Articles | Volume 19, issue 10
https://doi.org/10.5194/gmd-19-4497-2026
https://doi.org/10.5194/gmd-19-4497-2026
Model description paper
 | 
27 May 2026
Model description paper |  | 27 May 2026

The Normalized Interpolated Convolution from an Adaptive Subgrid (NICAS) method

Benjamin Ménétrier

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Cited articles

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. Meteorol. Soc., 134, 1951–1970, 2008a. a, b
Bannister, R. N.: A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics, Q. J. Roy. Meteorol. Soc., 134, 1971–1996, 2008b. a, b
Bannister, R. N.: Balance conditions in variational data assimilation for a high-resolution forecast model, Q. J. Roy. Meteorol. Soc., 147, 2917–2934, 2021. a
Bridson, R.: Fast poisson disk sampling in arbitrary dimensions, SIGGRAPH sketches, ACM, p. 22, https://doi.org/10.1145/1278780.1278807, 2007. a
Buehner, M.: Ensemble-derived stationary and flow-dependent background-error covariances: Evaluation in a quasi-operational NWP setting, Q. J. Roy. Meteorol. Soc., 131, 1013–1043, 2005. a
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Short summary
The application of very large correlation operators to vectors is an persistent challenge for variational data assimilation. It must be accurate, fast and scalable. This article proposes a new generic method that works for any model grid, relying on adaptive subgrids to achieve this goal, even with advanced correlation functions. It describes the motivations and advantages of this method and its limitations depending on a few key parameters of the problem.
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