Articles | Volume 13, issue 8
Geosci. Model Dev., 13, 3607–3625, 2020
https://doi.org/10.5194/gmd-13-3607-2020
Geosci. Model Dev., 13, 3607–3625, 2020
https://doi.org/10.5194/gmd-13-3607-2020

Development and technical paper 19 Aug 2020

Development and technical paper | 19 Aug 2020

An offline framework for high-dimensional ensemble Kalman filters to reduce the time to solution

Yongjun Zheng et al.

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Cited articles

Albergel, C., Munier, S., Leroux, D. J., Dewaele, H., Fairbairn, D., Barbu, A. L., Gelati, E., Dorigo, W., Faroux, S., Meurey, C., Le Moigne, P., Decharme, B., Mahfouf, J.-F., and Calvet, J.-C.: Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area, Geosci. Model Dev., 10, 3889–3912, https://doi.org/10.5194/gmd-10-3889-2017, 2017. a
Anderson, J. L.: An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, 2884–2903, 2001. a, b
Anderson, J. L. and Collins, N.: Scalable Implementations of Ensemble Filter Algorithms for Data Assimilation, J. Atmos. Ocean. Tech., 24, 1452–1463, https://doi.org/10.1175/JTECH2049.1, 2007. a
Bannister, R. N.: A review of operational methods of variational and ensemble-variational data assimilation, Q. J. Roy. Meteor. Soc., 143, 607–633, 2017. a
Bishop, C. H., Etherton, B. J., and Majumdar, S. J.: Adaptive sampling with the ensemble transform Kalman filter. Part I: theoretical aspects, Mon. Weather Rev., 129, 420–436, 2001. a, b
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Short summary
This study proposes a sophisticated dynamically running job scheme as well as an innovative parallel IO algorithm to reduce the time to solution of an offline framework for high-dimensional ensemble Kalman filters. The offline and online modes of ensemble Kalman filters are built to comprehensively assess their time to solution efficiencies. The offline mode is substantially faster than the online mode in terms of time to solution, especially for large-scale assimilation problems.