Articles | Volume 28, issue 3
Nonlin. Processes Geophys., 28, 295–309, 2021
Nonlin. Processes Geophys., 28, 295–309, 2021

Research article 06 Jul 2021

Research article | 06 Jul 2021

Ensemble Riemannian data assimilation over the Wasserstein space

Sagar K. Tamang et al.

Related authors

Ensemble Riemannian Data Assimilation: Towards High-dimensional Implementation
Sagar Kumar Tamang, Ardeshir Ebtehaj, Peter Jan van Leeuwen, Gilad Lerman, and Efi Foufoula-Georgiou
Nonlin. Processes Geophys. Discuss.,,, 2021
Preprint under review for NPG
Short summary
Framework for quantifying flow and sediment yield to diagnose and solve the aggradation problem of an ungauged catchment
Sagar Kumar Tamang, Wenjun Song, Xing Fang, Jose Vasconcelos, and J. Brian Anderson
Proc. IAHS, 379, 131–138,,, 2018
Short summary

Related subject area

Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Theory
Improving the potential accuracy and usability of EURO-CORDEX estimates of future rainfall climate using frequentist model averaging
Stephen Jewson, Giuliana Barbato, Paola Mercogliano, Jaroslav Mysiak, and Maximiliano Sassi
Nonlin. Processes Geophys., 28, 329–346,,, 2021
Short summary
An early warning sign of critical transition in the Antarctic ice sheet – a data-driven tool for a spatiotemporal tipping point
Abd AlRahman AlMomani and Erik Bollt
Nonlin. Processes Geophys., 28, 153–166,,, 2021
Short summary
Multivariate localization functions for strongly coupled data assimilation in the bivariate Lorenz ’96 system
Zofia Stanley, Ian Grooms, and William Kleiber
Nonlin. Processes Geophys. Discuss.,,, 2021
Revised manuscript accepted for NPG
Short summary
Behavior of the iterative ensemble-based variational method in nonlinear problems
Shin'ya Nakano
Nonlin. Processes Geophys., 28, 93–109,,, 2021
Short summary
A methodology to obtain model-error covariances due to the discretization scheme from the parametric Kalman filter perspective
Olivier Pannekoucke, Richard Ménard, Mohammad El Aabaribaoune, and Matthieu Plu
Nonlin. Processes Geophys., 28, 1–22,,, 2021
Short summary

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
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.