- journal article metadata
European Mathematical Society Publishing House
2017-12-23 23:40:02
Oberwolfach Reports
Oberwolfach Rep.
OWR
1660-8933
1660-8941
General
10.4171/OWR
http://www.ems-ph.org/doi/10.4171/OWR
subscribers
European Mathematical Society Publishing House
Zuerich, Switzerland
© Mathematisches Forschungsinstitut Oberwolfach
13
2016
4
Mathematical and Algorithmic Aspects of Data Assimilation in the Geosciences
Andreas
Griewank
Humboldt-Universität zu Berlin, Germany
Sebastian
Reich
Universität Potsdam, Germany
Ian
Roulstone
University of Surrey, Guildford, UK
Andrew
Stuart
University of Warwick, Coventry, UK
The field of “Data Assimilation” has been driven by applications from the geosciences where complex mathematical models are interfaced with observational data in order to improve model forecasts. Mathematically, data assimilation is closely related to filtering and smoothing on the one hand and inverse problems and statistical inference on the other. Key challenges of data assimilation arise from the high-dimensionality of the underlying models, combined with systematic spatio-temporal model errors, pure model uncertainty quantification and relatively sparse observation networks. Advances in the field of data assimilation will require combination of a broad range of mathematical techniques from differential equations, statistics, machine learning, probability, scientific computing and mathematical modeling, together with insights from practitioners in the field. The workshop brought together a collection of scientists representing this broad spectrum of research strands.
Dynamical systems and ergodic theory
Probability theory and stochastic processes
2705
2748
10.4171/OWR/2016/47
http://www.ems-ph.org/doi/10.4171/OWR/2016/47
12
20
2017