使用数据驱动模型进行渤海多元海面预测,Journal of Marine Science and Engineering

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使用数据驱动模型进行渤海多元海面预测
Journal of Marine Science and Engineering ( IF 2.7 ) Pub Date : 2023-11-01 , DOI: 10.3390/jmse11112096
Song Hu ₁ , Qi Shao _{1,

2,

3} , Wei Li ₁ , Guijun Han ₁ , Qingyu Zheng ₁ , Ru Wang ₁ , Hanyu Liu ₁

Affiliation

海洋环境变量的数据驱动预测通常集中于单个变量。然而，在真实的海洋环境中，不同海洋变量之间存在相关性。此外，海气相互作用在影响海洋环境的演变中发挥着重要作用。内部动力和外部驱动因素都促成了这些变化。在这项研究中，提出了一个利用渤海海面高度异常（SSHA）、海面温度（SST）和海面风（SSW）的数据驱动模型。该模型将多元经验正交函数 (MEOF) 与长短期记忆 (LSTM) 相结合。MEOF 分析用于 SSHA 和 SST 的多元数据集，考虑海面变量之间的相关性。引入SSW作为预测器来增强多元海面模型的可预测性。以渤海为例，对无风场效应模型、全耦合模型和本文提出的预测模型进行了对比试验。MEOF 分析用于海洋变量、大气变量以及大气和海洋组合变量的比较实验。结果表明，利用风场作为预报因子可以提高渤海SSHA和SST的预报精度。7 天预报中 SSHA 和 SST 的均方根误差 (RMSE) 分别为 0.016 m 和 0.3200 °C。

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Multivariate Sea Surface Prediction in the Bohai Sea Using a Data-Driven Model

Data-driven predictions of marine environmental variables are typically focused on single variables. However, in real marine environments, there are correlations among different oceanic variables. Additionally, sea–air interactions play a significant role in influencing the evolution of the marine environment. Both internal dynamics and external drivers contribute to these changes. In this study, a data-driven model is proposed using sea surface height anomaly (SSHA), sea surface temperature (SST), and sea surface wind (SSW) in the Bohai Sea. This model combines multivariate empirical orthogonal functions (MEOFs) with long and short-term memory (LSTM). MEOF analysis is used on the multivariate dataset of SSHA and SST, considering the correlation among sea surface variables. SSW is introduced as a predictor to enhance the predictability of the multivariate sea surface model. In the case of the Bohai Sea, the comparative tests of the model without wind field effect, the fully coupled model, and the proposed prediction model were carried out. MEOF analysis is employed in comparative experiments for oceanic variables, atmospheric variables, and combined atmospheric and oceanic variables. The results demonstrate that using wind field as a predictor can improve the forecast accuracy of SSHA and SST in the Bohai Sea. The root mean square errors (RMSE) for SSHA and SST in a 7-day forecast are 0.016 m and 0.3200 °C, respectively.

更新日期：2023-11-01

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