Latest news with #Pangu-Weather
The Star
6 days ago
- Climate
- The Star
Huawei's AI weather model among top performers in China tests
Meteorological agencies worldwide are racing to develop and roll out new AI tools: the US National Hurricane Center teamed up with Google this month to improve tropical cyclone forecasts, while the premier European forecasting body became the first major prediction center to release a model this year. — Pixabay China's weather agency is testing more than a dozen artificial intelligence models in an effort to enhance its forecasting, with a system from Huawei Technologies Co showing accelerated improvement. The best models from the trial will be prioritized for deployment by provincial bureaus, and granted priority access to official weather data, according to the China Meteorological Administration, which is running the program. The CMA has said it wants to ensure "orderly and standardized development' as the technology rapidly develops at home and abroad. In recent weeks, Huawei's Pangu-Weather has improved relative to others in the program. It was the second-best last month and over the first half of June, according to CMA data, compared with its seventh-place ranking in January. In particular, the model was better able to predict the intensity and geographic scope of several heavy rainfall events across southern China in April. Huawei didn't respond to a request for comment. Meteorological agencies worldwide are racing to develop and roll out new AI tools: the US National Hurricane Center teamed up with Google this month to improve tropical cyclone forecasts, while the premier European forecasting body became the first major prediction center to release a model this year. While AI models have shown promise, outperforming traditional physics-based methods on a number of measures, they do have some limitations. Many are trained on historical data, which could make them prone to underestimating extremes as the Earth's warming climate enters uncharted territory. China stands out for the number of models it's publicly testing. The trial scores them based on how well they predict key metrics such as temperature, wind speed, and humidity. Performance scores are updated daily, and detailed monthly evaluation reports are posted online. Of the 14 models being evaluated, other top performers include Fuxi from Fudan University, and Puyun from Hangzhou-based startup Meta Carbon Co. Another is Sphere Fusion Forecast, jointly developed by China's state weather agency, Tsinghua University, and US chipmaker Nvidia Corp. China's weather industry is closely regulated and regarded as a sector with important national security considerations, and only government meteorological agencies are allowed to issue public forecasts. New measures took effect this month regulating the use of AI, with the state weather bureau and the powerful Cyberspace Administration of China spearheading the initiative. – Bloomberg
The Hindu
22-05-2025
- Science
- The Hindu
Microsoft AI weather forecast faster, cheaper, truer: study
Microsoft has developed an artificial intelligence (AI) model that beats current forecasting methods in tracking air quality, weather patterns, and climate-addled tropical storms, according to findings published Wednesday. Dubbed Aurora, the new system, which has not been commercialised, generated 10-day weather forecasts and predicted hurricane trajectories more accurately and faster than traditional forecasting, and at lower costs, researchers reported in the journal Nature. "For the first time, an AI system can outperform all operational centres for hurricane forecasting," said senior author Paris Perdikaris, an associate professor of mechanical engineering at the University of Pennsylvania. Trained only on historical data, Aurora was able to correctly forecast all hurricanes in 2023 more accurately than operational forecasting centres, such as the U.S. National Hurricane Center. Traditional weather predicting models are designed on first physical principles, such as conservation of mass, momentum and energy, and require massive computer power. The computational costs of Aurora were several hundred times lower, the study said. The experimental results follow on the heels of the Pangu-Weather AI model developed and unveiled by Chinese tech giant Huawei in 2023, and could herald a paradigm shift in how the world's major weather agencies forecast weather and potentially deadly extreme events exacerbated by global warming. "I believe that we're at the beginning of a transformation age in air system science," Perdikaris said in a video presentation distributed by Nature. "In the next five to 10 years the holy grail is how to build systems that can directly work with observations from remote sensing sources like satellites and weather stations to generate forecasts at high resolution anywhere we like." According to its designers, Aurora is the first AI model to consistently outperform seven forecasting centres in predicting the five-day trajectory of devastating cyclones. In its simulation, for example, Aurora correctly forecast four days in advance where and when Doksuri, the most costly typhoon ever recorded in the Pacific, would hit the Philippines. Official forecasts at the time, in 2023, had it heading north of Taiwan. Microsoft's AI model also outperformed the European Centre for Medium-Range Weather Forecasts (ECMWF) model in 92 percent of cases for 10-day global forecasts, on a scale of approximately 10 square kilometres (3.86 square miles). The ECMWF, which provides forecasts for 35 European countries, is considered the global benchmark for weather accuracy. In December, Google announced that its GenCast model had surpassed the European centre's accuracy in more than 97 percent of the 1,320 climate disasters recorded in 2019. These promising performances, all experimental and based on observed events, are being closely scrutinised by weather agencies. Several, including Meteo-France, are developing their own AI learning models alongside traditional digital models. "This is something we have taken very seriously," Florence Rabier, Director General of the ECMWF, told AFP. Their first "learning model", made available to member states in February, is "about 1,000 times less expensive in terms of computing time than the traditional physical model", she added. While operating as a lower resolution (30 sq km) than Aurora, the ECMWF model is already operational.
