Application of Multi-channel 3D-cube Successive Convolution Network for Convective Storm Nowcasting

very short-term weather forecasting or nowcasting has attracted substantial attention in various fields. Existing methods can nowcast storm advection based on radar data. Due to the limitations of the radar observations, it is still challenging to nowcast storm initiation and growth. However, as the 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. It is of great importance to leverage these re-analysis data.This paper describes our first attempt to nowcast storm initiation, growth, and advection simultaneously under the framework of convolutional neural network using the very large multi-source meteorological data. To this end, we construct a multi-channel 3D-cube successive convolution network which leveraging both raw 3D radar and re-analysis data 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 our approach show encouraging results of nowcasting of storm initiation, growth, and advection.