AI Data-Driven Sediments Dynamics Short Term Forecast From Observation in the Bay of Biscay

Characterization of suspended sediment dynamics in the coastal ocean provides essential information for scientific studies and operational challenges concerning, among others, turbidity, water transparency and the development of microorganisms using photosynthesis, which is critical for primary production. The complexity of the processes involved in sediment dynamics makes it difficult to predict surface dynamics. In the continuity of previous experiments, the 4DVarNet model having shown encouraging results with SSSC interpolations, it is tested in a 20-day forecasting problem. In addition to the learning architecture including the missing observation data, a protocol has been conceptualized to integrate different types of forcing to improve the reconstructions. The results of the method show that it is possible to produce satisfactory results. The results of the method show that it is possible to produce satisfactory results. The contribution of the input forcing is notable improving of 20% precision horizons. The study also highlights a characterization of the different input forcing and their effect on the system.