Weather and climate experts are divided on whether AI or more traditional methods are most effective. In this new model, Google’s researchers bet on both.
Researchers from Google have built a new weather prediction model that combines machine learning with more conventional techniques, potentially yielding accurate forecasts at a fraction of the current cost.
The model, called NeuralGCM and described in a paper in Nature today, bridges a divide that’s grown among weather prediction experts in the last several years.
It then incorporates AI, which tends to do well where those larger models fall flat—typically for predictions on scales smaller than about 25 kilometers, like those dealing with cloud formations or regional microclimates (San Francisco’s fog, for example).
But the real promise of technology like this is not in better weather predictions for your local area, says Aaron Hill, an assistant professor at the School of Meteorology at the University of Oklahoma, who was not involved in this research.
That means the best climate models are hamstrung by the high costs of computing power, which presents a real bottleneck to research.
While many of the AI skeptics in weather forecasting have been won over by recent developments, according to Hill, the fast pace is hard for the research community to keep up with.
The original article contains 773 words, the summary contains 188 words. Saved 76%. I’m a bot and I’m open source!
This is the best summary I could come up with:
Researchers from Google have built a new weather prediction model that combines machine learning with more conventional techniques, potentially yielding accurate forecasts at a fraction of the current cost.
The model, called NeuralGCM and described in a paper in Nature today, bridges a divide that’s grown among weather prediction experts in the last several years.
It then incorporates AI, which tends to do well where those larger models fall flat—typically for predictions on scales smaller than about 25 kilometers, like those dealing with cloud formations or regional microclimates (San Francisco’s fog, for example).
But the real promise of technology like this is not in better weather predictions for your local area, says Aaron Hill, an assistant professor at the School of Meteorology at the University of Oklahoma, who was not involved in this research.
That means the best climate models are hamstrung by the high costs of computing power, which presents a real bottleneck to research.
While many of the AI skeptics in weather forecasting have been won over by recent developments, according to Hill, the fast pace is hard for the research community to keep up with.
The original article contains 773 words, the summary contains 188 words. Saved 76%. I’m a bot and I’m open source!