Sammy Metref, Emmanuel Cosme, Florian Le Guillou, Julien Le Sommer, Jean-Michel Brankart, Jacques Verron

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 structured errors and, hence, can not be assimilated as easily as nadir altimeters. The present paper proposes to embed a state-of-the-art error detrending method for the SWOT data into an ensemble-based DA scheme. This new detrended-DA scheme 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 and when the region is intensely observed, the detrended-DA – in comparison to the classical DA – reduces the root-mean-square-error (RMSE) of the reconstruction in SSH, relative vorticity and surface currents and slightly improves the relative error spectrum and spectral coherence of the SSH signal at mesoscale (100-200km). In a less energetic region, the detrended-DA reduces on average by more than 50% the RMSE in SSH therefore allowing a significantly more accurate reconstruction of SSH at mesoscale in terms of relative error spectrum, spectral coherence and power spectral density.