The AI revolution in weather forecasting: Reactions and comments from the mediasphere
The accuracy and timeliness of weather forecasts can make all the difference in decision making processes. However, as private sector actors leverage artificial intelligence to develop more accurate forecasts than traditional models, experts and journalists try to identify new ethical, political, and technological challenges for the future of weather forecasting.
From ancient Babilonian and Egyptian civilizations — carefully observing natural phenomena to develop early weather predictions — to the current medium range forecasts understanding future weather has been a key component in efforts to improve societal conditions for centuries. Today, efforts to make weather forecasting more accurate and timely continues to evolve with a new player entering the arena: Artificial Intelligence (AI).
In July 2023 a paper published in Nature announced the beginning of a revolution in weather forecasting. It showed how AI weather forecasting tools, developed by Huawei Cloud, could predict weather with more accuracy than the industry gold-standard weather simulation system — the High Resolution Forecast (HRES), produced by the European Centre for Medium-Range Weather Forecasts (ECMWF).
Traditional forecasting works by using Numerical Weather Prediction (NWP), whose starting point are physics equations that simulate atmospheric behavior through principles like fluid dynamics and thermodynamics. Using observational data from weather stations and satellites, including temperature and wind speed, these models then process the data with complex and time consuming calculations performed by supercomputers.
In contrast, deep learning ushers in a new era in weather forecasting by taking an entirely different approach to solving the problem. These AI generated weather forecasts use data instead of physical equations to create a weather forecast system.
“AI tools are statistical models: they recognize patterns in training data sets composed of decades of observational weather records and information gleaned from physical forecasting. Thus these models may notice that the weather setup of a certain day resembles similar events in the past and make a forecast based on that pattern,” explains the Scientific American in an article discussing the latest development in AI weather forecasting tools.
Reactions to the latest development in AI tools for weather forecasting have been mixed, with many experts recognising their efficacy whilst at the same time highlighting that their future role will be complementary to traditional tools rather than outright replacing them. Furthermore, a growing concern revolves around the potential shift in ownership of weather forecasting from the public to the private domain.
What has changed in weather forecasting?
In November 2023, Google’s DeepMind also revealed their own weather-forecasting AI tool, in a paper published in Science, which proved to be even more accurate than Huawei’s. “Predictions were more accurate than those of traditional weather models in 90% of tested cases and displayed better severe event prediction for tropical cyclones, atmospheric rivers, and extreme temperatures,” writes the paper’s editor Jesse Smith.
The American tech giant’s tool is called GraphCast, a state-of-the-art AI model that predicts weather conditions up to 10 days in advance more accurately and much faster than HRES, explains lead author of the study, Remi Lam, when writing on the Google DeepMind Blog.
“GraphCast uses decades worth of historical weather data to learn a model of the cause and effect relationships that govern how Earth’s weather evolves, from the present into the future,” continues Lam.
The tool is not only generating more accurate weather predictions but also requires a lot less computational power. Making 10-day forecasts with GraphCast takes less than a minute compared to the hours needed when using a conventional HRES approach, which uses a supercomputer with hundreds of machines.
“The importance of the work of DeepMind and others like it ( such as the recent Pangu-Weather system designed by Chinese scientists) is that they demonstrate that you can achieve or even improve on the predictive forecasting of traditional models by using artificial intelligence,” says researcher Ignacio López Gómez when interviewed for the article. López also acknowledges that although AI models are expensive to train they are also able to perform weather forecasts much more efficiently once they are trained. “Instead of requiring supercomputers, AI-based predictions can even be done on personal computers within a reasonable amount of time.”
And GraphCast isn’t the only AI tool for weather prediction. Google DeepMind and Google Research have also developed a Nowcasting model that produces forecasts up to 90 minutes ahead, and MetNet-3, a regional weather forecasting model that is already being used in some parts of the US and Europe, produces more accurate 24-hour forecasts than any other system.
According to Scientific American, on top of Google, “NVIDIA and Huawei have both developed their own AI weather models. All are billed as having an accuracy that is comparable with or higher than that of the best non-AI forecasting computer models and have made a splash in meteorology,” reads the article.
These tools are a “reckoning moment” for weather prediction because they show that predictions can be made using historical data, says Aditya Grover, an assistant professor of computer science at UCLA, who developed ClimaX, a foundation model that allows researchers to do different tasks relating to modeling the Earth’s weather and climate, when talking to Technology Review.
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