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In this work we improve forecasting of Sea Surface Height (SSH) and currentvelocity (speed and direction) in oceanic scenarios. We do so by resorting toRandom Forests so as to predict the error of a numerical forecasting systemdeveloped for the Santos Channel in Brazil. We have used the Santos OperationalForecasting System (SOFS) and data collected in situ between the years of 2019and 2021. In previous studies we have applied similar methods for currentvelocity in the channel entrance, in this work we expand the application toimprove the SHH forecast and include four other stations in the channel. Wehave obtained an average reduction of 11.9% in forecasting Root-Mean SquareError (RMSE) and 38.7% in bias with our approach. We also obtained an increaseof Agreement (IOA) in 10 of the 14 combinations of forecasted variables andstations.

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