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首页> 外文期刊>Ocean Dynamics >Parameterization of typhoon-induced ocean cooling using temperature equation and machine learning algorithms: an example of typhoon Soulik (2013)
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Parameterization of typhoon-induced ocean cooling using temperature equation and machine learning algorithms: an example of typhoon Soulik (2013)

机译：利用温度方程和机器学习算法对台风引起的海洋冷却进行参数化：台风Soulik（2013）的示例

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摘要

This study proposed three algorithms that can potentially be used to provide sea surface temperature (SST) conditions for typhoon prediction models. Different from traditional data assimilation approaches, which provide prescribed initial/boundary conditions, our proposed algorithms aim to resolve a flow-dependent SST feedback between growing typhoons and oceans in the future time. Two of these algorithms are based on linear temperature equations (TE-based), and the other is based on an innovative technique involving machine learning (ML-based). The algorithms are then implemented into a Weather Research and Forecasting model for the simulation of typhoon to assess their effectiveness, and the results show significant improvement in simulated storm intensities by including ocean cooling feedback. The TE-based algorithm I considers wind-induced ocean vertical mixing and upwelling processes only, and thus obtained a synoptic and relatively smooth sea surface temperature cooling. The TE-based algorithm II incorporates not only typhoon winds but also ocean information, and thus resolves more cooling features. The ML-based algorithm is based on a neural network, consisting of multiple layers of input variables and neurons, and produces the best estimate of the cooling structure, in terms of its amplitude and position. Sensitivity analysis indicated that the typhoon-induced ocean cooling is a nonlinear process involving interactions of multiple atmospheric and oceanic variables. Therefore, with an appropriate selection of input variables and neuron sizes, the ML-based algorithm appears to be more efficient in prognosing the typhoon-induced ocean cooling and in predicting typhoon intensity than those algorithms based on linear regression methods.
机译：这项研究提出了三种算法，可用于为台风预报模型提供海面温度（SST）条件。与提供规定的初始/边界条件的传统数据同化方法不同，我们提出的算法旨在解决未来台风与海洋之间流量相关的SST反馈。这些算法中的两种基于线性温度方程（基于TE），另一种基于涉及机器学习的创新技术（基于ML）。然后将这些算法实施到“天气研究和预报”模型中，以模拟台风，以评估其有效性，结果通过包含海洋冷却反馈，显示出模拟风暴强度的显着改善。我基于TE的算法仅考虑了风引起的海洋垂直混合和上升过程，因此获得了天气条件和相对平稳的海面温度降温。基于TE的算法II不仅包含台风，还包含海洋信息，从而解决了更多的降温特征。基于ML的算法基于神经网络，该神经网络由多层输入变量和神经元组成，并根据其振幅和位置产生对冷却结构的最佳估计。敏感性分析表明，台风引起的海洋冷却是一个非线性过程，涉及多个大气和海洋变量的相互作用。因此，通过适当选择输入变量和神经元大小，与基于线性回归方法的算法相比，基于ML的算法在预测台风引起的海洋降温和预测台风强度方面似乎更为有效。

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  • 来源
    《Ocean Dynamics》 |2017年第9期|1179-1193|共15页
  • 作者

    Wei Jun; Jiang Guo-Qing; Liu Xin;

  • 作者单位

    Peking Univ, Dept Atmospher & Ocean Sci, Lab Climate & Ocean Atmosphere Studies, Beijing, Peoples R China;

    Peking Univ, Dept Atmospher & Ocean Sci, Lab Climate & Ocean Atmosphere Studies, Beijing, Peoples R China;

    Peking Univ, Dept Atmospher & Ocean Sci, Lab Climate & Ocean Atmosphere Studies, Beijing, Peoples R China|Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Tropical cyclones; Typhoons; Parameterization; Machine learning;

    机译：热带气旋;台风;参数化;机器学习;

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