  Convective storms are one of the severe weather hazards found during the warm
season. Doppler weather radar is the only operational instrument that can
frequently sample the detailed structure of convective storm which has a small
spatial scale and short lifetime. For the challenging task of short-term
convective storm forecasting, 3-D radar images contain information about the
processes in convective storm. However, effectively extracting such information
from multisource raw data has been problematic due to a lack of methodology and
computation limitations. Recent advancements in deep learning techniques and
graphics processing units now make it possible. This article investigates the
feasibility and performance of an end-to-end deep learning nowcasting method.
The nowcasting problem was transformed into a classification problem first, and
then, a deep learning method that uses a convolutional neural network was
presented to make predictions. On the first layer of CNN, a cross-channel 3D
convolution was proposed to fuse 3D raw data. The CNN method eliminates the
handcrafted feature engineering, i.e., the process of using domain knowledge of
the data to manually design features. Operationally produced historical data of
the Beijing-Tianjin-Hebei region in China was used to train the nowcasting
system and evaluate its performance; 3737332 samples were collected in the
training data set. The experimental results show that the deep learning method
improves nowcasting skills compared with traditional machine learning methods.

Geophysics;Computer Vision and Pattern Recognition;Atmospheric and Oceanic Physics