How Do Deep Learning Forecasting Models Perform for Surface Variables in the South China Sea Compared to Operational Oceanography Forecasting Systems?

It is fundamental and useful to investigate how deep learning forecasting models (DLMs) perform compared to operational oceanography forecast systems (OFSs). However, few studies have intercompared their performances using an identical reference. In this study, three physically reasonable DLMs are implemented for the forecasting of the sea surface temperature (SST), sea level anomaly (SLA), and sea surface velocity in the South China Sea. The DLMs are validated against both the testing dataset and the “OceanPredict” Class 4 dataset. Results show that the DLMs’ RMSEs against the latter increase by 44%, 245%, 302%, and 109% for SST, SLA, current speed, and direction, respectively, compared to those against the former. Therefore, different references have significant influences on the validation, and it is necessary to use an identical and independent reference to intercompare the DLMs and OFSs. Against the Class 4 dataset, the DLMs present significantly better performance for SLA than the OFSs, and slightly better performances for other variables. The error patterns of the DLMs and OFSs show a high degree of similarity, which is reasonable from the viewpoint of predictability, facilitating further applications of the DLMs. For extreme events, the DLMs and OFSs both present large but similar forecast errors for SLA and current speed, while the DLMs are likely to give larger errors for SST and current direction. This study provides an evaluation of the forecast skills of commonly used DLMs and provides an example to objectively intercompare different DLMs.