Abstract's details
Barotropic tide filtering in SWOT observations through data assimilation
Event: 2025 SWOT Science Team Meeting
Session: Deltas, Estuaries and Coasts
Presentation type: Poster
Recent analyses performed on harmonic constants derived from SWOT satellite observations, including data from both the Cal/Val phase and the Science orbit, have revealed the presence of significant non-tidal signals. These signals manifest as small to medium-scale spatial structures characteristic of surface ocean circulation, illustrating the challenge of accurately isolating the tidal component in these observations.
To address this issue, the present study uses the FES2022 tidal ensembles combined with the SpEnOI (Spectral Ensemble Optimal Interpolation) assimilation method. This approach offers the potential for highly precise filtering of the spurious ocean circulation signals by leveraging the spatial scale separation provided by the tidal ensembles. Through this assimilation technique, the fine-scale non-tidal structures is effectively isolated and excluded from the assimilation process, ensuring a cleaner retrieval of the tidal signal.
Our analysis focuses on both coastal areas, where tidal dynamics interact strongly with complex bathymetry and coastal processes, and offshore regions, where mesoscale and submesoscale ocean variability dominate. By evaluating the performance of this method across these diverse environments, the study assesses its capacity to improve tidal signal extraction and reduce omission errors in the context of SWOT-derived tide data. The outcomes are expected to contribute to enhancing the quality and reliability of tidal products derived from high-resolution satellite altimetry missions.
Back to the list of abstractTo address this issue, the present study uses the FES2022 tidal ensembles combined with the SpEnOI (Spectral Ensemble Optimal Interpolation) assimilation method. This approach offers the potential for highly precise filtering of the spurious ocean circulation signals by leveraging the spatial scale separation provided by the tidal ensembles. Through this assimilation technique, the fine-scale non-tidal structures is effectively isolated and excluded from the assimilation process, ensuring a cleaner retrieval of the tidal signal.
Our analysis focuses on both coastal areas, where tidal dynamics interact strongly with complex bathymetry and coastal processes, and offshore regions, where mesoscale and submesoscale ocean variability dominate. By evaluating the performance of this method across these diverse environments, the study assesses its capacity to improve tidal signal extraction and reduce omission errors in the context of SWOT-derived tide data. The outcomes are expected to contribute to enhancing the quality and reliability of tidal products derived from high-resolution satellite altimetry missions.