Data-Driven Spatio-Temporal Interpolation of Sea Surface Sediment Concentration from Satellite-Derived Data: An OSSE Case-Study in the Bay of Biscay

Due to complex natural and anthropogenic forcings, the dynamics of suspended sediments within the ocean water column remains difficult to monitor. Nowadays however, more and more available information is coming from in situ and satellite measurements, as well as from simulation models. Data assimilation methods propose to combine all this information to produce the most precise results, allowing better analyzes of the processes in play. Here a comparison of data-driven methods is presented. Optimal Interpolation (OI), Empirical Orthogonal Function (EOF) based and Kalman Filter based methods are compared to a new one using neural networks. The latter is a Data Interpolation method based on convolutional AutoEncoders (DinAE). Present results show that DinAE better performs compared to other methods, having the lowest error budget and the highest learning of high frequency events.