SWOT Data Assimilation with Correlated Error Reduction: Fitting Model and Error Together

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Yu Gao, Sarah T. Gille , Bruce D. Cornuelle, Matthew R, Mazloff


The Surface Water Ocean Topography (SWOT) satellite mission provides high-resolution two-dimensional sea surface height (SSH) data with swath coverage. However, spatially correlated errors affect these SSH measurements, particularly in the cross-track direction. The scales of errors can be similar to the scales of ocean features. Conventionally, instrumental errors and ocean signals have been solved for independently in two stages. Here, we have developed a one-stage procedure that solves for the correlated error at the same time that data are assimilated into a dynamical ocean model. This uses the ocean dynamics to distinguish ocean signals from observation errors. We test its performance relative to the two-stage method using simplified dynamics and a data set consisting of westward propagating Rossby waves, along with correlated instrumental errors of varying magnitudes. In a series of ensemble analyses, we found that the one-stage approach consistently outperforms the two-stage approach when estimating SSH signal and correlated errors. The one-stage approach can recover over 95% of the SSH signal, while skill for the two-stage approach drops significantly as error increases. Our findings suggest that solving for the correlated errors within the assimilation framework can provide an effective analysis approach, reducing the risks of confounding signal and instrument noise.




Oceanography, Oceanography and Atmospheric Sciences and Meteorology


satellite remote sensing, Bayesian estimation, least-squares fitting, Rossby Waves


Published: 2024-05-01 15:18

Last Updated: 2024-05-01 19:18


CC BY Attribution 4.0 International

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Data Availability (Reason not available):
Code for this project is available at https://github.com/sgille/swot_correlated_error or 10.5281/zenodo.11095818. . Data can be accessed via doi:10.5281/zenodo.10963448.