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Data Assimilation

Data Assimilation
Methods, Algorithms, and Applications

£73.99

Part of Fundamentals of Algorithms

  • Date Published: February 2017
  • availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
  • format: Paperback
  • isbn: 9781611974539

£ 73.99
Paperback

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  • 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, emphasising 'why' and not just 'how'. Methods and diagnostics are emphasised, enabling readers to readily apply them to their own field of study. This comprehensive guide is accessible to non-experts and contains numerous examples and diverse applications from a broad range of domains, including geophysics, environmental acoustics, medical imaging, mechanical and biomedical engineering, economics and finance, and traffic control and urban planning. Readers will also find included the latest methods for advanced data assimilation, combining variational and statistical approaches.

    • Provides a comprehensive guide which will be accessible to non-experts
    • Contains many examples and a variety of applications from a broad range of domains
    • Outlines the most up-to-date methods for advanced data assimilation, combining variational and statistical approaches
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    Product details

    • Date Published: February 2017
    • format: Paperback
    • isbn: 9781611974539
    • length: 320 pages
    • dimensions: 255 x 177 x 20 mm
    • weight: 0.69kg
    • availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
  • Table of Contents

    List of figures
    List of algorithms
    Notation
    Preface
    Part I. Basic Methods and Algorithms for Data Assimilation:
    1. Introduction to data assimilation and inverse problems
    2. Optimal control and variational data assimilation
    3. Statistical estimation and sequential data assimilation
    Part II. Advanced Methods and Algorithms for Data Assimilation:
    4. Nudging methods
    5. Reduced methods
    6. The ensemble Kalman filter
    7. Ensemble variational methods
    Part III. Applications and Case Studies:
    8. Applications in environmental sciences
    9. Applications in atmospheric sciences
    10. Applications in geosciences
    11. Applications in medicine, biology, chemistry, and physical sciences
    12. Applications in human and social sciences
    Bibliography
    Index.

  • Authors

    Mark Asch, Université de Picardie Jules Verne, Amiens
    Mark Asch currently leads an action theme in the Belmont Forum Data Management and e-Infrastructure initiative, is a co-organizer of the BDEC (Big Data and Extreme-Scale Computing) forum, and is a full professor of mathematics at Université de Picardie Jules Verne, Amiens. He was programme manager for Mathematics, Computer Science, HPC, and Big Data at the French National Research Agency (ANR). From 2012 to 2015, he was scientific officer for mathematics and e-infrastructures at the French ministry of research.

    Marc Bocquet, Ecole des Ponts ParisTech
    Marc Bocquet is professor, senior scientist, and deputy director of the Environment Research Centre (CEREA) at École des Ponts ParisTech. He is chair of the Statistics for Analysis, Modelling and Assimilation group of the Pierre-Simon Laplace Institute (IPSL). Prior to 2002, he worked in the Rudolf Peierls Centre for Theoretical Physics of the University of Oxford, the Department of Physics at the University of Warwick, and the Theoretical Physics Institute of the French Alternative Energies and Atomic Energy Commission, Saclay. He is Associate Editor for the Quarterly Journal of the Royal Meteorological Society.

    Maëlle Nodet, Université Grenoble Alpes
    Maëlle Nodet is an associate professor in applied mathematics at Université Grenoble Alpes. Her research interests are data assimilation methods, inverse problems, sensitivity analysis, control, optimal transport, and imaging applied to various geoscience fields. She is strongly involved in teaching and outreach activities, particularly in developing and promoting active, problem-based, and student-centred learning.

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