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NPG | Articles | Volume 25, issue 2
Nonlin. Processes Geophys., 25, 355–374, 2018
https://doi.org/10.5194/npg-25-355-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Nonlin. Processes Geophys., 25, 355–374, 2018
https://doi.org/10.5194/npg-25-355-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 03 May 2018

Research article | 03 May 2018

Feature-based data assimilation in geophysics

Matthias Morzfeld et al.
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Cited articles  
Agapiou, S., Papaspiliopoulos, O., Sanz-Alonso, D., and Stuart, A.: Importance sampling: computational complexity and intrinsic dimension, Stat. Sci., 32, 405–431, 2017. a, b, c
Arulampalam, M., Maskell, S., Gordon, N., and Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE T. Signal Proces., 50, 174–188, 2002. a
Atkins, E., Morzfeld, M., and Chorin, A.: Implicit particle methods and their connection with variational data assimilation, Mon. Weather Rev., 141, 1786–1803, 2013. a
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 252, 45–55, 2015. a
Bishop, C.: Pattern Recognition and Machine Learning, Springer-Verlag, New York, USA, 2006. a
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Many applications in science require that computational models and data be combined. In a Bayesian framework, this is usually done by defining likelihoods based on the mismatch of model outputs and data. However, matching model outputs and data in this way can be unnecessary or impossible. This issue can be addressed by selecting features of the data, and defining likelihoods based on the features, rather than by the usual mismatch of model output and data.
Many applications in science require that computational models and data be combined. In a...
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