How machine learning will help us prepare for extreme weather events
Here’s a topic I’ve been interested in for a while: using machine learning for weather modeling or forecasting. I came across this model published by DeepMind, the company founded by Demis Hassabis, Shane Legg and Mustafa Suleyman in 2010, and acquired by Alphabet for $650 million in 2014. It’s something that one of the co-founders of BigML, Tom Dietterich, has also been working on for some time, and that I’ve also had the opportunity to talk to him about on occasion.
The model, called GraphCast, outperforms the most accurate systems created by European and US government agencies, improving on 90% of the 1,380 verification targets, and very accurately forecasting severe events such as tropical cyclones, atmospheric rivers, heat waves, and other extreme temperatures.
On a planet suffering increasing destabilization due to a man-made climate emergency, it is vital to have systems capable of reliably forecasting the evolution of meteorological phenomena. Which is why DeepMind has created a machine learning model based on the re-analysis of more than forty years of historical data annotated with the climatological evolutions that they generated at the time, and using the computing capacity necessary to manage a model with several hundred variables. The result is a model capable of predicting hundreds of…
