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The Hindu
13-06-2025
- Science
- The Hindu
Google DeepMind builds new AI model for predicting cyclones
Google DeepMind and Google Research have launched a new website called Weather Lab that shares their AI weather models today. The model is able to predict cyclone formations, their route, intensity, size and shape up to 15 days ahead, Google said in a blog. The experimental AI model can also generate 50 different scenarios for the storm. The research teams have also released a new paper with details of the core along with an archive on Weather Lab of historical cyclone track data, evaluation and backtesting. Google also added that during internal testing they found that the model's predictions were as accurate and often more accurate than current physics-based methods, they have also partnered with the U.S. National Hurricance Center (NHC) to evaluate further how effective the model is. The Weather Lab website also shows a comparison between how AI models perform and how the traditional models perform. Google has also released other AI weather prediction models like GenCast which surpassed one of the physics-based models 97.2 percent of the time, as per a study published in Nature.
The Verge
12-06-2025
- Science
- The Verge
Google has a new AI model and website for forecasting tropical storms
Google is using a new AI model to forecast tropical cyclones and working with the US National Hurricane Center (NHC) to test it out. Google DeepMind and Google Research launched a new website today called Weather Lab to share AI weather models that Google is developing. It says its new, experimental AI-based model for forecasting cyclones — also called typhoons or hurricanes when they reach a certain strength — can generate 50 different scenarios for a storm's possible track, size, and intensity up to 15 days in advance. The NHC is working with Google to evaluate the effectiveness of the model. The collaboration comes after the Trump administration and DOGE slashed the National Weather Service's staff and capacity for federal climate and weather research. Other companies and weather agencies are also exploring whether AI can improve forecasts, but technological advances so far don't eliminate the need for traditional weather models. Google released a research paper today, which has yet to be peer-reviewed, on how its tropical cyclone model works. It claims that its model's predictions are at least as accurate as those of traditional physics-based models. We'll have to see what the National Hurricane Center's rating of it is as the Atlantic hurricane season churns through November. For now, the aim is to strengthen NHC's forecasting in order to give people more accurate warnings and time to prepare for a storm. According to Google, its model's five-day predictions for cyclone tracks in the North Atlantic and East Pacific were 87 miles (140 km) closer, on average, to the storm's actual track than predictions from the European Center for Medium-Range Weather Forecasts (ECMWF) in 2023 and 2024. Weather Lab's interactive website lets people see how AI models compare to the ECMWF's physics-based models. But Google is emphasizing that its website is just a research tool for now — not something the public should rely on for forecasts. Google's cyclone model is trained on data from Europe's ERA5 archive, which includes hundreds of millions of observations collected by weather agencies around the world combined with predictions from a traditional weather model. The company also used ERA5 to train its previous AI weather prediction model GenCast. That model outperformed one of ECMWF's leading physics-based models 97.2 percent of the time, according to research published in the journal Nature in December 2024. Animation showing the Google model's prediction for Cyclone Alfred when it was a Category 3 cyclone in the Coral Sea. The model's ensemble mean prediction (bold blue line) correctly anticipated Cyclone Alfred's rapid weakening to tropical storm status and eventual landfall near Brisbane, Australia, seven days later, with a high probability of landfall somewhere along the Queensland coast. Credit: Google The company says it's also working with the Cooperative Institute for Research in the Atmosphere at Colorado State University and other researchers in the UK and Japan to improve its AI weather models. The importance of real-world observations and older weather models in developing these new kinds of tools is one reason why AI is so far only poised to assist traditional weather forecasting instead of replacing it. Adjusting to a changing climate will also hinge on the ability to collect and analyze new data on increasingly extreme and erratic weather events. How well the US can keep up with this kind of research, however, is a growing concern under the Trump administration. DOGE's rampage through the federal government has taken its toll at the federal agency that houses the NHC and the National Weather Service, the National Oceanic and Atmospheric Administration (NOAA). The National Weather Service reduced the number of weather balloon launches after staffing cuts, and NOAA is increasingly relying on weather balloon data from private companies. Project 2025 called for dismantling NOAA — which leads climate research on top of providing weather forecasts — and privatizing much of its services. Some advocates are raising alarm over the prospect of turning weather forecasts into a paid product instead of a free service available to anyone and everyone. 'For a long time, weather has been viewed as a public good, and I think, you know, most of us agree with that … Hopefully we can contribute to that, and that's why we're trying to kind of partner with the public sector,' Peter Battaglia, a research scientist at Google DeepMind, said in a press call when The Verge asked about concerns surrounding privatizing weather services. Tellingly, Google's announcement today doesn't mention the climate crisis like the company has in previous launches for this kind of program. 'As climate change drives more extreme weather events, accurate and trustworthy forecasts are more essential than ever,' it said in a December 4 announcement for GenCast.
