graduate students, post-docs, scientists, and practitioners working in data assimilation.
Author: Bo-Wen Shen
Publisher: MDPI AG
ISBN: 3036589104
Category : Education
Languages : en
Pages : 338
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Book Description
Celebrate the 50th anniversary of the metaphorical butterfly effect, born from Edward Lorenz's 1963 work on initial condition sensitivity. In 1972, it became a metaphor for illustrating how minor changes could yield an organized system. Lorenz Models: Chaos & Regime Changes Explore Lorenz models' 1960-2008 evolution, chaos theory, and attractors. Unraveling High-dimensional Instability Challenge norms in "Butterfly Effect without Chaos?" as non-chaotic elements contribute uniquely. Modeling Atmospheric Dynamics Delve into atmospheric dynamics via "Storm Sensitivity Study." Navigating Data Assimilation Explore data assimilation's dance in chaotic and nonchaotic settings via the observability Gramian. Chaos, Instability, Sensitivities Explore chaos, instability, and sensitivities with Lorenz 1963 & 1969 models. Unraveling Tropical Mysteries Investigate tropical atmospheric instability, uncovering oscillation origins and cloud-radiation interactions. Chaos and Order Enter atmospheric regimes, exploring attractor coexistence and predictability. The Art of Prediction Peer into predictability realms, tracing the "butterfly effect's" impact on predictions. Navigating Typhoons Journey through typhoons, exploring rainfall and typhoon trajectory prediction. Analyzing Sea Surface Temperature Examine nonlinear analysis for classification. Computational Fluid Dynamics Immerse in geophysical fluid dynamics progress, simulating atmospheric phenomena.
