Articles | Volume 26, issue 3
Nonlin. Processes Geophys., 26, 175–193, 2019
Nonlin. Processes Geophys., 26, 175–193, 2019

Research article 24 Jul 2019

Research article | 24 Jul 2019

Data assimilation using adaptive, non-conservative, moving mesh models

Ali Aydoğdu et al.

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Cited articles

Alharbi, A. and Naire, S.: An adaptive moving mesh method for thin film flow equations with surface tension, J. Comput. Appl. Math., 319, 365–384,, 2017. a
Anderson, J. L. and Anderson, S. L.: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts, Mon. Weather Rev., 127, 2741–2758, 1999. a
Apte, A. and Jones, C. K. R. T.: The impact of nonlinearity in Lagrangian data assimilation, Nonlin. Processes Geophys., 20, 329–341,, 2013. a
Asch, M., Bocquet, M., and Nodet, M.: Data Assimilation: Methods, Algorithms, and Applications, Fundamentals of Algorithms, SIAM, Philadelphia, ISBN 978-1-611974-53-9, 2016. a
Babus̆ka, I. and Aziz, A.: On the Angle Condition in the Finite Element Method, SIAM J. Numer. Anal., 13, 214–226,, 1976. a
Short summary
Computational models involving adaptive meshes can both evolve dynamically and be remeshed. Remeshing means that the state vector dimension changes in time and across ensemble members, making the ensemble Kalman filter (EnKF) unsuitable for assimilation of observational data. We develop a modification in which analysis is performed on a fixed uniform grid onto which the ensemble is mapped, with resolution relating to the remeshing criteria. The approach is successfully tested on two 1-D models.