TL;DR
A multivariate ocean forecasting model combining Kolmogorov-Arnold Networks (KAN), Transformer and Transformer is proposed based on Pangu-Weather to accurately capture and predict complex associations among ocean multi-variables to provide accurate and stable ocean forecasts.
Ocean forecasting is crucial for both scientific research and societal benefits. Large artificial intelligence (AI)-based models have recently boosted forecasting efficiency and accuracy. However, it remains challenging to develop a comprehensive AI-driven ocean forecasting system capable of integrating cross-spatiotem...
A deep-learning model based on ConvLSTM to forecast SST and MLD in the Bay of Bengal for up to four weeks ahead contributes to improved ocean variable forecasting and demonstrates potential for ISMR prediction and disaster management.
The complete end-to-end system of FengYuan achieves forecast quality very close to the reanalysis-driven version while consistently outperforming IFS throughout the 10-day forecast period, demonstrating that FengYuan successfully bridges observations to forecasts while maintaining high accuracy.
The results suggest that bidirectional Vision 15 Mamba is a strong backbone for neural weather prediction — achieving better accuracy and growing computational efficiency, consistent with a more suitable spatial inductive bias for atmospheric modeling.
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