The Star
22-05-2025
- Science
- The Star
Study: Microsoft AI weather forecast faster, cheaper, truer
Trained only on historical data, Aurora was able to correctly forecast all hurricanes in 2023 more accurately than operational forecasting centres, such as the US National Hurricane Center. — Reuters PARIS: Microsoft has developed an artificial intelligence (AI) model that beats current forecasting methods in tracking air quality, weather patterns, and climate-addled tropical storms, according to findings published on May 21. Dubbed Aurora, the new system – which has not been commercialised – generated 10-day weather forecasts and predicted hurricane trajectories more accurately and faster than traditional forecasting, and at lower costs, researchers reported in the journal Nature. "For the first time, an AI system can outperform all operational centres for hurricane forecasting," said senior author Paris Perdikaris, an associate professor of mechanical engineering at the University of Pennsylvania. Trained only on historical data, Aurora was able to correctly forecast all hurricanes in 2023 more accurately than operational forecasting centres, such as the US National Hurricane Center. Traditional weather predicting models are designed on first physical principles – such as conservation of mass, momentum and energy – and require massive computer power. The computational costs of Aurora were several hundred times lower, the study said. The experimental results follow on the heels of the Pangu-Weather AI model developed and unveiled by Chinese tech giant Huawei in 2023, and could herald a paradigm shift in how the world's major weather agencies forecast weather and potentially deadly extreme events exacerbated by global warming. 'Holy grail' "I believe that we're at the beginning of a transformation age in air system science," Perdikaris said in a video presentation distributed by Nature. "In the next five to 10 years the holy grail is how to build systems that can directly work with observations from remote sensing sources like satellites and weather stations to generate forecasts at high resolution anywhere we like." According to its designers, Aurora is the first AI model to consistently outperform seven forecasting centres in predicting the five-day trajectory of devastating cyclones. In its simulation, for example, Aurora correctly forecast four days in advance where and when Doksuri – the most costly typhoon ever recorded in the Pacific – would hit the Philippines. Official forecasts at the time, in 2023, had it heading north of Taiwan. Microsoft's AI model also outperformed the European Centre for Medium-Range Weather Forecasts (ECMWF) model in 92% of cases for 10-day global forecasts, on a scale of approximately 10 square kilometres (3.86 square miles). The ECMWF, which provides forecasts for 35 European countries, is considered the global benchmark for weather accuracy. In December, Google announced that its GenCast model had surpassed the European centre's accuracy in more than 97% of the 1,320 climate disasters recorded in 2019. These promising performances – all experimental and based on observed events – are being closely scrutinised by weather agencies. Several, including Meteo-France, are developing their own AI learning models alongside traditional digital models. "This is something we have taken very seriously," Florence Rabier, Director General of the ECMWF, told AFP. Their first "learning model", made available to member states in February, is "about 1,000 times less expensive in terms of computing time than the traditional physical model", she added. While operating as a lower resolution (30 sq km) than Aurora, the ECMWF model is already operational. – AFP
