论文标题
Metnet：用于降水预测的神经天气模型
MetNet: A Neural Weather Model for Precipitation Forecasting
论文作者
论文摘要
天气预报是一项长期的科学挑战，具有直接的社会和经济影响。由于大量连续收集的数据以及呈现远距离依赖性的丰富空间和时间结构，该任务适用于深层神经网络。我们介绍了Metnet，这是一个神经网络，预测未来8小时的降水量以1 km $^2 $的高空间分辨率，并且在2分钟的时间分辨率下，延迟的时间为秒。 Metnet作为输入雷达和卫星数据以及预测交货时间，并产生概率沉淀图。该体系结构使用轴向自我注意力，从与一百万平方公里相对应的大输入贴片中汇总了全球环境。我们在各种降水阈值下评估了Metnet的性能，并发现Metnet在美国大陆的规模上以高达7到8小时的预测优于数值天气预测。
Weather forecasting is a long standing scientific challenge with direct social and economic impact. The task is suitable for deep neural networks due to vast amounts of continuously collected data and a rich spatial and temporal structure that presents long range dependencies. We introduce MetNet, a neural network that forecasts precipitation up to 8 hours into the future at the high spatial resolution of 1 km$^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.
