Interpretable Deep Learning for Spatial Analysis of Severe Hailstorms
Interpretable Deep Learning for Spatial Analysis of Severe Hailstorms
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DOI：
10.1175/mwr-d-18-0316.1
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发表时间：
2019-08-01
影响因子：
3.2
通讯作者：
Thompson, Gregory
中科院分区：
文献类型：
--
作者：
Gagne, David John, II;Haupt, Sue Ellen;Thompson, Gregory
深度学习模型，如卷积神经网络，利用多个专门的层来编码不同尺度的空间模式。在这项研究中，深度学习模型与标准机器学习方法进行了比较，这些方法基于对流允许的数值天气预报模型的高空动力学和热力学场来预测严重冰雹的概率。这项研究的数据来自2016年5月3日至6月3日NCAR对流允许集合中确定的风暴周围的补丁。机器学习模型经过训练，以预测在风暴检测后的一小时内，来自汤普森冰雹大小诊断的模拟表面冰雹大小是否超过25毫米。卷积神经网络与使用来自每个字段的空间均值或主成分分析的输入变量的逻辑回归进行比较。卷积神经网络在统计学上显著优于所有其他方法的Brier技能得分和接收器操作者特征曲线下的面积。通过特征重要性和特征优化对卷积神经网络的解释表明，该网络合成了有关环境和风暴形态的信息，这些信息与我们对冰雹生长的理解一致，包括大的直减率和有利于广泛上升气流的风切变廓线。网络中不同的神经元也记录不同的风暴模式，这些神经元的输出的大小被用来分析不同的风暴模式在NCAR集合的时空分布。
Deep learning models, such as convolutional neural networks, utilize multiple specialized layers to encode spatial patterns at different scales. In this study, deep learning models are compared with standard machine learning approaches on the task of predicting the probability of severe hail based on upper-air dynamic and thermodynamic fields from a convection-allowing numerical weather prediction model. The data for this study come from patches surrounding storms identified in NCAR convection-allowing ensemble runs from 3 May to 3 June 2016. The machine learning models are trained to predict whether the simulated surface hail size from the Thompson hail size diagnostic exceeds 25 mm over the hour following storm detection. A convolutional neural network is compared with logistic regressions using input variables derived from either the spatial means of each field or principal component analysis. The convolutional neural network statistically significantly outperforms all other methods in terms of Brier skill score and area under the receiver operator characteristic curve. Interpretation of the convolutional neural network through feature importance and feature optimization reveals that the network synthesized information about the environment and storm morphology that is consistent with our understanding of hail growth, including large lapse rates and a wind shear profile that favors wide updrafts. Different neurons in the network also record different storm modes, and the magnitude of the output of those neurons is used to analyze the spatiotemporal distributions of different storm modes in the NCAR ensemble.