Causality‐Based Deep Learning Forecast of the Kuroshio Volume Transport in the East China Sea

Abstract

AbstractThe Kuroshio volume transport (KVT) in the East China Sea has enormous impacts on navigation, circulation structure, ecological environment, and local climate. In this study, we aim to forecast the daily variability of the KVT at three different sections using the deep learning method. We train the deep learning model using data from 1982 to 2008 and validate the model with data of 2009–2010 and subsequently test it with data of 2011–2015. Four deep learning models, including Artificial Neural Network, Temporal Convolutional Network, Gated Recurrent Unit, and Long Short‐Term Memory (LSTM) models, are first tested to choose the best prediction model. As a result, the LSTM has the best performance for the KVT prediction at each section. We then employ a multivariate causal analysis method to identify the factors affecting the KVT at the current section, such as upstream and downstream KVT, regional mean wind stress, sea surface height and temperature and combine this method with the LSTM model to construct an information flow causality‐based LSTM (IFC‐LSTM) model for predicting daily KVT variability. The results indicate that IFC‐LSTM has the highest forecast skill compared to the standard LSTM (only input KVT at the current section into the LSTM), ALL‐LSTM (input all nine variables into the LSTM), multiple linear regression, and persistence model, which can forecast the KVT variability from 23 to 27 days in advance at the three sections with relative improvement rates of 12.5%–50%.