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Data Assimilation for the Geosciences, 2 ed
◆JpGU2025 セール開催中！：2025年6月30日(月)ご注文分まで
※上記表示の販売価格は割引適用後の価格です 出版済み 3-5週間でお届けいたします。 From Theory to Application Author: Fletcher, Steven J. (Research Scientist III, Cooperative Institute for Research in the Atmosphere (CIRA), Colorado State University - Fort Collins, Colorado, USA) Publisher: Elsevier USA ISBN: 9780323917209 Cover: PAPERBACK Date: 2022年11月 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. TABLE OF CONTENTS 1. Introduction 2. Overview of Linear Algebra 3. Univariate Distribution Theory 4. Multivariate Distribution Theory 5. Introduction to Calculus of Variation 6. Introduction to Control Theory 7. Optimal Control Theory 8. Numerical Solutions to Initial Value Problems 9. Numerical Solutions to Boundary Value Problems 10. Introduction to Semi-Lagrangian Advection Methods 11. Introduction to Finite Element Modeling 12. Numerical Modeling on the Sphere 13. Tangent Linear Modeling and Adjoints 14. Observations 15. Non-variational Sequential Data Assimilation Methods 16. Variational Data Assimilation 17. Subcomponents of Variational Data Assimilation 18. Observation Space Variational Data Assimilation Methods 19. Kalman Filter and Smoother 20. Ensemble-Based Data Assimilation 21. Non-Gaussian Variational Data Assimilation 22. Markov Chain Monte Carlo and Particle Filter Methods 23. Machine Learning Artificial Intelligence with Data Assimilation 24. Applications of Data Assimilation in the Geosciences 25. Solutions to Select Exercise
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