Enhancing Oceanic Variables Forecast in the Santos Channel by Estimating Model Error with Random Forests. (arXiv:2208.05966v1 [physics.ao-ph])

In this work we improve forecasting of Sea Surface Height (SSH) and current
velocity (speed and direction) in oceanic scenarios. We do so by resorting to
Random Forests so as to predict the error of a numerical forecasting system
developed for the Santos Channel in Brazil. We have used the Santos Operational
Forecasting System (SOFS) and data collected in situ between the years of 2019
and 2021. In previous studies we have applied similar methods for current
velocity in the …

arxiv error forecast physics random random forests variables