Latest news with #weatherforecasting
RNZ News
18 hours ago
- Climate
- RNZ News
Auckland's rain radar 'vulnerable' at end of life
Auckland's rain radar has reached its end of life and is vulnerable to significant outages in the event of a component failure. The coverage of country's largest city and biggest international airport relies on outdated technology, which limits forecasting and warning capabilities in the region. This was the advice given to Associate Minister of Transport James Meager - who has oversight of the Meteorological Service of New Zealand Limited (MetService) contract for forecasting. The Auckland rain radar, commissioned in 1989, is the oldest in the network and while periodic upgrades have allowed it to extend its useful life well beyond the typical 20-year horizon, it is now in need of urgent replacement. To discuss are MetService's General Manager of Observing Systems Kevin Alder and MetService's Chief Meteorologist Chris Noble. The remote rain radar is located on Mount Tamahunga near Warkworth and is only accessible via helicopter. Photo: Supplied by MetService
Coin Geek
4 days ago
- Climate
- Coin Geek
AI records advanced proficiency in forecasting storms
Getting your Trinity Audio player ready... Two research teams have recorded impressive levels of success with artificial intelligence (AI)-based weather forecasting systems. According to a report, the first research team from Microsoft (NASDAQ: MSFT) leveraged the company's Aurora AI model to create an advanced weather forecasting system. The research, published in the Nature Journal, disclosed that the Aurora AI-based model outperformed traditional computerized weather forecasts. The Microsoft researchers noted that the model can accurately predict a range of weather events, including narrowing down forecasts to specific ocean wave patterns and air quality. Furthermore, the researchers state that the model exceeded expectations in forecasting tropical cyclones with significantly lower computational costs. The team achieved the feat by training the AI weather prediction model on over 1 million hours of 'diverse geophysical data,' providing the system with a deep pool to accurately make forecasts. A U.S.-based team has also recorded significant strides in AI-based weather forecasts, leaning on Google DeepMind's (NASDAQ: GOOGL) Graphcast tool. The National Oceanic and Atmospheric Administration (NOAA) researchers revealed that the new model is 10X faster than traditional systems in storm predictions. The model, trained on data from NOAA's Warn-on-Forecast-System (WOFS), reduces the forecast time for storms from nearly five minutes to seconds. Apart from spotting incoming storms, the Google-based model can predict the storm's pattern and movement for up to two hours with remarkable accuracy. 'The model yielded largely accurate predictions of how storms would evolve for up to two hours,' read the report. 'These predictions matched 70% to 80% of those generated by the Warn-on-Forecast system.' Big Tech is hurtling toward AI-based weather forecasting tools, with Google leading the charge. Microsoft and IBM (NASDAQ: IBM) have also unveiled their AI-based weather prediction models with varying degrees of success. Countries are turning to AI models to stay ahead of the curve Nations at risk of climate and weather disasters embrace AI models to predict incoming events and take proactive measures to mitigate damage. India has integrated AI to track flood patterns, while Chinese researchers are identifying the upsides to AI-based weather forecasting for the country. An Australian charity is leaning on AI to protect the Daintree rainforest from ecological challenges. However, an integration with blockchain and Internet of Things (IoT) technology is tipped to improve the accuracy of AI weather prediction models. DLT can tackle food fraud, but success remains a challenge The scourge of food fraud is on the rise, stealing nearly $50 billion annually from the global food industry while posing significant health risks. However, a report notes that blockchain offers a veritable solution to stifle the activities of bad actors in the food industry. According to a report, while food fraud siphons only a small slice of the food industry's $12 trillion valuation, the figure is equivalent to the GDP of Malta. Cases of food mislabeling and dilution are at an all-time high, with horse meat sold as beef and olive oil diluted with cheaper vegetable oils. The report notes that the absence of transparency in global supply chains is fueling the rise of food fraud. With industry processes built in silos and 'information islands,' participants in the supply chain do not have a bird's eye view of the processes. 