Towards improved analysis of short mesoscale sea level signals from satellite altimetry

Abstract. Satellite altimeters routinely supply sea surface height (SSH) measurements, which are key observations for monitoring ocean dynamics. However, below a wavelength of about 70 km, along-track altimeter measurements often characterized by dramatic drop in signal-to-noise ratio (SNR), making it very challenging to fully exploit the available precisely analyze small mesoscale variations SSH. Although various approaches have been proposed and applied identify filter noise from no distinct methodology has emerged systematic application operational products. To best address this unresolved issue, Copernicus Marine Environment Monitoring Service (CMEMS) actually provides simple band-pass filtered data mitigate contamination SSH signals. More innovative suitable filtering methods thus left users seeking unveil small-scale As demonstrated here, data-driven approach is developed successfully provide robust estimates noise-free level anomaly (SLA) signals (Quilfen, 2021). The method combines empirical mode decomposition (EMD), used help non-stationary non-linear processes, an adaptive technique inspired discrete wavelet transform (DWT) decompositions. It found resolve distribution SLA variability 30–120 km band. A practical uncertainty variable attached denoised that accounts errors related local SNR but also uncertainties denoising process, assumes results part stochastic process. For period, Jason-3, Sentinel-3, SARAL/AltiKa missions processed analyzed, their energy spectral seasonal distributions domain. In anticipation upcoming SWOT (Surface Water Ocean Topography) mission data, SASSA (Satellite Altimeter Short-scale Signals Analysis, https://doi.org/10.12770/1126742b-a5da-4fe2-b687-e64d585e138c, Quilfen Piolle, 2021) set three reference already shown yield valuable opportunities evaluate global kinetic distributions.