Huawei’s Pangu-Weather AI model can predict weather events in seconds: just released to the public for free


SHENZHEN, China, Aug. 4, 2023 /PRNewswire/ — July 2023 is likely to be recorded as the hottest month on record, and possibly the warmest in 120,000 years. The climate is warming, and as a result, the likelihood of extreme weather events is increasing. Traditional weather prediction requires huge amounts of computing power to work. Now a new AI-powered weather model is being released to the public that transforms the way weather is predicted.

Pangu-Weather, an AI model for weather prediction developed by HUAWEI CLOUD, enables more accurate weather forecasts to be made with a 10,000x improvement in prediction speeds, reducing global weather prediction times to just seconds. This facilitates the early prediction and preparation of extreme weather. These results were published in the peer-reviewed scientific publication Nature on July 5, 2023.

Pangu-Weather is the first AI prediction model with higher precision than traditional numerical prediction methods and is being released to the public for the first time, for free on the ECMWF (European Centre for Medium-Range Weather Forecasts) website. This provides global weather forecasters, meteorologists, weather enthusiasts, and the general public with a platform to view Pangu Weather Model’s 10-day global weather forecasts.

A “game-changer” for traditional weather forecasting

In addition to making 10-day weather forecasts available, the ECMWF has also released a report comparing the forecasts made by Pangu-Weather and the ECMWF IFS (a leading global NWP system) from April to July 2023.

According to the report, the uptake of machine learning (ML) methods like Pangu-Weather could be “a game-changer for the incremental and rather slow progress of traditional numerical weather prediction (NWP) methods” whose forecast skill has been increasing by about one day per decade (according to the World Meteorological Organization, or WMO). This can be attributed to the high computational cost of running a forecast with standard NWP systems. ML models are poised to revolutionize weather forecasting with forecasts that require much lower computational costs and are highly-competitive in terms of accuracy.

Dr. Tian Qi, Chief Scientist of HUAWEI CLOUD AI Field, an IEEE Fellow, and Academician of the International Eurasian Academy of Sciences, explained “Weather forecasting is one of the most important scenarios in the field of scientific computing because meteorological prediction is a very complex system, yet it is difficult to cover all aspects of mathematical and physical knowledge. At present, Pangu-Weather mainly completes the work of the forecast system, and its main ability is to predict the evolution of atmospheric states.”

Proven high accuracy in predicting extreme weather

Pangu-Weather model’s prediction capabilities have been tested in extreme situations such as Storm Eunice which hit north-western Europe in February 2022 and the first time the UK hit 40°C in the summer of 2022. These two examples show that data-driven models are capable of forecasting extreme weather situations and of providing guidance for medium-range forecasting.

ECMWF website showing weather forecasts made by Pangu-Weather (Source: ECMWF)

Pangu-Weather prediction covers geopotential, specific humidity, wind speed and temperature. All of this information is critical to predicting the development of weather systems, storm trajectories, air quality, and weather patterns. Pangu-Weather has also been used in predicting the trajectory of Typhoon Khanun, the sixth typhoon this year.

The ECMWF has long called for more efforts from the global weather forecasting community to use AI models as additional components of their forecasting systems and to further explore the strengths and weaknesses of such models to assist management of weather.

Dr. Tian Qi said, “Our ultimate goal is to build next-generation weather forecasting framework using AI technologies to strengthen the existing forecasting systems.”

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