'Many companies maintain their own internal tracking systems, but these often lack interoperability with their suppliers or customers,' said Naoris Protocol CEO David Carvalho. To increase transparency and reduce the footprint of bad actors in the space, industry experts are making a case for blockchain. There is a consensus that the transparency and immutability features of publicly distributed ledgers will hold food suppliers to a higher standard. Furthermore, the industry players highlight the perks of 'selective transparency,' which allows supply chain participants to share only relevant data while protecting sensitive commercial data from authorized participants. Experts are turning to the utility of smart contracts and automation functionalities as reasons for blockchain-based supply chains to fight food fraud. Early use cases have yielded a streak of positives with South Korea's KT using the technology to fight food fraud, laying the foundation for new entrants. Vietnamese companies are turning to blockchain to verify halal certifications, preventing unscrupulous suppliers from passing off non-halal food to unsuspecting consumers. Malaysia is also mulling the prospects of on-chain halal certifications, citing a wave of positives for the food industry. Not a walk in the park Incorporating blockchain in food supply chains is not easy, with the report noting steep integration costs and manpower training. Apart from high implementation costs, there is the additional 'garbage in, garbage out' challenge associated with a Web3-based system. The report recommends the integration of oracles and IoT technologies to feed external data into distributed ledgers. Other challenges include privacy and data concerns, as well as the absence of standardized protocols for blockchain adoption across several jurisdictions. In order for artificial intelligence (AI) to work right within the law and thrive in the face of growing challenges, it needs to integrate an enterprise blockchain system that ensures data input quality and ownership—allowing it to keep data safe while also guaranteeing the immutability of data. Check out CoinGeek's coverage on this emerging tech to learn more why Enterprise blockchain will be the backbone of AI. Watch: Artificial intelligence needs blockchain title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen="">
Bloomberg
6 days ago
- Climate
- Bloomberg
Huawei's AI Weather Model Among Top Performers in China Tests
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.
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WIRED
11-06-2025
- Science
- WIRED
Orbital Intelligence: When Satellites Meet Machine Learning
How BCG X, research institutions, and space agencies are using generative AI to supercharge weather forecasting with the GAIA Foundation Model. The 20,000-Foot (or Mile?) View Here's a fact that almost everyone on the planet is becoming increasingly familiar with: As the Earth's climate warms and its weather systems become less reliable, so do the weather prediction capabilities underpinning the business practices of countless agriculture, insurance, public safety, and scientific research organizations around the world. Here's a less obvious fact: As those prediction capabilities deteriorate, so do many of the public and private services we take for granted every day. 'Having better, more reliable, more detailed intelligence about what's going on in the weather system has a lot to do with who's going to win and lose in financial industries like insurance and lending, in infrastructure sectors like energy, and places like state and local government,' says David Potere, geospatial tech leader and BCG X managing director and partner. 'As an example, the way we characterize risk affects the homes we buy, the businesses we invest in, the cities that grow or don't. And right now, there is a known gap in the insurance industry being able to cover a rapidly changing game board.' The impact that this weather intelligence gap—and countless other gaps like it—has on organizational margins can trickle down to consumers in harsh ways. New volatility in the climate system can manifest as extended droughts and high winds that fuel record-breaking wildfires, or back-to-back 100-year storms that cause property damage at massive scales. A societal inability to forecast those kinds of events drives up our insurance rates, undercuts public safety measures, and strains governmental relief efforts. The world needed a novel solution to a rapidly growing problem. The BCG X AI Science Institute may have found it alongside a growing new class of gen AI-powered weather models. Turning to Eyes in the Sky Enter GAIA (Geospatial Artificial Intelligence for Atmospheres) Foundation Model, an open source foundation model built in partnership between the BCG X AI Science Institute and several of the world's leading aerospace organizations to help researchers all across the world better understand and anticipate weather's next move. Similar to large language models (LLMs) trained on text, the GAIA Foundation Model is a gen AI vision model trained on 25 years of satellite imagery that allows researchers to study climate and weather patterns at a greater speed and accessibility than ever before. Specifically, GAIA works with images from a constellation of school bus-sized satellites that 'stare' at the planet from a stationary position more than 22,000 miles above the surface, capturing high- resolution images of the entire 'disk' of the Earth every 30 minutes. This provides a continuous, real-time stream of images and atmospheric data. Taken in concert with a global array of thousands of hyper-detailed weather ground stations, meteorologists can essentially visually map weather developments in near real time across the entire globe. 