Treffer: Tidal Corrections From and for SWOT Using a Spatially Coherent Variational Bayesian Harmonic Analysis.

The accuracy of global tidal models degrades significantly in coastal and estuarine regions. These models are important for correcting measurements from satellite altimetry and are used in numerous scientific and engineering applications. The new Surface Water Ocean Topography (SWOT) mission is providing measurements at unprecedented horizontal resolution in these regions. These data present both the opportunity and the necessity to quantify and correct the spatial variability in the model inaccuracies specific to these regions. We develop a variational Bayesian framework for tidal harmonic analysis which can be applied to SWOT, and is especially useful for exploting the data from the Cal/Val phase. The approach demonstrates superior robustness to different types of noise contamination in comparison to conventional least‐squares approaches while providing full uncertainty estimation. By imposing a spatially coherent inductive bias on the model, we achieve superior harmonic constituent inference from temporally sparse but spatially dense data. Bayesian uncertainty estimation gives rise to statistical methods for outlier removal and constituent selection. Using our approach, we estimate a lower bound for the residual tidal variability for two SWOT Cal/Val passes (003 and 016) around the European Shelf to be 7% $7\%$ on average. We also show similar estimates cannot be produced using standard least‐squares approaches. Tide gauge validation in the same region confirms the superiority of our empirical approach in coastal environments. Empirical corrections for the SWOT data products are provided alongside an open‐source Python package, VTide. Plain Language Summary: Three decades of satellite altimetry has provided a rich data source for the study of oceanic processes. In order to use these data, and to study smaller processes, one must first remove larger oceanic signals: namely tides. Because the complexity of tides in coastal and estuarine regions, traditional modeling approaches often struggle. Although we know these models are not accurate in these regions, quantifying these errors remains a difficult task, as in situ measurements are sparse and often integrated into the models themselves. The new Surface Water Ocean Topography (SWOT) mission provides high‐resolution measurements in these regions, presenting both the need and the opportunity to quantify and correct these inaccuracies. We develop a Bayesian approach for predicting tides in these regions capable of exploiting the spatial information in the data. Our method outperforms traditional modeling techniques in these regions and allows us to estimate errors in existing tidal models. These findings could be useful for analyzing the more than 30 years of historical satellite altimetry data and highlights the potential of our variational Bayesian method in other geophysical studies. Key Points: Variational Bayesian harmonic analysis provides superior robustness to noise and uncertainty estimation of harmonic constituentsA spatially coherent inductive bias further improves tidal and mean sea surface estimationSWOT data are useful for both quantifying and correcting errors in present geophysical models [ABSTRACT FROM AUTHOR]

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