Articles | Volume 29, issue 3
https://doi.org/10.5194/npg-29-317-2022
https://doi.org/10.5194/npg-29-317-2022
Research article
 | 
01 Sep 2022
Research article |  | 01 Sep 2022

Applying prior correlations for ensemble-based spatial localization

Chu-Chun Chang and Eugenia Kalnay

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Cited articles

Anderson, J. and Lei, L.: Empirical localization of observation impact in ensemble Kalman filters, Mon. Weather Rev., 141, 4140–4153, https://doi.org/10.1175/MWR-D-12-00330.1, 2013. 
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. 
Anderson, J. L.: Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter, Physica D, 230, 99–111, https://doi.org/10.1016/j.physd.2006.02.011, 2007. 
Bishop, C. and Hodyss, D.: Ensemble covariances adaptively localized with ECO-RAP. Part 1: Tests on simple error models, Tellus A, 61, 84–96, https://doi.org/10.1111/j.1600-0870.2008.00371.x, 2009. 
Cohn, S. E., Da Silva, A., Guo, J., Sienkiewicz, M., and Lamich, D.: Assessing the effects of data selection with the DAO physical-space statistical analysis system, Mon. Weather Rev., 126, 2913–2926, https://doi.org/10.1175/1520-0493(1998)126<2913:ATEODS>2.0.CO;2, 1998. 
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Short summary
This study introduces a new approach for enhancing the ensemble data assimilation (DA), a technique that combines observations and forecasts to improve numerical weather predictions. Our method uses the prescribed correlations to suppress spurious errors, improving the accuracy of DA. The experiments on the simplified atmosphere model show that our method has comparable performance to the traditional method but is superior in the early stage and is more computationally efficient.