Articles | Volume 29, issue 1
Nonlin. Processes Geophys., 29, 77–92, 2022
https://doi.org/10.5194/npg-29-77-2022
Nonlin. Processes Geophys., 29, 77–92, 2022
https://doi.org/10.5194/npg-29-77-2022
Research article
18 Feb 2022
Research article | 18 Feb 2022

Ensemble Riemannian data assimilation: towards large-scale dynamical systems

Sagar K. Tamang et al.

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Cited articles

Agueh, M. and Carlier, G.: Barycenters in the Wasserstein space, SIAM J. Math. Anal., 43, 904–924, 2011. a, b
Altman, A. and Gondzio, J.: Regularized symmetric indefinite systems in interior point methods for linear and quadratic optimization, Optim. Method. Softw., 11, 275–302, 1999. a
Anderson, J. and Lei, L.: Empirical localization of observation impact in ensemble Kalman filters, Mon. Weather Rev., 141, 4140–4153, 2013. a
Anderson, J. L.: An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, 2884–2903, 2001. a
Anderson, J. L.: Localization and sampling error correction in ensemble Kalman filter data assimilation, Mon. Weather Rev., 140, 2359–2371, 2012. a, b
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Short summary
The outputs from Earth system models are optimally combined with satellite observations to produce accurate forecasts through a process called data assimilation. Many existing data assimilation methodologies have some assumptions regarding the shape of the probability distributions of model output and observations, which results in forecast inaccuracies. In this paper, we test the effectiveness of a newly proposed methodology that relaxes such assumptions about high-dimensional models.