Mesoscale Eddy (ME) is a widely recognized and significant oceanic phenomenon characterized by extensive energy exchange. Accurate predictions of the future geospatial distribution of ME are crucial for maritime activities. Deep learning (DL) methods for ME prediction have consistently outperformed classical mathematical models and numerical modeling approaches. However, current DL methods based on two-dimensional gridded data suffer from error accumulation issues due to their indirect prediction approach, which involves first predicting the future environmental field and then identifying the ME patterns within it. Additionally, these models ignore the potential influence of prior knowledge related to ME on prediction outcomes. To address these issues, this paper proposes a direct prediction DL network called ES-ConvGRU, which incorporates historical prior statistics. First, we conduct an analysis of ME data from the Northwest Pacific region, extracting prior statistical knowledge such as interannual variations and seasonal characteristics. Second, mining patterns for the spatiotemporal features of both ME sequence and sea surface environmental field sequence are established. Furthermore, a nonlinear approach for mapping prior statistical knowledge and a multi-step autoregressive prediction model are designed, facilitating the integration of prior knowledge with multidimensional spatiotemporal features for ME prediction. Finally, the performance and effectiveness of ES-ConvGRU are evaluated. It achieves an average pixel accuracy of 93.21%, a mean intersection over union of 81.48%, and a frequency-weighted intersection over union of 87.55% for the 7-day distribution prediction. The results show that ES-ConvGRU exhibits notable advantages compared to the existing indirect and direct prediction methods.

中文翻译：

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通过先验统计深度学习直接预测海洋中尺度涡旋地理空间分布

中尺度涡流（ME）是一种广泛认可的重要海洋现象，其特征是广泛的能量交换。准确预测ME的未来地理空间分布对于海上活动至关重要。用于 ME 预测的深度学习 (DL) 方法始终优于经典数学模型和数值建模方法。然而，当前基于二维网格数据的深度学习方法由于其间接预测方法而存在误差累积问题，该方法首先需要预测未来的环境场，然后识别其中的 ME 模式。此外，这些模型忽略了与 ME 相关的先验知识对预测结果的潜在影响。为了解决这些问题，本文提出了一种称为 ES-ConvGRU 的直接预测深度学习网络，它结合了历史先验统计数据。首先，我们对西北太平洋地区的 ME 数据进行分析，提取先验统计知识，例如年际变化和季节特征。其次，建立了ME序列和海面环境场序列时空特征的挖掘模式。此外，还设计了映射先验统计知识的非线性方法和多步自回归预测模型，有助于将先验知识与多维时空特征相结合进行ME预测。最后，评估ES-ConvGRU的性能和有效性。对于 7 天分布预测，其平均像素精度为 93.21%，平均交集率为 81.48%，频率加权并集交集率为 87.55%。结果表明，与现有的间接和直接预测方法相比，ES-ConvGRU 表现出显着的优势。