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Author: Mark Asch
Publisher: SIAM
ISBN: 1611974542
Category : Mathematics
Languages : en
Pages : 306
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Book Description
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: Mark Asch
Publisher: SIAM
ISBN: 1611974542
Category : Mathematics
Languages : en
Pages : 306
Get Book
Book Description
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: Kody Law
Publisher: Springer
ISBN: 3319203258
Category : Mathematics
Languages : en
Pages : 242
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Book Description
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.
Author: Steven J. Fletcher
Publisher: Elsevier
ISBN: 0323972535
Category : Science
Languages : en
Pages : 1130
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Book Description
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
Author: Geir Evensen
Publisher: Springer Science & Business Media
ISBN: 3540383018
Category : Science
Languages : en
Pages : 285
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Book Description
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: John M. Lewis
Publisher: Cambridge University Press
ISBN: 0521851556
Category : Mathematics
Languages : en
Pages : 601
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Book Description
Publisher description
Author: Eugenia Kalnay
Publisher: Cambridge University Press
ISBN: 9780521796293
Category : Mathematics
Languages : en
Pages : 368
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Book Description
This book, first published in 2002, is a graduate-level text on numerical weather prediction, including atmospheric modeling, data assimilation and predictability.
Author: Seon Ki Park
Publisher: Springer Science & Business Media
ISBN: 3642350887
Category : Science
Languages : en
Pages : 730
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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: William Lahoz
Publisher: Springer Science & Business Media
ISBN: 3540747036
Category : Science
Languages : en
Pages : 710
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Book Description
Data assimilation methods were largely developed for operational weather forecasting, but in recent years have been applied to an increasing range of earth science disciplines. This book will set out the theoretical basis of data assimilation with contributions by top international experts in the field. Various aspects of data assimilation are discussed including: theory; observations; models; numerical weather prediction; evaluation of observations and models; assessment of future satellite missions; application to components of the Earth System. References are made to recent developments in data assimilation theory (e.g. Ensemble Kalman filter), and to novel applications of the data assimilation method (e.g. ionosphere, Mars data assimilation).
Author: Henry D. I. Abarbanel
Publisher: Cambridge University Press
ISBN: 1316519635
Category : Computers
Languages : en
Pages : 207
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Book Description
The theory of data assimilation and machine learning is introduced in an accessible manner for undergraduate and graduate students.
Author: Shunlin Liang
Publisher: World Scientific
ISBN: 981447262X
Category : Science
Languages : en
Pages : 492
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Book Description
This book is unique in its ambitious and comprehensive coverage of earth system land surface characterization, from observation and modeling to data assimilation, including recent developments in theory and techniques, and novel application cases. The contributing authors are active research scientists, and many of them are internationally known leading experts in their areas, ensuring that the text is authoritative. This book comprises four parts that are logically connected from data, modeling, data assimilation integrating data and models to applications. Land data assimilation is the key focus of the book, which encompasses both theoretical and applied aspects with various novel methodologies and applications to the water cycle, carbon cycle, crop monitoring, and yield estimation. Readers can benefit from a state-of-the-art presentation of the latest tools and their usage for understanding earth system processes. Discussions in the book present and stimulate new challenges and questions facing today's earth science and modeling communities. Contents:Observation:Remote Sensing Data Products for Land Surface Data Assimilation System Application (Yunjun Yao, Shunlin Liang and Tongren Xu)Second-Generation Polar-Orbiting Meteorological Satellites of China: The Fengyun 3 Series and Its Applications in Global Monitoring (Peng Zhang)NASA Satellite and Model Land Data Services: Data Access Tutorial (Suhung Shen, Gregory Leptoukh and Hongliang Fang)Modeling:Land Surface Process Study and Modeling in Drylands and High-Elevation Regions (Yingying Chen and Kun Yang)Review of Parameterization and Parameter Estimation for Hydrologic Models (Soroosh Sorooshian and Wei Chu)Data Assimilation:Assimilating Remote Sensing Data into Land Surface Models: Theory and Methods (Xin Li and Yulong Bai)Estimating Model and Observation Error Covariance Information for Land Data Assimilation Systems (Wade T Crow)Inflation Adjustment on Error Covariance Matrices for Ensemble Kalman Filter Assimilation (Xiaogu Zheng, Guocan Wu, Xiao Liang and Shupeng Zhang)A Review of Error Estimation in Land Data Assimilation Systems (Yulong Bai, Xin Li and Qianlong Chai)An Introduction to Multi-scale Kalman Smoother-Based Framework and Its Application to Data Assimilation (Daniel E Salas and Xu Liang)Application:Overview of the North American Land Data Assimilation System (NLDAS) (Youlong Xia, Brian A Cosgrove, Michael B Ek, Justin Sheffield, Lifeng Luo, Eric F Wood, Kingtse Mo and the NLDAS team)Soil Moisture Data Assimilation for State Initialization of Seasonal Climate Prediction (Wenge Ni-Meister)Assimilation of Remote Sensing Data and Crop Simulation Models for Agricultural Study: Recent Advances and Future Directions (Hongliang Fang, Shunlin Liang and Gerrit Hoogenboom)Simultaneous State-Parameter Estimation for Hydrologic Modeling Using Ensemble Kalman Filter (Xianhong Xie) Readership: Graduate students and scientists in remote sensing, hydrology, ecology, environment and other earth sciences. Keywords:Data Assimilation;Uncertainties;Land Surface Processes;Satellite Data;Dynamic ModelsKey Features:The contribution authors are a group of leading experts international in those areasIt elaborates on the state-of-the-art land data assimilation, from theoretical derivations to current application problemsIt provides the latest development of satellite data and products, and presents novel applications of data assimilation for water cycle, crop monitoring and yield estimation
