  The accumulated remote sensing data of altimeters and scatterometers have
provided a new opportunity to forecast the ocean states and improve the
knowledge in ocean/atmosphere exchanges. Few previous studies have focused on
sea level anomaly (SLA) multi-step forecasting by multivariate deep learning
for different modalities. For this paper, a novel multimodal fusion approach
named MMFnet is used for SLA multi-step forecasting in South China Sea (SCS).
First, a grid forecasting network is trained by an improved Convolutional Long
Short-Term Memory (ConvLSTM) network on daily multiple remote sensing data from
1993 to 2016. Then, an in-situ forecasting network is trained by an improved
LSTM network, which is decomposed by the ensemble empirical mode decomposition
(EEMD-LSTM), on real-time, in-situ and remote sensing data. Finally, the two
single-modal networks are fused by an ocean data assimilation scheme. During
the test period from 2017 to 2019, the average RMSE of the MMFnet (single-modal
ConvLSTM) is 4.03 cm (4.51 cm), the 15th-day anomaly correlation coefficient is
0.78 (0.67), the performance of MMFnet is much higher than those of current
state-of-the-art dynamical (HYCOM) and statistical (ConvLSTM, Persistence and
daily Climatology) forecasting systems. Sensitivity experiments analysis
indicates that, the MMFnet, which added CCMP SCAT products and OISST for SLA
forecasting, has improved the forecast range over a week and can effectively
produce 15-day SLA forecasting with reasonable accuracies.In an extension of
the validation over the North Pacific Ocean, MMFnet can calculate the
forecasting results in a few minutes, and we find good agreement in amplitude
and distribution of SLA variability between MMFnet and other classical
operational model products.
