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

TL;DR

This paper improves oceanic variable forecasts in the Santos Channel by using Random Forests to predict and correct model errors, significantly enhancing accuracy and bias reduction across multiple stations.

Contribution

The study introduces a novel application of Random Forests to estimate and correct errors in numerical ocean forecasts, expanding previous work to include SSH and multiple stations.

Findings

11.9% average reduction in RMSE

38.7% bias reduction

Increased agreement (IOA) in 10 of 14 variable-station combinations

Abstract

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 channel entrance, in this work we expand the application to improve the SHH forecast and include four other stations in the channel. We have obtained an average reduction of 11.9% in forecasting Root-Mean Square Error (RMSE) and 38.7% in bias with our approach. We also obtained an increase of Agreement (IOA) in 10 of the 14 combinations of forecasted variables and stations.