论文标题
通过随机森林估算模型误差，增强桑托斯通道中的海洋变量预测
Enhancing Oceanic Variables Forecast in the Santos Channel by Estimating Model Error with Random Forests
论文作者
论文摘要
在这项工作中，我们改善了海面高度（SSH）和海洋场景中当前速度（速度和方向）的预测。我们通过诉诸随机森林来预测为巴西桑托斯通道开发的数值预测系统的误差。我们已经使用了Santos运营预测系统（SOF）和在2019年至2021年之间原位收集的数据。在先前的研究中，我们应用了类似的方法，用于通道入口中的当前速度，在这项工作中，我们扩展了应用程序以改善SHH预测，并在通道中包括其他四个站点。通过我们的方法，我们的方法平均降低了11.9％，而我们的方法的平均降低为38.7％。在预测变量和站点的14个组合中，我们还获得了一致性（IOA）的增加。
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.
