| 25 Aug 2026
Fine-Scale Sea Level Anomaly Reconstruction Constrained by SWOT-Derived Decorrelation Scales
Abstract. The Surface Water and Ocean Topography (SWOT) mission reveals fine-scale ocean variability at wavelengths below 100 km. However, most oceanographic applications of satellite altimetry rely on gridded sea level anomaly (SLA) products derived from along-track observations, whose representation of fine-scale variability is strongly influenced by covariance assumptions. Here, we use SWOT-derived spatial and temporal decorrelation scales as observational constraints on the prior covariance structure for reconstructing fine-scale SLA variability. Autocorrelation functions derived from SWOT observations are used to quantify the global spatial and temporal decorrelation characteristics of SLA, and the resulting decorrelation scales are incorporated into an optimal interpolation framework to improve fine-scale SLA reconstruction. In two subpolar regions, the SWOT-informed mapping resolves SLA variability down to wavelengths of approximately 55 km and improves the representation of geostrophic velocity, strain, relative vorticity, Okubo–Weiss parameter, and eddy kinetic energy. In the Irminger Sea, the reconstructed fields reveal an earlier wintertime enhancement of fine-scale eddy kinetic energy that is attenuated in existing gridded products. These results demonstrate that SWOT observations can enhance SLA mapping not only by providing additional measurements, but also by constraining the covariance structure underlying the reconstruction of SLA variability.
The paper offers an important methodological idea for improving the mapping of the altimetric measurements. SWOT makes this possible due to its high resolution by allowing the spatial and temporal correlation scales to be estimated and incorporated into the interpolation.
The method looks reasonable given the currently available data (only a few years of SWOT), the limitations are mentioned and the assumptions justified. The estimated anisotropy of the spatial SLA autocorrelation structure can provide an important improvement in altimetry mapping. The study is novel, important and presented well and should be published after a minor revision/some clarifications. I have a couple of main questions and some minor comments.
The paper is well-written and brief, which is nice, however I find some of the methodological details difficult to follow. My first question is about the temporal autocorrelation scale. I am not sure how the Lt is used in the optimal interpolation – since the function used to derive Lt and the function used in the OI are different. I would appreciate more details on how the derived ACFs enter the OI.
It would also be nice to provide a link to a map of the SWOT CalVal phase coverage to explain to the reader the sparse coverage (lines 217—219).
My second question is about the fact that the nadir-only map in Section 4.3 reproduces the EKE cycle at least as well as the nadir+SWOT LOI product. This is surprising and I would appreciate more insight into this result. What if you consider different regions for this calculation? Do similarly high correlations remain? How would you explain it? Would it be possible to validate the LOI product also with some additional observations (maybe in situ oceanographic)? Additional validation could also be helpful because some of the validation data are not fully independent.
Minor comments:
Section 3: for reproducibility it would be great to know how exactly the ACFs for different pixels were aggregated – i.e., how do you go from figure 2a to figure 2b? The same applies to the temporal autocorrelations: how are the values weighted/averaged?
What percentage of the ACF estimates was excluded, and where are these excluded estimates located (could it potentially influence the resulting estimates – i.e., could the spatial distribution influence the analysis or systematically omit some features or regions)? Lines 205—206 say, that mainly coastal regions had bad fits – it would be nice to see the coverage.
Section 4.2: would be nice to have these regions marked also on Figure 4a. Bathymetry would be useful on Figure 9.