Latest news with #weatherforecasting
Yahoo
2 days ago
- Climate
- Yahoo
From droughts to downpours: How AI is revolutionizing extreme weather forecasting
The Brief A new groundbreaking study uncovers ways to use A.I. to prepare for extreme weather. A.I. could even help predict wildfires. Issues remain about the ethical use of this technology, and trust in the data. MILWAUKEE - There is a lot of talk these days about artificial intelligence or A.I., and that is also true in the world of weather forecasting. A.I. is rapidly transforming how we model, detect, and respond to extreme weather and climate events. In a 2025 review published in Nature Communications, a team of researchers explored the full potential of A.I. and its ability to forecast floods, droughts, heatwaves, and even wildfires. The goal was to uncover ways to help communities prepare before disaster strikes. FREE DOWNLOAD: Get breaking news alerts in the FOX LOCAL Mobile app for iOS or Android What we know Machine learning and deep learning A.I. models were found to outperform some traditional forecasting techniques and seemed to excel at identifying and predicting extreme events. That accuracy went up when multiple data sources were integrated into the models, like satellite imagery, climate data, and ground sensors. The study went beyond basic forecasting and used A.I. to explore the "why," "what if," scenarios of different extreme weather events and even asked A.I. "how confident" it was in those predictions. Droughts: Hybrid models were able to help predict impacts on agriculture and forest health. Heatwaves: Specific models were able to give improved forecasts of regional temperature anomalies. Wildfires: Deep learning A.I. was able to provide early detection of dangerous fire events. Floods: A.I. was seen to enhance early warning systems and real-time alerts. The other side Not everything in the study was a net positive. The authors clearly stressed the need for transparent, ethical, and localized AI systems, particularly in high-stakes scenarios where false alarms can erode public trust or mislead disaster responses. In short, A.I. isn't ready to be handed the reins and will still need significant human oversight, but it could be an amazing tool for forecasters in the very near future. There are other significant hurdles as well, such as: Data Limitations: Extreme events, by their very nature, are rare, which makes it hard to train A.I. models. There's also a shortage of data that reflects the diverse geographic and socio-economic realities of our country and our planet. Integration Issues: AI models, in some cases, under-performed traditional methods. Real-world applications came up short due to high uncertainty, and incomplete data. SIGN UP TODAY: Get daily headlines, breaking news emails from FOX6 News What's next To unlock A.I.'s full potential, the study's authors suggest we develop better datasets that are tailored for each type of extreme weather event. They call for greater collaboration across A.I., climate science, and policy disciplines and again stress the need for ethical safeguards, especially for vulnerable communities. The Source Camps-Valls, G., Fernández-Torres, M.Á., Cohrs, K.-H., Höhl, A., Castelletti, A., Pacal, A., et al. (2025). Artificial intelligence for modeling and understanding extreme weather and climate events. Nature Communications, 16, 1919.
Bloomberg
6 days ago
- Business
- Bloomberg
How AI Could Change the Future of Weather Forecasts
Technology Explainer Weather forecasting helps industries avert billions in losses from extreme events, and AI could make predictions faster, cheaper and more accurate. Weather forecasting has gone through incremental but tremendous progress in past decades. By one metric, today's five-day forecast is now as accurate as a three-day forecast was in 2000. Entire ecosystems rely on weather forecasting, and any improvements — particularly as climate change heightens weather volatility — can help not just individuals to better manage risks, but also entire industries to avert billions in economic losses. In the US alone, an estimated one-third of the economy, or about $3 trillion, is sensitive to the weather and climate.
New York Times
23-05-2025
- Climate
- New York Times
Quote of the Day: A.I. May Make Weather Forecasting Faster and More Exac
'It doesn't know the laws of physics, so it could make up something completely crazy.' AMY MCGOVERN, a computer scientist and meteorologist at the University of Oklahoma, on A.I. weather forecasting models, and the limitation they still face.
