Articles | Volume 29, issue 2
Nonlin. Processes Geophys., 29, 241–253, 2022
Nonlin. Processes Geophys., 29, 241–253, 2022
Research article
22 Jun 2022
Research article | 22 Jun 2022

A stochastic covariance shrinkage approach to particle rejuvenation in the ensemble transform particle filter

Andrey A. Popov et al.

Related authors

A Bayesian approach to multivariate adaptive localization in ensemble-based data assimilation with time-dependent extensions
Andrey A. Popov and Adrian Sandu
Nonlin. Processes Geophys., 26, 109–122,,, 2019
Short summary

Related subject area

Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Theory
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
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
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

Cited articles

Acevedo, W., de Wiljes, J., and Reich, S.: Second-order accurate ensemble transform particle filters, SIAM J. Sci. Comput., 39, A1834–A1850, 2017. a, b, c, d, e, f
Aggarwal, C. C.: Neural networks and deep learning, Springer,, 2018. a
Anderson, J. L.: An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, 2884–2903, 2001. a, b
Anderson, J. L.: Localization and sampling error correction in ensemble Kalman filter data assimilation, Mon. Weather Rev., 140, 2359–2371, 2012. a
Asch, M., Bocquet, M., and Nodet, M.: Data assimilation: methods, algorithms, and applications, SIAM,, 2016. a, b, c
Short summary
Numerical weather prediction requires the melding of both computational model and data obtained from sensors such as satellites. We focus on one algorithm to accomplish this. We aim to aid its use by additionally supplying it with data obtained from separate models that describe the average behavior of the computational model at any given time. We show that our approach outperforms the standard approaches to this problem.