Abstract
The Surface Water and Ocean Topography (SWOT) mission is a next generation satellite mission expected to provide a 2 km-resolution observation of the sea surface height (SSH) on a two-dimensional swath. Processing SWOT data will be challenging because of the large amount of data, the mismatch between a high spatial resolution and a low temporal resolution, and the observation errors. The present paper focuses on the reduction of the spatially structured errors of SWOT SSH data. It investigates a new error reduction method and assesses its performance in an observing system simulation experiment. The proposed error-reduction method first projects the SWOT SSH onto a subspace spanned by the SWOT spatially structured errors. This projection is removed from the SWOT SSH to obtain a detrended SSH. The detrended SSH is then processed within an ensemble data assimilation analysis to retrieve a full SSH field. In the latter step, the detrending is applied to both the SWOT data and an ensemble of model-simulated SSH fields. Numerical experiments are performed with synthetic SWOT observations and an ensemble from a North Atlantic, 1/60° simulation of the ocean circulation (NATL60). The data assimilation analysis is carried out with an ensemble Kalman filter. The results are assessed with root mean square errors, power spectrum density, and spatial coherence. They show that a significant part of the large scale SWOT errors is reduced. The filter analysis also reduces the small scale errors and allows for an accurate recovery of the energy of the signal down to 25 km scales. In addition, using the SWOT nadir data to adjust the SSH detrending further reduces the errors.
Highlights
The upcoming Surface Water Ocean Topography (SWOT) satellite altimetry mission has the potential to provide dense and accurate information on ocean mesoscale and submesoscale flows [1,2,3]
The potential of the upcoming SWOT wide-swath altimetry mission lies in two characteristics: (i) The two-dimensionality of the wide-swath data will provide a new insight on the ocean surface dynamic where the evolution of structures can be tracked and studied, and (ii) the high resolution of the Ka-Band Radar Interferometer (KaRIn) instrument will reach very fine scale structures
In conjunction with the detrending, we propose a SWOT error reduction method based on a static ensemble data assimilation (DA)
Summary
The upcoming Surface Water Ocean Topography (SWOT) satellite altimetry mission has the potential to provide dense and accurate information on ocean mesoscale and submesoscale flows [1,2,3]. Some techniques to correct the SWOT data’s long-range correlated errors have been investigated by Dibarboure and Ubelmann [10]. These techniques are based on the cross-calibration of the satellite signal between multiple local zones in the satellite ground track. Information accumulated over a certain period is used to retrieve the SWOT signal free of error These techniques have shown promising results, they only gain in accuracy as long as the ocean state remains relatively static, which is not true, especially for the temporal/spatial scale ratio of SWOT. The benefits of comparing the different approaches could be explored
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