'There are naturally gaps in this record, including a 'soft spot' when it comes to tracking weather in polar regions,' says Potere.. 'What we're talking about is investments on the ground through generative AI capabilities like GAIA that have the potential to unlock a synthetic fourth satellite constellation.' That kind of visualization capability, produced via open source gen AI technology, is groundbreaking on its own. But the setup behind that tooling is equally innovative. Consider compute power: Depending on the bands and mosaicking process, global satellite imagery can clock in at 3298 x 9896 pixels (and more), and a 15-year span of data measured every 30 minutes yields 263,000 images—more than the total frames in a typical Hollywood feature length film. That's 17 TB of data to be crunched per training session for the GAIA model. The team is also working with live weather data, tapping into the same operational satellites that weather forecasters use on the news at night. These foundation model approaches require a lot of GPUs—a common reason why visual-based gen AI tools have traditionally been a lesser-explored space. 'Up until now, the sheer compute and the algorithms and the know-how you need to be able to translate pixels into answers has been very rare,' Potere says. Tackling the Compute Problem BCG X made two conscious decisions when scoping the endeavor that not only proved to be novel but allowed them to bring the project online in just one year rather than the 18 to 24 months typical for other projects. The first was to commit to creating an environment that could be deployed in the cloud, rather than being tethered to a purpose-built supercomputer. According to Tom Berg, BCG X lead engineer for the project, 'There was something really daunting here; it's almost become an accepted truth that to roll up your sleeves and build your own foundational model is too expensive, if you look at the immense resources the hyperscalers are using. One of the things we wanted to show is that if it works, you don't have to have a dedicated supercomputer to do these kinds of builds.' To that end, the GAIA team turned to a national network of university computing resources distributed across the United States. This constellation of off-the-shelf GPUs (ranging from state-of-the-art to 10-year-old GPUs) is precisely what BCG X's development team had in mind. 'That profile, rather than matching a supercomputer, gave us a lot of parameters to work with,' Berg says. 'It's a very, very adaptable system, and at one point we were using 15 percent of the NRP's entire cloud.' Still, such a setup provided some interesting challenges. Where a dedicated supercomputer has all of its processing power in one building with one uniform power configuration, Berg and Potere's team would instead be connecting GPUs on opposite sides of the Earth. There were also acute issues like power outages, or a university unexpectedly cycling their data centers. Crucially, GAIA was sharing compute space with hundreds of other research applications running at the same time. 'You're basically on a busy public road rather than a dedicated racetrack,' Berg says. The team's second operational decision was to initially narrow their focus to precipitation and top-of-cloud temperature data—as opposed to modeling all aspects of every layer of the atmosphere. Because that selected data closely corresponds to a range of weather phenomena, it provided researchers with the flexibility needed to prove out the foundation model and run experiments at a still-manageable level of initial effort. Critically, by focusing on this 'lower complexity' problem statement, the GAIA team was able to immediately scale their modeling to global atmospheric conditions, putting a dent in the problem of weather predictability. That surgically targeted start allowed the team to reach an equally targeted—yet incredibly meaningful—outcome. Building a Global Toolset and Modeling Solutions A key reason why the team was able to build so rapidly: open source tools and resources, oftentimes combined with earlier research from equally pioneering research teams. 'We have the benefit of standing on the shoulders of some of the earliest groups working on this,' Potere says. 'Even now, the literature has gotten three times denser since we started, but there was something of a literature, so we certainly benefited from the second mover advantage.' That open source, iterative mindset will now define the project's next phase, as well: To give back to the research community and contribute to its ever-evolving toolset, BCG X and their collaborators released the GAIA Foundation Model to the global open source community. In other words, they modeled the Earth, for the Earth. And their work couldn't have come at a better time. As governments, businesses, and research institutions increasingly grapple with the new normal of disruptive weather volatility, gen AI weather and environmental models like GAIA can fuel faster and better decision making—something experts and organizations across the world need more every day. Climate change may very well be the defining issue of our time—and GAIA may very well be part of how the world as a whole is able to meet it. Learn more about Boston Consulting Group here.
