DeepMind AI can beat the best weather forecasts – but there is a catch

By using artificial intelligence to spot patterns in weather data, Google DeepMind says it can beat existing weather forecasts up to 99.7 per cent of the time, but data issues mean the approach is limited for now

By Matthew Sparkes

14 November 2023

Can AI tell you if you will need an umbrella?

SEBASTIEN BOZON/AFP via Getty Images

AI can predict the weather 10 days ahead more accurately than current state-of-the-art simulations, says AI firm Google DeepMind – but meteorologists have warned against abandoning weather models based in real physical principles and just relying on patterns in data, while pointing out shortcomings in the AI approach.

Existing weather forecasts are based on mathematical models, which use physics and powerful supercomputers to deterministically predict what will happen in the future. These models have slowly become more accurate by adding finer detail, which in turn requires more computation and therefore ever more powerful computers and higher energy demands.

Rémi Lam at Google DeepMind and his colleagues have taken a different approach. Their GraphCast AI model is trained on four decades of historical weather data from satellites, radar and ground measurements, identifying patterns that not even Google DeepMind understands. “Like many machine-learning AI models, it’s not very easy to interpret how the model works,” says Lam.

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To make a forecast, it uses real meteorological readings, taken from more than a million points around the planet at two given moments in time six hours apart, and predicts the weather six hours ahead. Those predictions can then be used as the inputs for another round, forecasting a further six hours into the future.

Researchers at DeepMind ran this process with data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to create a 10-day forecast. They say it beat the ECMWF’s “gold-standard” high-resolution forecast (HRES) by giving more accurate predictions on more than 90 per cent of tested data points. At some altitudes, this accuracy rose as high as 99.7 per cent.