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

TL;DR

This paper introduces a multi-channel 3D convolution network that simultaneously nowcasts storm initiation, growth, and advection using multi-source meteorological data, improving over traditional methods.

Contribution

It presents the first deep learning framework capable of nowcasting storm initiation, growth, and advection simultaneously with multi-source data without feature engineering.

Findings

Deep learning outperforms traditional extrapolation methods.

The proposed 3D-SCN effectively predicts storm initiation and growth.

Encouraging qualitative results for storm nowcasting.

Abstract

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.