Addressing Out-of-Sample Issues in Multi-Layer Convolutional Neural-Network Parameterization of Mesoscale Eddies Applied Near Coastlines

IF 4.6 2区 地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES
Cheng Zhang, Pavel Perezhogin, Alistair Adcroft, Laure Zanna
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Abstract

This study addresses the boundary artifacts in machine-learned (ML) parameterizations for ocean subgrid mesoscale momentum forcing, as identified in the online ML implementation from a previous study (Zhang et al., 2023, https://doi.org/10.1029/2023ms003697). We focus on the boundary condition (BC) treatment within the existing convolutional neural network (CNN) models and aim to mitigate the “out-of-sample” errors observed near complex coastal regions without developing new, complex network architectures. Our approach leverages two established strategies for placing BCs in CNN models, namely zero and replicate padding. Offline evaluations revealed that these padding strategies significantly reduce root mean squared error (RMSE) in coastal regions by limiting the dependence on random initialization of weights and restricting the range of out-of-sample predictions. Further online evaluations suggest that replicate padding consistently reduces boundary artifacts across various retrained CNN models. In contrast, zero padding sometimes intensifies artifacts in certain retrained models despite both strategies performing similarly in offline evaluations. This study underscores the need for BC treatments in CNN models trained on open water data when predicting near-coastal subgrid forces in ML parameterizations. The application of replicate padding, in particular, offers a robust strategy to minimize the propagation of extreme values that can contaminate computational models or cause simulations to fail. Our findings provide insights for enhancing the accuracy and stability of ML parameterizations in the online implementation of ocean circulation models with coastlines.

Abstract Image

求解近海中尺度涡旋多层卷积神经网络参数化中的样本外问题
本研究解决了海洋亚网格中尺度动量强迫的机器学习(ML)参数化中的边界伪影,正如先前研究中的在线ML实现所确定的那样(Zhang等人,2023,https://doi.org/10.1029/2023ms003697)。我们专注于现有卷积神经网络(CNN)模型中的边界条件(BC)处理,旨在减轻在复杂沿海地区附近观察到的“样本外”误差,而无需开发新的复杂网络架构。我们的方法利用了两种既定的策略来将bc放置在CNN模型中,即零填充和复制填充。离线评估表明,这些填充策略通过限制对权重随机初始化的依赖和限制样本外预测的范围,显著降低了沿海地区的均方根误差(RMSE)。进一步的在线评估表明,重复填充一致地减少了各种重新训练的CNN模型的边界伪影。相比之下,尽管这两种策略在离线评估中表现相似,但在某些重新训练的模型中,零填充有时会加剧工件。这项研究强调了在ML参数化中预测近岸亚网格力时,对开放水域数据训练的CNN模型进行BC处理的必要性。特别是,复制填充的应用程序提供了一种健壮的策略,可以最大限度地减少可能污染计算模型或导致模拟失败的极值的传播。我们的研究结果为提高ML参数化在具有海岸线的海洋环流模型在线实现中的准确性和稳定性提供了见解。
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来源期刊
Journal of Advances in Modeling Earth Systems
Journal of Advances in Modeling Earth Systems METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
11.40
自引率
11.80%
发文量
241
审稿时长
>12 weeks
期刊介绍: The Journal of Advances in Modeling Earth Systems (JAMES) is committed to advancing the science of Earth systems modeling by offering high-quality scientific research through online availability and open access licensing. JAMES invites authors and readers from the international Earth systems modeling community. Open access. Articles are available free of charge for everyone with Internet access to view and download. Formal peer review. Supplemental material, such as code samples, images, and visualizations, is published at no additional charge. No additional charge for color figures. Modest page charges to cover production costs. Articles published in high-quality full text PDF, HTML, and XML. Internal and external reference linking, DOI registration, and forward linking via CrossRef.
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