低估了海平面潮重建的极端情况
Ludovic Harter1,2, Lucia Pineau-Guillou3, Bertrand Chapron3
1IFREMER, Laboratoire d'Océanographie Physique et Spatiale, UMR 6523 (IFREMER, CNRS, IRD, UBO), IUEM, Brest, France. ludovic.harter@ird.fr.
Scientific reports
|June 27, 2024
概括
统计模型通过使用风力压力和波浪数据来改善风暴预测,减少对极端事件的低估. 神经网络通过分析大面积的大气压力,即使没有风力数据,也能提供准确的预测.
科学领域:
- 海洋学 海洋学 海洋学
- 气象学 天气学
- 气候科学 气候科学
背景情况:
- 风暴潮重建传统上依赖于数值模型,这些数值模型是计算密集的.
- 统计模型提供了一个具有成本效益的替代方案,通过将浪潮与气象和海洋学 (metocean) 变量联系起来.
- 现有的统计模型往往低估了极端激增事件.
研究的目的:
- 用统计模型减少极端风暴潮重建中的偏见.
- 为此目的,评估多重线性回归和神经网络的有效性.
- 调查不同预测因素对冲浪预测准确性的影响.
主要方法:
- 测试各种配置的多重线性回归和神经网络模型.
- 利用来自东北大西洋的14个长期潮表的数据.
- 将风速与风应力的使用进行比较,并将显著的波浪高度作为预测因素.
主要成果:
- 使用风压力作为预测因素,与风速相比,在极端浪潮重建中显著减少了偏差.
- 包括显著的波高进一步改善了在特定位置的极端浪潮预测.
- 大气再分析可能低估了19世纪的极端浪潮,当用这些模型分析时.
- 神经网络可以在没有风力数据的情况下预测极端浪潮,前提是大气压力数据覆盖了大面积.
结论:
- 风力压力和海浪高度是改善统计风暴浪潮模型的关键预测因素,特别是在极端事件中.
- 神经网络显示出准确的极端浪潮预测的前景,为空海相互作用和风力压力参数化提供了洞察力.
- 由于潜在的低估,历史大气再分析可能需要对极端冲浪事件进行重新评估.
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