Articles | Volume 29, issue 1
Nonlin. Processes Geophys., 29, 77–92, 2022
Nonlin. Processes Geophys., 29, 77–92, 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.

Related authors

Ensemble Riemannian data assimilation over the Wasserstein space
Sagar K. Tamang, Ardeshir Ebtehaj, Peter J. van Leeuwen, Dongmian Zou, and Gilad Lerman
Nonlin. Processes Geophys., 28, 295–309,,, 2021
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
Applying prior correlations for ensemble-based spatial localization
Chu-Chun Chang and Eugenia Kalnay
Nonlin. Processes Geophys., 29, 317–327,,, 2022
Short summary
A stochastic covariance shrinkage approach to particle rejuvenation in the ensemble transform particle filter
Andrey A. Popov, Amit N. Subrahmanya, and Adrian Sandu
Nonlin. Processes Geophys., 29, 241–253,,, 2022
Short summary
Inferring the instability of a dynamical system from the skill of data assimilation exercises
Yumeng Chen, Alberto Carrassi, and Valerio Lucarini
Nonlin. Processes Geophys., 28, 633–649,,, 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., 28, 565–583,,, 2021
Short summary
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

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
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.