Abstract
For decades now, satellite altimetric observations have been successfully integrated in numerical oceanographic models using data assimilation (DA). So far, sea surface height (SSH) data were provided by one-dimensional nadir altimeters. The next generation Surface Water and Ocean Topography (SWOT) satellite altimeter will provide two-dimensional wide-swath altimetric information with an unprecedented high resolution. This new type of SSH data is expected to strongly improve altimetric assimilation. However, the SWOT data is also expected to be affected by spatially correlated errors and, hence, can not be assimilated as easily as nadir altimeters. The present paper proposes to embed a state-of-the-art correlated-error reduction (CER) method for the SWOT data into an ensemble-based DA scheme. The DA with the new correlated-error reduced-data (CER-data) is implemented and tested in a simple SSH reconstruction problem using artificial SWOT data and a quasi-geostrophic model. The results show that, in an energetic large scale region, the DA with CER-data—in comparison to the classical DA—reduces the root-mean-square-error (RMSE) of the reconstruction in SSH by approximately 10%, in relative vorticity by 5% and in surface currents by 5–10%, and also slightly improves the noise-to-signal ratio and spectral coherence of the SSH signal at mesoscale (100–200 km) but with a small degradation on the large scales (>300 km). In a less energetic region, the DA with CER-data cuts down the RMSE in SSH by more than 50% on average therefore allowing a significantly more accurate reconstruction of SSH at mesoscale in terms of noise-to-signal ratio, spectral coherence, and power spectral density.
Highlights
In operational oceanography, the assimilation of altimetric data has become crucial to control the time evolution of oceanic surface flows as well as its impact on the circulation in the deeper ocean (Chelton et al, 2001; Fu and Cazenave, 2001; Fu and Chelton, 2001; Morrow and Le Traon, 2012; Stammer and Cazenave, 2017)
In OSMOSIS, no analysis was recently performed at day November 4, 2012 but the error across-track variations of previous observations that were forecast remain visible in the ensemble transform Kalman filter (ETKF) full errors reconstruction
This confirms the importance of assessing the impact of the Surface Water and Ocean Topography (SWOT) errors and the correlated-error reduction (CER)-data in an assimilation problem cycled in time
Summary
The assimilation of altimetric data has become crucial to control the time evolution of oceanic surface flows as well as its impact on the circulation in the deeper ocean (Chelton et al, 2001; Fu and Cazenave, 2001; Fu and Chelton, 2001; Morrow and Le Traon, 2012; Stammer and Cazenave, 2017). The new Surface Water and Ocean Topography (SWOT) satellite altimeter, planned for launch in 2021, will bring a large amount of two-dimensional high resolution data that should significantly improve altimetric assimilation. A 4D reconstruction of the upper ocean circulation has never been performed at these small scales, and will be difficult due to the discrepancy between the spatial and temporal resolutions. Another important challenge which is the focus of this article, comes from the fact that the SWOT data are expected to be impacted by large spatially correlated errors, especially in the across track direction (Gaultier et al, 2016; Esteban-Fernandez, 2017; Metref et al, 2019)
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