KunPeng: A Global Ocean Environmental Model

• URL: http://arxiv.org/abs/2504.04766v1
• Date: Mon, 07 Apr 2025 06:41:05 GMT
• Title: KunPeng: A Global Ocean Environmental Model
• Authors: Yi Zhao, Jiaqi Li, Haitao Xia, Tianjiao Zhang, Zerong Zeng, Tianyu Ren, Yucheng Zhang, Chao Zhu, Shengtong Xu, Hongchun Yuan,
• Abstract summary: This study migrates meteorological large-model techniques to the ocean domain, constructing the KunPeng global ocean environmental prediction model.<n>Aimed at the characteristics of marine space, we propose a terrain-adaptive mask constraint mechanism to mitigate effectively training divergence caused by abrupt gradients at land-sea boundaries.
• Score: 5.565814778529731
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Inspired by the similarity of the atmosphere-ocean physical coupling mechanism, this study innovatively migrates meteorological large-model techniques to the ocean domain, constructing the KunPeng global ocean environmental prediction model. Aimed at the discontinuous characteristics of marine space, we propose a terrain-adaptive mask constraint mechanism to mitigate effectively training divergence caused by abrupt gradients at land-sea boundaries. To fully integrate far-, medium-, and close-range marine features, a longitude-cyclic deformable convolution network (LC-DCN) is employed to enhance the dynamic receptive field, achieving refined modeling of multi-scale oceanic characteristics. A Deformable Convolution-enhanced Multi-Step Prediction module (DC-MTP) is employed to strengthen temporal dependency feature extraction capabilities. Experimental results demonstrate that this model achieves an average ACC of 0.80 in 15-day global predictions at 0.25$^\circ$ resolution, outperforming comparative models by 0.01-0.08. The average mean squared error (MSE) is 0.41 (representing a 5%-31% reduction) and the average mean absolute error (MAE) is 0.44 (0.6%-21% reduction) compared to other models. Significant improvements are particularly observed in sea surface parameter prediction, deep-sea region characterization, and current velocity field forecasting. Through a horizontal comparison of the applicability of operators at different scales in the marine domain, this study reveals that local operators significantly outperform global operators under slow-varying oceanic processes, demonstrating the effectiveness of dynamic feature pyramid representations in predicting marine physical parameters.

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