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This guide provides a framework for and insight into the inverse problem nature of data assimilation, emphasised methods and diagnostics.
Acerca de los autores:
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.
Título: Data Assimilation: Methods, Algorithms, and ...
Editorial: Society for Industrial & Applied Mathematics
Año de publicación: 2016
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Condición: New. This guide provides a framework for and insight into the inverse problem nature of data assimilation, emphasised methods and diagnostics.KlappentextrnrnThis guide provides a framework for and insight into the inverse problem nature of data a. Nº de ref. del artículo: 596415871
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