In this study, a deep learning model called Four Dimensional Residual Network (4D-ResNet) was proposed, which can capture both temporal and spatial information. Temperatures at various depths were predicted for the next 40 days using the last month's sea surface variables, and a spatio-temporal prediction of the thermocline was achieved. In addition to the satellite-observed sea surface parameters: sea surface temperature (SST), sea level anomaly (SLA), and sea surface wind (SSW), net heat flux (Qnet) was also included in the model input. Qnet can alter the density of the upper water, resulting in convection or improved stratification stability. The results indicate that the additional input of Qnet improves the model's accuracy, especially at the depth of the thermocline, where the RMSE reduced by up to 13.7%. The 4D-ResNet model has much lower estimation error compared to other models and successfully captures the seasonal characteristics of the thermocline.

中文翻译：

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海面热通量有助于预测南海温跃层


在这项研究中，提出了一种称为四维残差网络 （4D-ResNet） 的深度学习模型，该模型可以捕获时间和空间信息。使用上个月的海面变量预测了未来 40 天不同深度的温度，并实现了温跃层的时空预测。除了卫星观测的海面参数：海面温度 （SST）、海平面距平 （SLA） 和海面风 （SSW） 外，净热通量 （Qnet） 也包含在模型输入中。Qnet 可以改变上层水的密度，从而产生对流或提高分层稳定性。结果表明，Qnet 的额外输入提高了模型的准确性，尤其是在温跃层深度，其中 RMSE 降低了 13.7%。与其他模型相比，4D-ResNet 模型的估计误差要低得多，并成功捕获了温跃层的季节性特征。