Issues in making the weakly-constrained 4DVar formulation computationally efficient - Archive ouverte HAL Access content directly
Book Sections Year : 2017

Issues in making the weakly-constrained 4DVar formulation computationally efficient

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Abstract

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.
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Dates and versions

hal-03176447 , version 1 (22-03-2021)

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Serge Gratton, Selime Gurol, Ehouarn Simon, Philippe Toint. Issues in making the weakly-constrained 4DVar formulation computationally efficient. Griewank, Andreas; Reich, Sebastian; Roulstone, Ian; Stuart, Andrew. Mathematical and Algorithmic Aspects of Data Assimilation in the Geosciences, 13 (4), Mathematisches Forschungsinstitut Oberwolfach, pp.2705-2748, 2017, Oberwolfach Reports, ISSN: 1660-8933. ⟨10.4171/OWR/2016/47⟩. ⟨hal-03176447⟩
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