Since its release in August last year, Pangu has revolutionised weather forecasting, offering quicker and more accurate predictions than traditional meteorological methods.
Pangu-Weather first burst onto the scene in July 2023, when a paper detailing the AI model was published in the journal, Nature. A month later, it was launched on the European Centre for Medium-Range Weather Forecasts (ECMWF) website.
The AI model hit a major milestone when it was able to complete a seven-day weather forecast in just 10 seconds – more than 10,000 times faster than traditional methods.
Then on February 29, just months after its launch, Pangu-Weather was ranked first among China’s top 10 scientific advances in 2023 by the National Natural Science Foundation of China (NSFC).
Chinese tech giant Huawei Technologies is leading the meteorological revolution with its fast and accurate weather forecasting AI models. Photo: Reuters
“In its recognition by the NSFC, Pangu had two major accomplishments: first, it improved the world’s leading ECMWF weather forecasting system by about 0.6 days. This means it can predict extreme weather earlier and more accurately,” Science and Technology Daily reported. “The second is 7-day predictions in 10 seconds, 10,000 times faster than numerical ones.”
According to a Huawei report in late February, Pangu delivered more accurate forecasts for crucial weather elements, such as temperature, pressure, humidity and wind speed, than numerical simulations. Plus its error margin for predicting the paths of tropical cyclones was 25 per cent lower than the ECMWF.
It is quite an achievement for the AI model, which has so quickly changed the face of global weather forecasting. By leveraging AI to predict weather patterns, scientists can bypass the complexities associated with traditional methods of forecasting. No mathematical physics knowledge or expert experience are needed for AI, something which has created a new avenue for weather prediction.
Now, researchers have used Pangu as a foundation to develop the new regional model, Zhiji.
Created in collaboration with the Shenzhen Meteorological Bureau, Zhiji has been trained with high-resolution data from southern China.
According to the Huawei team, Zhiji can provide a five-day forecast with a precision of 3km for Shenzhen and its surrounding areas. While the Central Meteorological Bureau already provides hourly forecasts with street-level precision, these are generally only available for the following 24 hours.
“Zhiji is capable of forecasting core meteorological elements such as wind speed, temperature, humidity and precipitation. Since its trial operation began in February, it has provided valuable insights to the Shenzhen Meteorological Bureau on multiple occasions,” Huawei reported in late March.
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Currently, AI and manual predictions each have their strengths and weaknesses.
AI has the edge in predicting the paths of typhoons; whereas numerical simulations are more accurate in determining wind strength values.
“Scientists can now integrate results from numerical simulations with forecasts provided by Zhiji to make the most advantageous judgments,” a Huawei spokesman said. “This could be a trend in the future.”
According to researchers, this year’s flood season will be the true test for Zhiji 1.0. They expect to see the model further optimised with improvements made to algorithms as a result.
Ongoing work on the technology aims to enhance its rainfall forecasting capabilities, including providing specialised forecasts like heatstroke indexes and comfort levels, and improving the resolution of heavy rainfall forecasts to 1km.
“For example, in typhoon conditions, precise meteorological models can predict street-level precipitation, offering early warnings for urban drainage systems,” Huawei said.
Similar to Zhiji, if regional data from other areas are available for training, scientists could potentially develop local models tailored to those regions, serving more cities.
In December last year, the team announced a collaboration with the Thai Meteorological Department, with related products currently under development.
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