Reduction of Spatially Structured Errors in Wide-Swath Altimetric Satellite Data Using Data Assimilation

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.

Wide-Swath Altimetric Satellite Data Assimilation With Correlated-Error Reduction

“…Indeed, if the coefficients {α i } i=0,...,6 are not estimated accurately, the process could actually introduce artificial variations in the CER-data. Hence, similarly to Metref et al (2019), in order to increase the accuracy of the coefficient estimation, we make the assumption that the coefficients are constant along-track over each pass. Further, we justify the small impact of this assumption by the relatively small size of the regions of interest GULFSTREAM and OSMOSIS.…”

“…The residual of this projection, the SWOT correlated-error reduced-data (CERdata), is then used in the assimilation process instead of the SWOT data. As mentioned in Metref et al (2019), the CER-data are not a direct observation of SSH but a proxy of SSH. Hence, to keep the assimilation process consistent, the CER must be embedded in the observation operator of the DA scheme.…”

“…Also, 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).…”

“…In the present paper, we embed the correlated-error reduction (CER) procedure-that was developed by Metref et al (2019)into an assimilation scheme: the local ensemble transform Kalman filter (Hunt et al, 2007). The CER procedure focuses on the across-track variations of the correlated SWOT errors, its aim is to remove the part of the signal potentially impacted by these errors.…”

“…The paper is structured as follows. Section 2 recalls the errors expected to impact the SWOT data, describes the CER procedure from Metref et al (2019) and provides the theoretical grounds to embed the CER in an ensemble-based DA scheme. Section 3 implements the DA with CER-data and tests it in the assimilation problem of SSH reconstruction using a one and a half layer QG model.…”