The Guardian
05-06-2025
- Climate
- The Guardian
‘Flying blind': leading Florida weatherman warns Trump funding cuts will degrade forecasts
A leading TV weatherman in Florida has warned viewers on air that he may not be able to properly inform them of incoming hurricanes because of cuts by the Trump administration to federal weather forecasting. John Morales, a veteran meteorologist at NBC 6 South Florida, told viewers on Monday night that Donald Trump's cuts to climate and weather agencies mean that forecasters will be 'flying blind' into what is expected to be an active hurricane season. Recalling Hurricane Dorian, which devastated the Bahamas in 2019 and appeared to be heading straight for Florida, Morales said he was confidently able to assure worried viewers it would turn away from the state. 'I am here to tell you I'm not sure I can do that this year,' he said. 'Because of the cuts, the gutting, the sledgehammer attack on science in general.' Morales said that the attacks by the Trump administration on science will have a 'multigenerational impact on science in this country' and will specifically hamper his job due to the slashing of hundreds of jobs at the National Weather Service and the National Oceanic and Atmospheric Administration (Noaa). 'Did you know central and south Florida National Weather Service offices are currently 20% to 40% understaffed, from Tampa to Key West?' Morales said, referencing the widespread staff shortages in weather service offices along the hurricane-prone Gulf of Mexico coast and Puerto Rico. 'This type of staffing shortage is having impacts across the nation because there has been a 20% reduction in weather balloon releases, launches. What we are starting to see is the quality of the forecast is becoming degraded.' TV forecasters such as Morales, as well as private weather forecasting services and apps, rely upon federal scientists for data gleaned from sources such as satellites, weather balloon launches and aircraft surveys. Morales warned viewers that Noaa 'hurricane hunter' aircraft may not be able to fly this year and 'with less reconnaissance we may be flying blind and we may not exactly know how strong a hurricane is before it reaches the coastline'. On Thursday, Morales told the Guardian that he stood by his statements and that the 'message was clear' to viewers. Asked if he was worried about retaliation from an administration that has sought to defund and disparage scientists, Morales said: 'No, not at all. Science is science.' Noaa has predicted that the US's hurricane season, which officially started on Sunday, will be more active than usual, with as many as five major hurricanes with winds of 111mph (179km/h) or more. This has heightened concerns over the consequences of funding cuts by Trump as part of the president's attempts to shrink the federal workforce. Sign up to This Week in Trumpland A deep dive into the policies, controversies and oddities surrounding the Trump administration after newsletter promotion After losing 600 staff to layoffs and early retirements, causing it to admit to 'degraded operations' with fewer staff to handle forecasts, the National Weather Service was this week given special permission to hire 100 forecasters, radar technicians and others despite a government-wide hiring freeze. The Trump administration has insisted the American public will be properly informed of hurricane risks despite the cuts. But experts have said that much more will need to be done to ensure the weather service isn't overstretched and for the US to become better prepared for extreme weather impacts that are escalating due to global heating. Trump has regularly dismissed the established science of climate change, calling it a 'giant hoax' and 'bullshit'. On air on Monday, Morales said viewers should rally to protect the National Weather Service. 'What you need to do is call your representatives and make sure these cuts are stopped,' he said.
