Enhancing Oceanic Variables Forecast in the Santos Channel by Estimating Model Error with Random Forests

In this work we improve forecasting of Sea Surface A recent and promising line of work consists of combining Height (SSH) and current velocity (speed and direction) ML with physics-based models -- often referred to as in oceanic scenarios. We do so by resorting Physics-Informed Machine Learning (PIML). Such an approach to Random Forests so as to predict the error of a numerical aims to take advantage of both the power of pattern forecasting system developed for the Santos recognition given by ML approaches and the power of generalization Channel in Brazil. We have used the Santos Operational in unseen scenarios given by the physics-based Forecasting System (SOFS) and data collected model. in situ between the years of 2019 and 2021. This work expands on our previous work [Moreno et al., In previous studies we have applied similar methods 2022] where PIML was used to correct the error predicted for current velocity in the channel entrance, in by a numerical model of the speed of water current in a this work we expand the application to improve the measuring station. Our main contribution here consists of SHH forecast and include four other stations in the inserting a correction for the direction of the water current channel. We have obtained an average reduction and the sea surface height (SSH) predicted by the numerical of 11.9% in forecasting Root-Mean Square Error model into the PIML model. In addition, we expand the (RMSE) and 38.7% in bias with our approach. We corrections to other measurement stations in the Santos-São also obtained an increase of Agreement (IOA) in 10 Vicente-Bertioga Estuarine System region on the Brazilian of the 14 combinations of forecasted variables and coast.