Preprints
https://doi.org/10.5194/npg-2024-3
https://doi.org/10.5194/npg-2024-3
24 Jan 2024
 | 24 Jan 2024
Status: this preprint is currently under review for the journal NPG.

A Comparison of Two Nonlinear Data Assimilation Methods

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

Abstract. Advanced numerical data assimilation (DA) methods, such as the four-dimensional variational (4DVAR) method, are elaborate and computationally expensive. Simpler methods exist that take time-variability into account, providing the potential of accurate results with a reduced computational cost. Recently, two of these DA methods were proposed for a nonlinear ocean model. The first method is Diffusive Back and Forth Nudging (D-BFN) which has previously been implemented in several complex models, most specifically, an ocean model. The second is the Concave-Convex Nonlinearity (CCN) method provided by Larios and Pei that has a straightforward implementation and promising results. D-BFN is less costly than a traditional variational DA system but it requires integrating the model forward and backward in time over a number of iterations, whereas CCN only requires integration of the forward model once. This paper will investigate if Larios and Pei's CCN algorithm can provide competitive results with the already tested D-BFN within simple chaotic models. Results show that observation density and/or frequency, as well as the length of the assimilation window, significantly impact the results for CCN, whereas D-BFN is fairly adaptive to sparser observations, predominately in time.

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

Status: open (until 20 Mar 2024)

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Vivian A. Montiforte, Hans E. Ngodock, and Innocent Souopgui
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, that 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 impacts which method is better suited for a problem.