Articles | Volume 28, issue 3
Nonlin. Processes Geophys., 28, 295–309, 2021
https://doi.org/10.5194/npg-28-295-2021
Nonlin. Processes Geophys., 28, 295–309, 2021
https://doi.org/10.5194/npg-28-295-2021

Research article 06 Jul 2021

Research article | 06 Jul 2021

Ensemble Riemannian data assimilation over the Wasserstein space

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
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
Amari, S.: Differential-geometrical methods in statistics, vol. 28, Springer Science & Business Media, New York, NY, 2012. a, b
Amezcua, J., Ide, K., Kalnay, E., and Reich, S.: Ensemble transform Kalman–Bucy filters, Q. J. Roy. Meteor. Soc., 140, 995–1004, 2014. a
Anderson, J. L.: A method for producing and evaluating probabilistic forecasts from ensemble model integrations, J. Climate, 9, 1518–1530, 1996. a
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Short summary
Data assimilation aims to improve hydrologic and weather forecasts by combining available information from Earth system models and observations. The classical approaches to data assimilation usually proceed with some preconceived assumptions about the shape of their probability distributions. As a result, when such assumptions are invalid, the forecast accuracy suffers. In the proposed methodology, we relax such assumptions and demonstrate improved performance.