David Bonacci (TéSA, France); Olivier Besson (TéSA/ISAE, France); Amandine Guillot (Centre National d'Etudes Spatiales (CNES), France); Sophie Le Gac (CNES, France); Nathalie Steunou (CNES, France); Cécile Cheymol (CNES, France); Nicolas Picot (CNES, France)

Spectral analysis of sea level anomalies (SLA) is widely used in the altimetry community to understand the geophysical content of the measured signal, to assess and compare the missions’ performances.
Spectral content of SLA is used to characterize the ocean at different scales as well as instrumental noise. Based on the SLA spectrum, one can estimate the spectral slope at medium to large scales (relied to the Surface Quasi-Geostrophic (SQG) ocean dynamics theory) and the measurement noise (observed as a noise plateau at smallest scales).
It has already been shown that the spectral slope strongly depends on ocean variability, both in time and space domains [1]. However, spectral analysis based on Fourier transform requires stationary signals and is well-known to suffer from a convolutive bias and a high variance of estimation [2]. Thus, using Fourier transforms for SLA spectral analysis requires mathematical caution and needs to be fully managed.
This study aims at reviewing applicability of Fourier transform-based methods to SLA analysis and comparing it to other spectral methods. Such comparison has been performed on both simulated SLA signals obtained from theoretical spectra and real signals from a high-resolution altimeter (Orbit – Range – Mean Sea Surface). Finally, a parametric spectral analysis method is proposed and suggested for use by the wider Cal/Val and altimetry science community.


[1] C. Dufau et al., Mesoscale capability of along-track altimeter data in LRM & SARM, OSTST Meeting, 2014.
[2] P. Stoica, R. Moses, Introduction to spectral analysis, Prentice Hall, 1997.