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arXiv · 2003.12140

MetNet: A Neural Weather Model for Precipitation Forecasting

Casper Kaae Sønderby·Lasse Espeholt·Jonathan Heek·Mostafa Dehghani·Avital Oliver·Tim Salimans·Shreya Agrawal·Jason Hickey·Nal Kalchbrenner
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Abstract

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.

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Casper Kaae Sønderby, Lasse Espeholt, Jonathan Heek, Mostafa Dehghani, Avital Oliver, Tim Salimans, Shreya Agrawal, Jason Hickey, Nal Kalchbrenner. 2020-03-24. MetNet: A Neural Weather Model for Precipitation Forecasting. https://arxiv.org/abs/2003.12140

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