Newsweek
30-05-2025
- Climate
- Newsweek
How AI Can Improve Storm Forecasting as Hurricane Season Arrives
Based on facts, either observed and verified firsthand by the reporter, or reported and verified from knowledgeable sources. Newsweek AI is in beta. Translations may contain inaccuracies—please refer to the original content. Weather models based on artificial intelligence (AI) are better than traditional forecasts at tracking tropical storms, new research has found, part of a wave of AI breakthroughs that could improve warnings for extreme weather such as hurricanes. "To our surprise, we saw that for the first time an AI system could outperform all existing operational forecasts for all those hurricane events," Paris Perdikaris, an associate professor in the School of Engineering and Applied Science at the University of Pennsylvania, told Newsweek. Perdikaris spent a year with Microsoft Research working on a large-scale AI model called Aurora that was trained on more than one million hours of data from various Earth systems. On May 21, Perdikaris and collaborators at Microsoft Research published results in the journal Nature. Aurora did better than traditional forecasts in a range of predictions, including a 20 to 25 percent improvement in tracking tropical storms over two to five days. "We see a uniform improvement across the board in terms of the accuracy," Perdikaris said. Weather forecasting systems using AI can now perform better than traditional forecasts when tracking the path of tropical storms. But researchers warn that AI cannot replace the need for physics-based systems and good data collection. Weather forecasting systems using AI can now perform better than traditional forecasts when tracking the path of tropical storms. But researchers warn that AI cannot replace the need for physics-based systems and good data collection. Photo-illustration by Newsweek/Getty It's the latest in a string of promising reports on AI forecasting for extreme weather. In December, researchers at the AI lab Google DeepMind also published in Nature results from its machine learning forecast system, called GenCast. The researchers wrote that GenCast "better predicts extreme weather, tropical cyclone tracks and wind power production." In February, the European Centre for Medium-Range Weather Forecasts (ECMWF) put its AI Forecasting System (AIFS) into operation and reported that it outperforms state-of-the-art physics-based models for many measures, including tropical cyclone tracks. "The AIFS typically does a more accurate job of moving large-scale weather systems around the globe," Matthew Chantry, strategic lead for machine learning at ECMWF, told Newsweek via email. "Storms, are typically more accurately positioned." AI is not a panacea for tropical storm forecasting, scientists said, and recent research has exposed some weaknesses in AI forecasting. One study found that while AI systems do well at tracking a hurricane's path, they tend to underestimate wind speed and storm strength. But with climate change supercharging storms, AI promises to be a valuable addition to our warning system, potentially helping to save lives and prevent property damage. Cheaper, Faster Weather Modeling The Weather Company, producers of The Weather Channel, Weather Underground and Storm Radar, have been developing AI and machine learning for forecasting tools for years, according to Peter Neilley, senior vice president of weather forecasting services and operations. "It's just gotten more sophisticated and that enabled us to create these data-driven models," Neilley told Newsweek. "So that's all culminated in this sort of revolution for weather." Neilley explained that, unlike traditional weather models in which supercomputers work through complicated physics formulas, AI systems operate by learning from patterns from historical weather data. Building the AI model takes a lot of work and computing power, he said, but "they're very cheap to run once you've built the model." That, Neilley said, is AI's main benefit. Where traditional physics-based models can take hours, an AI model could take less than a minute. "What that enables you to do is actually run them many, many times and each time, you're running a slightly different model," he said. "With that much better prediction of how it may play out, I can use that to help people and businesses make better decisions." Brad Reinhart, senior hurricane specialist at the National Hurricane Center, works on tracking Hurricane Beryl, the first hurricane of the 2024 season, at the National Hurricane Center on July 01, 2024 in Miami, Florida. Brad Reinhart, senior hurricane specialist at the National Hurricane Center, works on tracking Hurricane Beryl, the first hurricane of the 2024 season, at the National Hurricane Center on July 01, 2024 in Miami, Weather Company President Sheri Bachstein will be among the speakers at Newsweek's AI Impact Summit June 23 to 25 in Sonoma, California, to talk about how the company is investing in AI. In the past year, The Weather Company has partnered with NVIDIA to produce more granular forecasts using AI and to improve weather simulations. Another collaboration with government scientists aims for better integration of vast weather data to get a clear snapshot of the state of the atmosphere, the critical starting point for forecasting. AI Cannot Replace Need for Basic Data Neilley said the AI approach can also yield a different type of forecast, one with a probabilistic range of outcomes to consider. While that rich outlook offers many benefits in some extreme weather conditions, such as an approaching hurricane, it could lead to information overload. "Just giving decision makers more complicated information is probably making their job harder," he said. "What is needed is an AI-based decision modeling system on top of the weather model." The AI company Urbint aims to provide that