Application of Multi-channel 3D-cube Successive Convolution Network for Convective Storm Nowcasting
This work addresses the problem of accurate short-term storm prediction for meteorology and public safety by integrating radar and re-analysis data, though it is incremental as it builds on existing deep learning frameworks.
The paper tackled convective storm nowcasting by developing a multi-channel 3D-cube successive convolution network (3D-SCN) that uses multi-source meteorological data to simultaneously predict storm initiation, growth, and advection, achieving better performance than traditional extrapolation methods with encouraging qualitative results.
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.