AI helps to produce breakthrough in weather and climate forecasting

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Artificial intelligence has helped to make a breakthrough in accurate long-range weather and climate predictions, according to research that promises advances in both forecasting and the wider use of machine learning.

Using a hybrid of machine learning and existing forecasting tools, a model led by Google called NeuralGCM successfully harnessed AI to conventional atmospheric physics models to track decades-long climate trends and extreme weather events such as cyclones, a team of scientists found.

This combination of machine learning with established techniques could provide a template for refining the use of AI in other fields from materials discovery to engineering design, the researchers suggest. NeuralGCM was much faster than traditional weather and climate forecasting and better than AI-only models at longer-term predictions, they said.

“NeuralGCM shows that when we combine AI with physics-based models, we can dramatically improve the accuracy and speed of atmospheric climate simulations,” said Stephan Hoyer, senior staff engineer at Google Research and a co-author of a paper on the work published in Nature.

The paper said NeuralGCM proved faster, more accurate and used less computing power in tests against a current forecasting model based on atmospheric physics tools called X-SHiELD, which is being developed by an arm of the US National Oceanic and Atmospheric Administration.

In one trial, NeuralGCM identified almost the same number of tropical cyclones as conventional extreme weather trackers did, and twice the number of X-SHiELD. In another test based on temperature and humidity levels during 2020, the error rate was between 15 and 50 per cent less.

NeuralGCM’s calculations were able to generate 70,000 simulation days in 24 hours using one of Google’s customised AI tensor processing units, the paper says. By contrast, for comparable calculations, X-SHiELD generated only 19 simulation days, and needed 13,824 computer units to do it.

Google collaborated on the development of NeuralGCM with the inter-governmental European Centre for Medium-Range Weather Forecasts (ECMWF).

The European group made its model publicly available in June, and Google has made the code for NeuralGCM open access. It uses 80 years of ECMWF observational data and reanalysis for machine learning.

Google’s DeepMind unit last year unveiled an AI-only weather forecasting model called GraphCast, which outperformed conventional methods for periods up to 10 days ahead.

Established forecasting agencies such as the UK Met Office also have projects to integrate machine learning into their work.

Peter Dueben, head of the ECMWF’s earth system modelling and a co-author of the latest paper, said AI-only models were “often viewed sceptically” by experts because they were not based on mathematical equations devised from physics.

The combination of the physics-based model with the deep learning model “seems to get the best of both worlds”, he said, adding that the approach was a “big step towards climate modelling with machine learning”.

There was still more “work to do”, such as to enable NeuralGCM to estimate the impact of CO₂ increases on global surface temperatures, Dueben said. Other areas in which the model needed to be better included its capacity to simulate unprecedented climates, the paper said.

An expert not involved in the work, Cédric M. John, head of data science for the environment and sustainability at Queen Mary University of London, said there was “compelling evidence” that NeuralGCM was more accurate than machine learning alone and faster than the “full-physics” model. While there was still “room for improvement”, the possibility of error should be measurable and upgrades should be possible, he suggested.

“Importantly, this hybrid model does well at capturing an ensemble of predictions, and the practical implication of this is that an estimate of the uncertainty of the prediction can be derived,” said John.

Google has become involved in a growing number of environmental surveillance initiatives. It provides technological support for a satellite mission to track planet-warming emissions of methane and partners Nasa, the US space agency, to help local governments monitor air quality.