sort of informed weather preparation for electric utility companies. In April, Urbint acquired StormImpact, an AI company that predicts the risk of storms, wildfires and floods for utility infrastructure. "They don't just need to know that a storm is coming—they need to know which circuits will go down, how many customers will be impacted, and what resources they'll need on the ground to restore power quickly and safely," Urbint CEO Corey Capasso told Newsweek via email. The system predicts what transformers, substations and overhead lines are most vulnerable. "That means utilities can anticipate not just if, but where and how the grid will be impacted, and start planning for the exact number and type of crews needed," he said. StormImpact's technology is already being used by major utility companies, including Southern Company, American Electric Power and FirstEnergy. Weather-related disruptions cost utility companies an average of $70 billion annually, Urbint said. A report released earlier this month by the Electric Power Research Institute shows extreme weather events causing at least $1 billion in damage are becoming more frequent. From 2019 to 2023, billion-dollar disasters happened about 20 times a year. Climate scientists warn that our warmer atmosphere is contributing to many extreme weather events. Warmer air holds more moisture leading to more intense rainfall and flooding, and higher sea surface temperatures fuel tropical storms. As we head into this Atlantic hurricane season on the heels of the two hottest years on record, several forecasts predict a busier than average season. Several veteran storm forecasters have voiced concerns about the Trump administration's deep cuts to the National Weather Service and its parent agency, the National Oceanic and Atmospheric Administration (NOAA), warning that some key weather bureaus are understaffed and basic data gathering has been compromised. It may be tempting to look to the advances in AI to fill gaps left by those cutbacks. But for all the potential benefits AI holds for weather forecasting, researchers caution that it is not a replacement for existing systems of gathering and analyzing weather data. "We still need the raw data," the University of Pennsylvania's Paris Perdikaris said. "We still need high quality data coming from physics-based simulation models that have been in place all these years."
Axios
05-03-2025
- Business
- Axios
Exclusive: Google Cloud unveils AI-powered weather predictions
Google's Cloud division is taking a major step toward making operational recent gains in AI weather forecast models and marketing them for the energy industry, the company tells Axios exclusively. Why it matters: This is a prominent example of a tech company that invested in developing AI models to make the transition from research to applications. AI weather models are in their infancy but have demonstrated remarkable accuracy. Those advances have come as certain extreme weather events are becoming more intense and frequent due to human-caused global warming. Driving the news: Google Cloud is marketing two AI forecast models to its enterprise cloud customers. Both were developed by Google DeepMind, and used historical weather data to make predictions about future conditions out to 10 to 15 days in advance. One model, previously known as GenCast, bested some of the world's most accurate modeling systems. It generates probabilistic projections to allow companies to plan for high impact, low probability scenarios as well as the most likely forecast outcomes. The big picture: The tech industry has largely led the charge on AI modeling given its expertise working with large datasets and access to significant computer resources. Google, Microsoft and Nvidia have each pursued the development of AI weather models despite none of them being a strictly weather and climate company. However, Google is now out in front when it comes to bringing its models to market. The intrigue: Google Cloud is bringing two models, branded as "WeatherNext," to its Cloud enterprise customers to try to help them plan for extreme weather. The energy industry is a key customer given companies' needs to plan for changing weather conditions, Pete Battaglia, director of research for sustainability at Google DeepMind, told Axios in an interview. Energy companies, Google hopes, will find the new tools useful for everything from planning for supply and demand swings to anticipating the need to tap into battery storage resources. Google also hopes it can lead them to make decisions on where to build renewable energy infrastructure. Google's Cloud division also sees future demand for its new weather models coming from the logistics and retail sectors, as companies seek to optimize shipping routes and stores try to stock their shelves with weather-appropriate gear. Zoom out: Google made its announcement in the run-up to the annual CERAWeek energy conference in Houston, which features top oil and gas CEOs and representatives of the renewables sector as well as utilities. The announcement also comes as NOAA, the nation's top weather and climate agency goes through rounds of cuts and an uncertain future. Most private sector weather providers obtain original weather data for free from NOAA and other global centers, then use it to feed into their proprietary weather models. AI weather models work differently, since they are trained on historical weather data and don't involve computationally-intensive physics equations, enabling them to be run far faster and cheaper than traditional models. NOAA's approach to AI weather modeling is still developing, and Battaglia said he is open to collaboration opportunities between the agency and GoogleDeepMind. The bottom line: AI weather models are going mainstream, tailored to specific use cases. For now, they are supplementing, rather than replacing, traditional physics-based models.
