Articles | Volume 22, issue 4
https://doi.org/10.5194/npg-22-485-2015
https://doi.org/10.5194/npg-22-485-2015
Research article
 | 
18 Aug 2015
Research article |  | 18 Aug 2015

Spectral diagonal ensemble Kalman filters

I. Kasanický, J. Mandel, and M. Vejmelka

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Cited articles

Anderson, B. D. O. and Moore, J. B.: Optimal Filtering, Prentice-Hall, Englewood Cliffs, NJ, 1979.
Anderson, J. L.: An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, 2884–2903, https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2, 2001.
Beezley, J. D., Mandel, J., and Cobb, L.: Wavelet ensemble Kalman filters, in: Vol. 2, Proceedings of IEEE IDAACS'2011, 15–17 September 2011, Prague, 514–518, https://doi.org/10.1109/IDAACS.2011.6072819, 2011.
Berre, L.: Estimation of synoptic and mesoscale forecast error covariances in a limited-area model, Mon. Weather Rev., 128, 644–667, https://doi.org/10.1175/1520-0493(2000)128<0644:EOSAMF>2.0.CO;2, 2000.
Berre, L., Pannekoucke, O., Desroziers, G., Stefanescu, S., Chapnik, B., and Raynaud, L.: A variational assimilation ensemble and the spatial filtering of its error covariances: increase of sample size by local spatial averaging, http://old.ecmwf.int/publications/library/ecpublications/_pdf/workshop/2007/Data_assimilation/Berre.pdf (last access 7 August 2015), 2007.
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Short summary
A new type of ensemble Kalman filter for data assimilation is developed, based on fast Fourier transform and wavelet transform. The method can work with minimal computational resources. We develop variants for several general types of observations, give a rigorous proof that the method improves the approximation of the state covariance, and present computational experiments showing that the new technique works reliably with very small ensembles and is stable over multiple analysis cycles.