Articles | Volume 30, issue 1
Research article
09 Jan 2023
Research article |  | 09 Jan 2023

Guidance on how to improve vertical covariance localization based on a 1000-member ensemble

Tobias Necker, David Hinger, Philipp Johannes Griewank, Takemasa Miyoshi, and Martin Weissmann

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Cited articles

Anderson, J.: Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter, Physica D, 230, 99–111,, 2007. a, b, c
Anderson, J. and Lei, L.: Empirical Localization of Observation Impact in Ensemble Kalman Filters, Mon. Weather Rev., 141, 4140–4153,, 2013. a, b, c
Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Avellano, A.: The Data Assimilation Research Testbed: A Community Facility, B. Am. Meteor. Soc., 90, 1283–1296,, 2009. a, b
Anderson, J. L.: An Ensemble Adjustment Kalman Filter for Data Assimilation, Mon. Weather Rev., 129, 2884–2903,<2884:AEAKFF>2.0.CO;2, 2001. a
Anderson, J. L.: Localization and Sampling Error Correction in Ensemble Kalman Filter Data Assimilation, Mon. Weather Rev., 140, 2359–2371,, 2012. a, b, c, d, e, f, g, h
Short summary
This study investigates vertical localization based on a convection-permitting 1000-member ensemble simulation. We derive an empirical optimal localization (EOL) that minimizes sampling error in 40-member sub-sample correlations assuming 1000-member correlations as truth. The results will provide guidance for localization in convective-scale ensemble data assimilation systems.