The Independent
22-05-2025
- Science
- The Independent
AI is already beating traditional forecasters when it comes to predicting weather
A new AI model is outperforming the world's top forecasting systems for weather, pollution and cyclones, according to a new study, boosting hopes of weather forecasting becoming cheaper and more accurate. The model, called Aurora, accurately predicted cyclone paths and produced weather forecasts in a matter of seconds instead of hours. It was trained on a vast collection of atmospheric data, like weather observations, climate simulations and satellite measurements, by researchers at Microsoft and the University of Pennsylvania. When evaluated against global forecasting benchmarks, the AI system consistently produced faster forecasts than traditional models and, in many cases, offered greater accuracy, according to the new research published in Nature. Aurora was able to predict the path of Doksuri, the costliest Pacific typhoon of 2023, four days before landfall. While official weather agencies forecast landfall in Taiwan, Aurora correctly placed it in the northern Philippines. It also tracked the path and wind speeds of the storm Ciarán, which struck northwestern Europe last autumn, outperforming traditional models as well as newer systems based on AI like GraphCast and FourCastNet. According to the study, Aurora was the only model to correctly estimate peak winds from the storm. The results mark a major advance in modelling complex Earth systems with speed and accuracy. 'Earth's climate is perhaps the most complex system we study, with interactions spanning from quantum scales to planetary dynamics,' noted Dr Paris Perdikaris, associate professor at the University of Pennsylvania. 'With Aurora, we addressed a fundamental challenge in Earth system prediction: how to create forecasting tools that are both more accurate and dramatically more computationally efficient." The system is not limited to weather. Aurora has also been tested for forecasting air quality and ocean waves. In one case study, it accurately predicted a large sandstorm in Iraq, which closed airports and led to over 5,000 hospitalisations, a day before it occurred. The model managed to do this despite being trained without explicit knowledge of atmospheric chemistry. Aurora 'did not have any prior knowledge about atmospheric chemistry or how nitrogen dioxide, for instance, interacts with sunlight," said study co-first author Dr Megan Stanley of Microsoft Research, 'that wasn't part of the original training.' "And yet,' she said, 'in fine-tuning, Aurora was able to adapt to that because it had already learned enough about all of the other processes'. The model was also able to simulate complex ocean wave patterns generated by typhoons such as Nanmadol, which struck Japan in 2022. Aurora captured wave heights and direction with more detail and higher accuracy than the standard ocean forecasting systems in use today. 'When we compared Aurora to official forecasts from agencies like the National Hurricane Centre, China Meteorological Administration and others, Aurora outperformed all of them across different basins worldwide," said Dr Perdikaris. The model works by identifying patterns in large environmental datasets instead of solving physical equations. This allows it to generate 10-day weather forecasts and 5-day air quality predictions in under a minute, compared to the hours needed by traditional models running on supercomputers. Unlike traditional systems that need supercomputers, a key advantage of Aurora is that it can run on simpler machines. This could make accurate local forecasts possible even in countries with limited resources. 'The most transformative aspect is democratising access to high-quality forecasts,' Dr Perdikaris said. 'Traditional systems require supercomputers and specialised teams, putting them out of reach for many communities worldwide. Aurora can run on modest hardware while matching or exceeding traditional model performance.' The new AI model's foundation architecture allows it to be fine-tuned for various forecasting tasks, from local rain patterns to seasonal trends. "Knowledge gained from one area, such as atmospheric dynamics used in weather forecasting, enhances its predictive performance in other domains, including air quality modelling or predicting tropical cyclone formation," noted Dr Perdikaris. 'This cross-domain learning is central to the foundation model philosophy that guides my broader research programme.' Each new application requires only a small amount of additional data. According to Microsoft, some fine-tuning experiments took only a few weeks compared to the years typically needed to build numerical models. Although Aurora still needs existing data sources to generate forecasts, researchers say its speed and flexibility could make it useful for real-time applications in the future. Microsoft says the source code and model weights are publicly available and Aurora is already being used to improve weather services on its MSN platform. The researchers are interested in extending the model to generate predictions on a wider range of Earth system behaviours, including local and seasonal weather, extreme rainfall and urban flooding. "What excites me most about this technology is its broader applicability," Dr Perdikaris. "At Penn, we are exploring how similar foundation model approaches can address other prediction challenges beyond weather – from urban flooding to renewable energy forecasting to air quality management – making powerful predictive tools accessible to communities that need them most." Its developers believe that similar systems could eventually be adapted for other forecasting challenges, including floods, heatwaves and agriculture.
Washington Post
16-05-2025
- Climate
- Washington Post
Where local forecast offices no longer monitor weather around the clock
For at least half a century, the National Weather Service has been an around-the-clock operation. But after the U.S. DOGE Service led efforts to shrink the federal government, that is no longer possible in some parts of the country. In four of the agency's 122 weather forecasting offices around the country, there aren't enough meteorologists to staff an overnight shift, according to the National Weather Service Employees Organization, a union representing agency workers. And at least several more forecast offices are expected to stop staffing an overnight shift as early as Sunday.
