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Data assimilation: methods, algorithms, and applications

Mark Asch 1 Marc Bocquet 2 Maëlle Nodet 3 
3 AIRSEA - Mathematics and computing applied to oceanic and atmospheric flows
Inria Grenoble - Rhône-Alpes, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, UGA [2016-2019] - Université Grenoble Alpes [2016-2019], LJK - Laboratoire Jean Kuntzmann
Abstract : 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.
Document type :
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Contributor : Maëlle Nodet Connect in order to contact the contributor
Submitted on : Friday, November 25, 2016 - 11:51:59 AM
Last modification on : Tuesday, October 25, 2022 - 4:19:10 PM


  • HAL Id : hal-01402885, version 1


Mark Asch, Marc Bocquet, Maëlle Nodet. Data assimilation: methods, algorithms, and applications. SIAM, pp.xviii + 306, 2016, Fundamentals of Algorithms, 978-1-611974-53-9. ⟨hal-01402885⟩



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