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

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 betwee...

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Bibliographic Details
Published in:Remote Sensing
Main Authors: Sammy Metref, Emmanuel Cosme, Julien Le Sommer, Nora Poel, Jean-Michel Brankart, Jacques Verron, Laura Gómez Navarro
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2019
Subjects:
SWOT
correlated errors
OSSE
projection
detrending
ensemble kalman filter
Science
Q
North Atlantic
Online Access:https://doi.org/10.3390/rs11111336
https://doaj.org/article/eefedec7b4b047daaaed4fa4ddf753a3
Description
Summary: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.

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