DeepMind has proved once again the outstanding prowess of  neural networks. Working with the UK Met Office it has developed a deep-learning tool that can accurately predict the likelihood of rain in the next 90 minutes, one of weather forecasting’s toughest challenges.

Climate change is bringing an ever-increasing number of catastrophic weather events such as the devastating floods in Germany, Belgium and the Netherlands in July 2021 that claimed almost 200 lives with more than 700 injured. DeepMind's new tool, DMGR standing for Deep Generative Model of Rain, which can accurately predict where, when and how much rain will fall in the next 1-2 hours, could provide vital information to assist emergency services in this type of scenario.

DMGR is used for Nowcasting, the term for forecasting rain and other precipitation with the next 1-2 hours based on the most recent past high-resolution radar data.

In a paper published by Nature and on open access the 20-person Nowcasting team claimed:

"Using a systematic evaluation by more than 50 expert meteorologists, we show that our generative model ranked first for its accuracy and usefulness in 89% of cases against two competitive methods".

This illustration compares DGMR to the two alternatives, PySTEPS and UNet. 

A heavy precipitation event in April 2019 over the eastern US (Target is the observed radar). The generative approach DGMR balances intensity and extent of precipitation compared to an advection approach (PySTEPS), the intensities of which are often too high, and does not blur like deterministic deep learning methods (UNet).

The practical applicability of DeepMind's DGMR shows that it is making good on its undertaking to build on its experience of using deep learning to play games, recall the triumph of AlphaGo, and tackle real world problems. We have already reported on its contributions to quantum chemistry and to protein folding and now it has added meteorology to its growing list of skills.

More Information

Nowcasting the Next Hour of Rain (DeepMind blog)

Skilful precipitation nowcasting using deep generative models of radar (Nature)

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