摘要
In this study,convolutional long short-term memory(ConvLSTM)model is used to predict sea level anomaly(SLA)in the Kuroshio Extension(KE)region,utilizing daily satellite altimetry data(1993-2016).The model captures regional averaged SLA variability,achieving a correlation coefficient of 0.98 for prediction horizon up to 23 d.Propagating features of Rossby waves are also reproduced in the prediction model.While in spatial,discrepancies between predicted SLA and observed SLA are quite large,especially in regions with strong eddy activities.Incorporating equation of motion for the 11/2-layer reduced-gravity model,the performance of the model has a significant improvement spatially and temporally.Challenges persist in high-variability regions,underscoring the need for advanced models.This study highlights ConvLSTM’s potential for SLA forecasting with wind driven physical constraints,offering insights into wind-driven and eddy-influenced processes in the KE region.
基金
The National Natural Science Foundation of China under contract No.42276014
Jiangsu Natural Resources Development Special Fund(Marine Science and Technology Innovation)under contract No.JSZRKJ202403.
