Articles | Volume 29, issue 2
Nonlin. Processes Geophys., 29, 241–253, 2022
https://doi.org/10.5194/npg-29-241-2022
Nonlin. Processes Geophys., 29, 241–253, 2022
https://doi.org/10.5194/npg-29-241-2022
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.

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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, https://doi.org/10.1007/978-3-319-94463-0, 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, https://doi.org/10.1137/1.9781611974546, 2016. a, b, c
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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.