MetNet: A Neural Weather Model for Precipitation Forecasting
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 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.
Cite
@article{arxiv.2003.12140,
title = {MetNet: A Neural Weather Model for Precipitation Forecasting},
author = {Casper Kaae Sønderby and Lasse Espeholt and Jonathan Heek and Mostafa Dehghani and Avital Oliver and Tim Salimans and Shreya Agrawal and Jason Hickey and Nal Kalchbrenner},
journal= {arXiv preprint arXiv:2003.12140},
year = {2020}
}