CoAuthors

Bertrand Chapron (IFREMER, France)

Satellite radar altimeter observations are key to advance studies in ocean dynamics, with a particular focus on mesoscale processes. To resolve scales less than about 100 km, altimeter measurements are often characterized by low signal-to-noise ratio (SNR), and low-pass filtering or least-square curve fitting is generally applied to smooth the data before analysis. An alternative method is presented. It is based on Empirical Mode Decomposition (EMD) developed to analyze non-stationary and non-linear processes, which adaptively projects a signal on a basis of empirical AM / FM functions, called Intrinsic Modulation Functions (IMFs). Applied to a Gaussian noise signal, EMD provides a set of IMFs with a predictable distribution of noise energy, to be exploited by wavelet-inspired threshold methods for an efficient data denoising approach. The EMD method ensures a local SNR analysis, does not require a priori assumptions about the underlying geophysical signal, e.g. its degree of smoothness or its compliance with a particular mathematical framework. The signal is simply assumed to be the sum of a piecewise-smooth deterministic part and a stochastic part. The proposed EMD-based denoising approach is thus well suited for mapping non-linear features, such as strong gradients, and extreme values without significant smoothing. Using Jason-2, Cryosat-2, and Saral/AltiKa significant wave height measurements, the proposed method provides efficient means to map overlooked geophysical sea state variability at scales much below 100 km, i.e. a range of scales largely impacted by low SNRs. Furthermore, it further provides a consistent approach for long-term noise analysis and monitoring over global and local conditions. The proposed methodology is a step forward to better exploit the unique set of altimeter observations which now covers more than 25 years.