多通道三维立方体连续卷积网络在对流风暴临近预报中的应用
Application of Multi-channel 3D-cube Successive Convolution Network for Convective Storm Nowcasting
摘要：
对流风暴临近预报已引起各领域的广泛关注。深度学习框架下的现有方法主要依赖于雷达数据。虽然它们对临近预报风暴平流有很好的预报效果，但由于雷达观测资料的局限性，对风暴的发生和发展进行临近预报仍然具有挑战性。本文介绍了第一次尝试临近预报风暴的启动，增长，平流下同时使用多源气象数据的深度学习框架。为此，我们提出了一种多通道3D-cube连续卷积网络（3D-SCN）。由于实时再分析气象数据现在可以提供有价值的大气边界层热动力学信息，这对于预测风暴的形成和发展至关重要，因此直接使用原始3D雷达和再分析数据，而无需任何手工特征工程。这些数据被公式化为多通道3D立方体，被馈送到我们的网络中，这些数据通过跨通道3D卷积进行卷积。通过在没有池化的情况下堆叠连续的卷积层，我们构建了一个端到端的可训练模型来进行临近预报。实验结果表明，深度学习方法比传统的外推方法获得了更好的性能。3D-SCN的定性分析显示了令人鼓舞的结果，风暴的产生，发展和平流的临近预报。
英文摘要：
Convective storm nowcasting has attracted substantial attention in various fields. Existing methods under a deep learning framework rely primarily on radar data. Although they perform nowcast storm advection well, it is still challenging to nowcast storm initiation and growth, due to the limitations of the radar observations. This paper describes the first attempt to nowcast storm initiation, growth, and advection simultaneously under a deep learning framework using multi-source meteorological data. To this end, we present a multi-channel 3D-cube successive convolution network (3D-SCN). As real-time re-analysis meteorological data can now provide valuable atmospheric boundary layer thermal dynamic information, which is essential to predict storm initiation and growth, both raw 3D radar and re-analysis data are used directly without any handcraft feature engineering. These data are formulated as multi-channel 3D cubes, to be fed into our network, which are convolved by cross-channel 3D convolutions. By stacking successive convolutional layers without pooling, we build an end-to-end trainable model for nowcasting. Experimental results show that deep learning methods achieve better performance than traditional extrapolation methods. The qualitative analyses of 3D-SCN show encouraging results of nowcasting of storm initiation, growth, and advection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
