On denoising satellite altimeter measurements for high-resolution geophysical signal analysis

Satellite radar altimeter observations are key to advanced studies in ocean dynamics, particularly those focusing on mesoscale processes. To resolve scales below about 100 km, because measurements often characterized by a low signal-to-noise ratio (SNR), low-pass filtering or least-squares curve fitting is generally applied smooth the data before analysis. Here, we present an alternative method. It based Empirical Mode Decomposition (EMD) developed analyze non-stationary and non-linear processes, which adaptively projects signal basis of empirical AM/FM functions called Intrinsic Modulation Functions (IMFs). Applied Gaussian noise signal, EMD provides set IMFs with predictable distribution energy that can be exploited wavelet-inspired threshold methods provide efficient approach denoising. The method performs local analysis SNR, does not require priori assumptions underlying geophysical e.g., its degree smoothness compliance particular mathematical framework. simply assumed sum piecewise-smooth deterministic part stochastic part. proposed EMD-based denoising therefore well suited for mapping features, such as strong gradients, extreme values without significant smoothing. Using Jason-2, Cryosat-2, Saral/AltiKa wave height measurements, effective means overlooked variability sea state at between km 25 range largely impacted SNR. Below spectral hump caused inhomogeneities footprint essentially dominates signal. In addition, consistent long-term monitoring under global conditions. step forward will enable better exploitation unique now covers more than years.