Matteo ZAMBRA Thesis Defense

Abstract : The aim of this thesis is to investigate the simultaneous use of heterogeneous ocean datasets to improve the performance of predictive models used in scientific and operational fields for the simulation and analysis of the ocean and marine environment. Two distinct case studies were explored in the course of the thesis work. The first study focuses on the local estimation of wind speed at the sea surface from underwater soundscape measurements and atmospheric model products. The second study considers the spatial extension of the problem and the use of observations at different scales and spatial resolutions, from pseudo-observations simulating satellite images to time series measured by in-situ infrastructures. The recurring theme of this research is the multimodality of the data introduced into the model. In other words, to what extent and how the predictive model can benefit from the use of heterogeneous spatiotemporal information channels. The preferred methodological tool is a simulation system based on variational data assimilation and deep learning concepts.