Articles | Volume 31, issue 4
https://doi.org/10.5194/npg-31-463-2024
https://doi.org/10.5194/npg-31-463-2024
Research article
 | 
08 Oct 2024
Research article |  | 08 Oct 2024

A comparison of two nonlinear data assimilation methods

Vivian A. Montiforte, Hans E. Ngodock, and Innocent Souopgui

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Short summary
Advanced data assimilation methods are complex and computationally expensive. We compare two simpler methods, diffusive back-and-forth nudging and concave–convex nonlinearity, which account for change over time with the potential of providing accurate results with a reduced computational cost. We evaluate the accuracy of the two methods by implementing them within simple chaotic models. We conclude that the length and frequency of observations impact which method is better suited for a problem.
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