Articles | Volume 26, issue 2
Nonlin. Processes Geophys., 26, 109–122, 2019
https://doi.org/10.5194/npg-26-109-2019
Nonlin. Processes Geophys., 26, 109–122, 2019
https://doi.org/10.5194/npg-26-109-2019
Research article
14 Jun 2019
Research article | 14 Jun 2019

A Bayesian approach to multivariate adaptive localization in ensemble-based data assimilation with time-dependent extensions

Andrey A. Popov and Adrian Sandu

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Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
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Cited articles

Anderson, J. L.: An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, 2884–2903, 2001. a
Anderson, J. L.: Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter, Physica D, 230, 99–111, 2007. a
Anderson, J. L.: Localization and sampling error correction in ensemble Kalman filter data assimilation, Mon. Weather Rev., 140, 2359–2371, 2012. a
Arakawa, A.: Computational design for long-term numerical integration of the equations of fluid motion: Two-dimensional incompressible flow. Part I, J. Comput. Phys., 1, 119–143, 1966. a
Asch, M., Bocquet, M., and Nodet, M.: Data assimilation: methods, algorithms, and applications, SIAM, Philadelphia, PA, USA, 2016. a, b
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Short summary
This work has to do with a small part of existing algorithms that are used in applications such as predicting the weather. We provide empirical evidence that our new technique works well on small but representative models. This might lead to creation of a better weather forecast and potentially save lives as in the case of hurricane prediction.