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English(EN) Data-driven and physics-guided hindcasting of measured wave spectra

新模型利用数据和物理引导回溯预报波浪谱

研究人员开发了一种数据驱动和物理引导的波浪谱回溯预报模型,利用了2017年至2022年间波罗的海三个站点的数据。该模型结合了局部风、风区和深度,并使用ERA5再分析数据提供风力数据。结果表明,谱方差和峰值频率的监督能够产生最低的谱误差,而额外的峰值幅度监督则能降低波高误差。诸如弛豫等物理约束在减少RMSE方面显示出适度但依赖于目标的益处。 AI

影响 这项研究通过整合数据驱动方法和物理原理,有可能提高短期波浪预报的准确性。

排序理由 该集群包含一篇详细介绍新型波浪谱回溯预报模型的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv cs.LG 阅读 →

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新模型利用数据和物理引导回溯预报波浪谱

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该集群包含一篇详细介绍新型波浪谱回溯预报模型的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Sander Rikka ·

    数据驱动与物理引导的实测波浪谱回溯

    arXiv:2610.29306v1 Announce Type: cross Abstract: Short-range hindcasts of measured wave spectra are evaluated using a recurrent model initialized by one hourly buoy spectrum and supplied with local wind, fetch and depth. Measurements from three Baltic Sea stations are combined f…