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A computer model that combines conventional weather-forecasting technology with machine learning has outperformed other artificial intelligence (AI)-based tools at .

The tool, described in Nature on 22 July, is the first machine-learning model to generate accurate ensemble weather forecasts — ones which present a range of scenarios. Its development opens the door for forecasting that is faster and less energy-intensive than existing tools, and more detailed than approaches based solely on AI.

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Hoyer and his team developed and trained NeuralGCM, a model that combines “aspects from a traditional physics-based atmospheric solver with some AI components”, says Hoyer. They used the model to produce short- and long-term weather forecasts, as well as climate projections. To assess NeuralGCM’s accuracy, the researchers compared its predictions with real-world data, as well as outputs from other models, including GCMs and those based purely on machine learning.

Like current machine-learning models, NeuralGCM could produce accurate short-term, deterministic weather forecasts — between one and three days in advance — while consuming a fraction of the power required by GCMs. But it made much fewer errors than other machine-learning models when producing long-term forecasts beyond seven days. In fact, NeuralGCM’s long-term forecasts were similar to predictions made by the European Centre for Medium-Range Weather Forecast’s ensemble model (ECMWF-ENS), a GCM that is widely regarded as the gold standard for weather forecasting.

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