Data assimilation: methods, algorithms, and applications

Mark Asch (1) , Marc Bocquet (2) , Maëlle Nodet (3)

1 LAMFA - Laboratoire Amiénois de Mathématique Fondamentale et Appliquée
2 CEREA - Centre d'Enseignement et de Recherche en Environnement Atmosphérique
3 AIRSEA - Mathematics and computing applied to oceanic and atmospheric flows

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.

Mots clés: ensemble methods; adjoint methods; data assimilation; inverse problems; Kalman filters

Domaines: Optimisation et contrôle [math.OC]

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⟩