  The goal of convective storm nowcasting is local prediction of severe and
imminent convective storms. Here, we consider the convective storm nowcasting
problem from the perspective of machine learning. First, we use a pixel-wise
sampling method to construct spatiotemporal features for nowcasting, and
flexibly adjust the proportions of positive and negative samples in the
training set to mitigate class-imbalance issues. Second, we employ a concise
two-stream convolutional neural network to extract spatial and temporal cues
for nowcasting. This simplifies the network structure, reduces the training
time requirement, and improves classification accuracy. The two-stream network
used both radar and satellite data. In the resulting two-stream, fused
convolutional neural network, some of the parameters are entered into a
single-stream convolutional neural network, but it can learn the features of
many data. Further, considering the relevance of classification and regression
tasks, we develop a multi-task learning strategy that predicts the labels used
in such tasks. We integrate two-stream multi-task learning into a single
convolutional neural network. Given the compact architecture, this network is
more efficient and easier to optimize than existing recurrent neural networks.
