Google's Deepmind AI Predicts Weather More Accurately Than Existing Forecasts
Arthur T Knackerbracket has processed the following story:
What to Know About Google's Breakthrough Weather Prediction ModelGenCast is the latest in DeepMind's ongoing research project to use artificial intelligence to improve weather forecasting. The model was trained on four decades of historical data from the European Centre for Medium-Range Weather Forecasts's (ECMWF) ERA5 archive, which includes regular measurements of temperature, wind speed and pressure at various altitudes around the globe.
Data up to 2018 was used to train the model and then data from 2019 was used to test its predictions against known weather. The company found that it beat ECMWF's industry-standard ENS forecast 97.4 per cent of the time in total, and 99.8 per cent of the time when looking ahead more than 36 hours.
[...] Existing weather forecasts are based on physics simulations run on powerful supercomputers that deterministically model and extrapolate weather patterns as accurately as possible. Forecasters usually run dozens of simulations with slightly different inputs in groups called ensembles to better capture a range of possible outcomes. These increasingly complex and numerous simulations are extremely computationally intensive and require ever more powerful and energy-hungry machines to operate.
AI could offer a less costly solution. For instance, GenCast creates forecasts with an ensemble of 50 possible futures, each taking just 8 minutes on a custom-made and AI-focused Google Cloud TPU v5 chip.
[...] But for now, GenCast does offer a way to run forecasts at lower computation cost, and more quickly. Kieran Hunt at the University of Reading, UK, says just as a collection of physics-based forecasts can generate better results than a single forecast, he believes ensembles will boost the accuracy of AI forecasts.
Hunt points to the record 40C (104F) temperatures seen in the UK in 2022 as an example. A week or two earlier, there were lone members of ensembles predicting it, but they were considered anomalous. Then, as we drew nearer to the heatwave, more and more forecasts fell in line, allowing early warning that something unusual was coming.
It does allow you to hedge a little if there is one member that shows something really extreme; it might happen, but it probably won't," says Hunt. I wouldn't view it as necessarily a step change. It's combining the tools that we've been using in weather forecasting for a while with the new AI approach in a way that will certainly work to improve the quality of AI weather forecasts. I've no doubt this will do better than the kind of first wave of AI weather forecasts."
Journal Reference:> NatureDOI: 10.1038/s41586-024-08252-9
upstart writes:
The Sun'll come out tomorrow, and you no longer have to bet your bottom dollar to be sure of it. Google's DeepMind team released its latest weather prediction model this week, which outperforms a leading traditional weather prediction model across the vast majority of tests put before it.
The generative AI model is dubbed GenCast, and it is a diffusion model like those undergirding popular AI tools including Midjourney, DALLE 3, and Stable Diffusion. Based on the team's tests, GenCast is better at predicting extreme weather, the movement of tropical storms, and the force of wind gusts across Earth's mighty sweeps of land. The team's discussion of GenCast's performance was published this week in Nature.
Where GenCast departs from other diffusion models is that it (obviously) is weather-focused, and "adapted to the spherical geometry of the Earth," as described by a couple of the paper's co-authors in a DeepMind blog post.
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