Articles | Volume 28, issue 3
Research article
06 Jul 2021
Research article |  | 06 Jul 2021

Ensemble Riemannian data assimilation over the Wasserstein space

Sagar K. Tamang, Ardeshir Ebtehaj, Peter J. van Leeuwen, Dongmian Zou, and Gilad Lerman

Related authors

Ensemble Riemannian data assimilation: towards large-scale dynamical systems
Sagar K. Tamang, Ardeshir Ebtehaj, Peter Jan van Leeuwen, Gilad Lerman, and Efi Foufoula-Georgiou
Nonlin. Processes Geophys., 29, 77–92,,, 2022
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
Toward a multivariate formulation of the parametric Kalman filter assimilation: application to a simplified chemical transport model
Antoine Perrot, Olivier Pannekoucke, and Vincent Guidard
Nonlin. Processes Geophys., 30, 139–166,,, 2023
Short summary
Data-driven reconstruction of partially observed dynamical systems
Pierre Tandeo, Pierre Ailliot, and Florian Sévellec
Nonlin. Processes Geophys., 30, 129–137,,, 2023
Short summary
Extending ensemble Kalman filter algorithms to assimilate observations with an unknown time offset
Elia Gorokhovsky and Jeffrey L. Anderson
Nonlin. Processes Geophys., 30, 37–47,,, 2023
Short summary
Towards Strongly-coupled Ensemble Data Assimilation with Additional Improvements from Machine Learning
Eugenia Kalnay, Travis Sluka, Takuma Yoshida, Cheng Da, and Safa Mote
Nonlin. Processes Geophys. Discuss.,,, 2023
Revised manuscript accepted for NPG
Short summary
Using orthogonal vectors to improve the ensemble space of the EnKF and its effect on data assimilation and forecasting
Yung-Yun Cheng, Shu-Chih Yang, Zhe-Hui Lin, and Yung-An Lee
Nonlin. Processes Geophys. Discuss.,,, 2023
Revised manuscript accepted for NPG
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.