Weather forecasting is a long standing scientific challenge with direct
social and economic impact. The task is suitable for deep neural networks due
to vast amounts of continuously collected data and a rich spatial and temporal
structure that presents long range dependencies. We introduce MetNet, a neural
network that forecasts precipitation up to 8 hours into the future at the high
spatial resolution of 1 km$^2$ and at the temporal resolution of 2 minutes with
a latency in the order of seconds. MetNet takes as input radar and satellite
data and forecast lead time and produces a probabilistic precipitation map. The
architecture uses axial self-attention to aggregate the global context from a
large input patch corresponding to a million square kilometers. We evaluate the
performance of MetNet at various precipitation thresholds and find that MetNet
outperforms Numerical Weather Prediction at forecasts of up to 7 to 8 hours on
the scale of the continental United States.

利用深度神经网络，MetNet 能在高达 1 km² 的空间分辨率下、每 2 分钟一个时间间隔内预测未来 8 小时的降水情况。它使用轴向自注意力来聚合来自一个对应于 1 百万平方千米的大型输入块的全局上下文。研究表明，在大陆尺度上，MetNet 在各种降水阀值下的预测性能优于数值天气预报长达 7 至 8 小时。