  Precipitation nowcasting is an important task for weather forecasting. Many
recent works aim to predict the high rainfall events more accurately with the
help of deep learning techniques, but such events are relatively rare. The
rarity is often addressed by formulations that re-weight the rare events.
Somehow such a formulation carries a side effect of making "blurry" predictions
in low rainfall regions and cannot convince meteorologists to trust its
practical usability. We fix the trust issue by introducing a discriminator that
encourages the prediction model to generate realistic rain-maps without
sacrificing predictive accuracy. Furthermore, we extend the nowcasting time
frame from one hour to three hours to further address the needs from
meteorologists. The extension is based on consecutive attentions across
different hours. We propose a new deep learning model for precipitation
nowcasting that includes both the discrimination and attention techniques. The
model is examined on a newly-built benchmark dataset that contains both radar
data and actual rain data. The benchmark, which will be publicly released, not
only establishes the superiority of the proposed model, but also is expected to
encourage future research on precipitation nowcasting.
