Automatic Detection of Stationary Fronts around Japan Using a Deep Convolutional Neural Network

Daisuke Matsuoka; Shiori Sugimoto; Yujin Nakagawa; Shintaro Kawahara; Fumiaki Araki; Yosuke Onoue; Masaaki Iiyama; Koji Koyamada

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Automatic Detection of Stationary Fronts around Japan Using a Deep Convolutional Neural Network

机译：使用深卷积神经网络自动检测日本周围的固定前线

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In this study, a stationary front is automatically detected from weather data using a U-Net deep convolutional neural network. The U-Net trained the transformation process from single/multiple physical quantities of weather data to detect stationary fronts using a 10-year data set. As a result of applying the trained U-Net to a 1-year untrained data set, the proposed approach succeeded in detecting the approximate shape of seasonal fronts with the exception of typhoons. In addition, the wind velocity (zonal and meridional components), wind direction, horizontal temperature gradient at 1000 hPa, relative humidity at 925 hPa, and water vapor at 850 hPa yielded high detection performance. Because the shape of the front extracted from each physical quantity is occasionally different, it is important to comprehensively analyze the results to make a final determination.

机译：在这项研究中，使用U-Net Deep Roadolutional Neural网络从天气数据自动检测到静止前线。 U-Net从单/多个物理数量的天气数据中培训了转换过程，以使用10年的数据集检测静止前线。由于将训练有素的U-Net应用于1年未经训练的数据集，所提出的方法成功地检测了季节性前沿的近似形状，除了台风。此外，风速（Zonal和子午子），风向，1000HPa的水平温度梯度，925HPa的相对湿度，850 HPA的水蒸气产生高的检测性能。由于从每个物理量提取的前部的形状偶尔不同，因此全面分析结果以进行最终确定。

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《SOLA: Scientific Online Letters on the Atmosphere》 |2019年第specialaedition期|共6页
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Daisuke Matsuoka; Shiori Sugimoto; Yujin Nakagawa; Shintaro Kawahara; Fumiaki Araki; Yosuke Onoue; Masaaki Iiyama; Koji Koyamada;
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Automatic Detection of Stationary Fronts around Japan Using a Deep Convolutional Neural Network

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