Data Assimilation

Methods, Algorithms, and Applications

AvMark Asch,Marc Bocquet

Häftad, Engelska, 2017

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2017-02-02
Mått:177 x 255 x 20 mm
Vikt:690 g
Format:Häftad
Språk:Engelska
Serie:Fundamentals of Algorithms
Antal sidor:320
Förlag:Society for Industrial and Applied Mathematics
ISBN:9781611974539

Utforska kategorier

Matematisk statistik inom Naturvetenskap och teknik

Mer om författaren

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 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 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.

Innehållsförteckning

Part I: Basic Methods and Algorithms for Data AssimilationChapter 1: Introduction to Data Assimilation and Inverse ProblemsChapter 2: Optimal Control and Variational Data AssimilationChapter 3: Statistical Estimation and Sequential Data AssimilationPart II: Advanced Methods and Algorithms for Data AssimilationChapter 4: Nudging MethodsChapter 5: Reduced MethodsChapter 6: The Ensemble Kalman FilterChapter 7: Ensemble Variational MethodsPart III: Applications and Case StudiesChapter 8: Applications in Environmental SciencesChapter 9: Applications in Atmospheric SciencesChapter 10: Applications in GeosciencesChapter 11: Applications in Medicine, Biology, Chemistry, and Physical SciencesChapter 12: Applications in Human and Social Sciences