Articles | Volume 15, issue 15
https://doi.org/10.5194/gmd-15-6197-2022
https://doi.org/10.5194/gmd-15-6197-2022
Development and technical paper
 | 
11 Aug 2022
Development and technical paper |  | 11 Aug 2022

Hybrid ensemble-variational data assimilation in ABC-DA within a tropical framework

Joshua Chun Kwang Lee, Javier Amezcua, and Ross Noel Bannister

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Cited articles

Amezcua, J., Ide, K., Bishop, C. H., and Kalnay, E.: Ensemble clustering in deterministic ensemble Kalman filters, Tellus A, 64, 18039, https://doi.org/10.3402/tellusa.v64i0.18039, 2012. a
Asch, M., Bocquet, M., and Nodet, M.: Data Assimilation: Methods, Algorithms, and Applications, Fundamentals of Algorithms, SIAM, Society for Industrial and Applied Mathematics, https://books.google.co.uk/books?id=A3Q6vgAACAAJ (last access: 20 February 2022), 2016. a
Balci, N., Mazzucato, A. L., Restrepo, J. M., and Sell, G. R.: Ensemble dynamics and bred vectors, Mon. Weather Rev., 140, 2308–2334, 2012. a, b, c
Bannister, R.: A review of operational methods of variational and ensemble-variational data assimilation, Q. J. Roy. Meteor. Soc., 143, 607–633, 2017. a, b
Bannister, R. N.: A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances, Q. J. Roy. Meteor. Soc. A, 134, 1951–1970, 2008a. a
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In this article, we implement a novel data assimilation method for the ABC–DA system which combines traditional data assimilation approaches in a hybrid approach. We document the technical development and test the hybrid approach in idealised experiments within a tropical framework of the ABC–DA system. Our findings indicate that the hybrid approach outperforms individual traditional approaches. Its potential benefits have been highlighted and should be explored further within this framework.
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