An Interpretable Deep Inference Model With Dynamic Constraints for Forecasting the Evolution of Sea Surface Variables in the South China Sea

Abstract An interpretable deep inference forecasting model is designed to improve the forecasting capability of sea surface variables. By incorporating the air‐sea coupling mechanism as a dynamic constraint, the interpretability and forecasting performance of the model are improved. More specifically, our findings underscore the critical role of air‐sea interactions in forecasting sea surface variables, especially sea surface temperature (SST) variations induced by tropical cyclones (TCs). Additionally, Liang‐Kleeman information flow (IF), a causal inference method, is introduced to optimize the selection of predictors. Using satellite remote sensing data, our study demonstrates the model's capability in realizing sea surface multivariate forecasts in the South China Sea (SCS) within 10 days. More importantly, the experimental results prove the applicability of the model in both normal and extreme weather conditions, highlighting its effectiveness in enhancing sea surface variables forecasting.