AI-powered weather and climate models are set to change the future of forecasting, researchers say

AI hierarchy [credit: Lollixzc @ Wikipedia]

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Expensive setup, but ‘faster and cheaper’ forecasts is one of the claims. Whether they’re somehow better, particularly the AI-backed climate models, than existing models is another matter. This article features one of the latest ones under development, suggesting it may be able to do more than existing models in some areas of forecasting.
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A new system for forecasting weather and predicting future climate uses artificial intelligence (AI) to achieve results comparable with the best existing models while using much less computer power, according to its creators – says The Conversation (via Phys.org).

In a paper published in Nature, a team of researchers from Google, MIT, Harvard and the European Center for Medium-Range Weather Forecasts say their model offers enormous “computational savings” and can “enhance the large-scale physical simulations that are essential for understanding and predicting the Earth system.”

The NeuralGCM model is the latest in a steady stream of research models that use advances in machine learning to make weather and climate predictions faster and cheaper.
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The researchers compared NeuralGCM against other models using a standardized set of forecasting tests called WeatherBench 2.

For three- and five-day forecasts, NeuralGCM did about as well as other machine-learning weather models such as Pangu and GraphCast. For longer-range forecasts, over 10 and 15 days, NeuralGCM was about as accurate as the best existing traditional models.

NeuralGCM was also quite successful in forecasting less-common weather phenomena, such as tropical cyclones and atmospheric rivers.

Why machine learning?
Machine learning models are based on algorithms that learn patterns in the data they are fed with, then use this learning to make predictions. Because climate and weather systems are highly complex, machine learning models require vast amounts of historical observations and satellite data for training.

The training process is very expensive and requires a lot of computer power. However, after a model is trained, using it to make predictions is fast and cheap. This is a large part of their appeal for weather forecasting.
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Nobody is actually using machine learning-based weather models for day-to-day forecasting yet. However, it is a very active area of research—and one way or another, we can be confident that the forecasts of the future will involve machine learning.

Full article here.
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Image: AI hierarchy [credit: Lollixzc @ Wikipedia]