WOAST : an Xarray package applying Wavelet Scattering Transform to geophysical data

Ocean plays a key role in regulating climate through the dynamical coupling between sea surface and the atmosphere. Understanding this coupling is a key issue in climate change modeling, but an adapted statistical representation is still lacking. A strong limitation comes from the non-Gaussianities existing inside a wind over waves surface layer, where wind flows are constrained by the sea state and the swell. We seek an approach to describe statistically the couplings across scales, which is poorly measured by the power spectrum. Recent developments in data science provide new tools as the Wavelet Scattering Transform (WST), which gives a low-variance statistical description of non-Gaussian processes and offers to go beyond the power spectrum representation. The latter is blind to position consistency between scales. To find the methodology, we applied the WST on 1D anemometer time series and 2D atmospheric simulations (LES) and compared them with well known statistical information. These analyses were made possible thanks to the development of WOAST (Wavelet Ocean-Atmosphere Scattering Transform) software. Computation of WST is mathematically embarrassingly parallel and the time consumption is mainly dominated by data access and memory management. Our preliminary geophysical analysis using WOAST and its efficiency of extracting unknown properties of intermittent processes will be shown through a jupyter notebook example. This work is part of the Astrocean project supported by 80Prime grants (CNRS).