Tidal and mean sea surface corrections from and for SWOT using a
spatially coherent variational Bayesian harmonic analysis
Abstract
The accuracy of global tidal and mean sea surface (MSS) models degrades
significantly in coastal and estuarine regions. These geophysical 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. This dataset
provides both the opportunity and the need to quantify and correct the
spatial variability of these errors. We develop a variational Bayesian
framework for tidal harmonic analysis and MSS estimation which can be
applied in the early stages of SWOT. The approach demonstrates superior
robustness to different types of noise contamination in comparison to
conventional least-squares approaches whilst providing full uncertainty
estimation. By imposing a spatially coherent inductive bias on the
model, we achieve superior harmonic constituent inference using
extremely sparsely sampled 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 at least 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. Analysis of the estimated MSS error highlights the danger
of interpolation and averaging in masking small-scale oceanographic
processes. Empirical corrections for the SWOT data products are provided
alongside an open-source Python package, VTide.