Artificial intelligence has for the first time convincingly outperformed conventional forecasting methods at predicting weather around the world up to 10 days into the future. The GraphCast AI model “marks a turning point in weather forecasting”, its developers at Google DeepMind said in a peer-reviewed paper published in the journal Science on Tuesday. An extensive evaluation showed that GraphCast was more accurate than the world’s leading conventional system for predictions three to 10 days ahead, which is run by the European Centre for Medium-range Weather Forecasts. It outperformed the ECMWF product in 90 per cent of the 1,380 metrics used, which included temperature, pressure, wind speed and direction, and humidity at different levels of the atmosphere. Matthew Chantry, machine-learning co-ordinator at ECMWF, said AI systems in meteorology had progressed “far sooner and more impressively than we expected even two years ago”. ECMWF, an intergovernmental body based in Reading in the UK, has been running live forecasts by AI models from Huawei and Nvidia as well as DeepMind alongside its own integrated forecasting system. Chantry endorsed DeepMind’s claim that its system is the most accurate. “We find GraphCast to be consistently more skilful than the other machine-learning models, Pangu-Weather from Huawei and FourCastNet from Nvidia, and on lots of scores it is more accurate than our own forecasting system,” he told the Financial Times. GraphCast uses a machine-learning architecture called graph neural network, which learnt from more than 40 years of past ECMWF data about how weather systems develop and move around the globe.
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