Multilayer Fusion Recurrent Neural Network for Sea Surface Height Anomaly Field Prediction
Yuan Zhou, Chang Lu, Keran Chen, Xiaofeng Li
Abstract
Sea surface height anomaly (SSHA) is vitally important for climate and marine ecosystems. This article develops a multilayer fusion recurrent neural network (MLFrnn) to achieve an accurate and holistic prediction of the SSHA field, given only as a series of past SSHA observations. The proposed approach learns long-term dependencies within the SSHA time series and spatial correlations among neighboring and remote regions. A new multilayer fusion cell as the building block of the MLFrnn model was designed, which fully fused spatial and temporal features. The daily average satellite altimeter SSHA data in the South China Sea from January 1, 2001, to May 13, 2019, were used to train and test the model. We performed a 21-day ahead SSHA prediction and our MLFrnn model has very high accuracy, with a root mean square error (RMSE) of 0.027 m. Compared with existing deep learning networks, the proposed model was superior both in prediction performance and stability, especially on the wide-scale and long-term predictions.