🔗 Share this article The Way Google’s DeepMind Tool is Revolutionizing Hurricane Forecasting with Speed As Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon grow into a monster hurricane. As the lead forecaster on duty, he predicted that in a single day the storm would intensify into a category 4 hurricane and begin a turn towards the coast of Jamaica. No forecaster had ever issued this confident forecast for rapid strengthening. But, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica. Growing Dependence on AI Predictions Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his public discussion that Google’s model was a key factor for his confidence: “Roughly 40/50 Google DeepMind simulation runs show Melissa becoming a most intense hurricane. Although I am unprepared to forecast that intensity at this time due to track uncertainty, that is still plausible. “It appears likely that a period of quick strengthening will occur as the storm drifts over very warm sea temperatures which represent the most extreme marine thermal energy in the entire Atlantic basin.” Outperforming Conventional Models Google DeepMind is the first artificial intelligence system focused on hurricanes, and now the first to beat traditional meteorological experts at their specialty. Across all 13 Atlantic storms this season, the AI is top-performing – even beating experts on path forecasts. The hurricane eventually made landfall in Jamaica at maximum strength, one of the strongest landfalls recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction likely gave people in Jamaica additional preparation time to get ready for the disaster, potentially preserving lives and property. How The Model Functions Google’s model operates through identifying trends that traditional time-intensive physics-based prediction systems may miss. “The AI performs much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” stated Michael Lowry, a former forecaster. “This season’s events has proven in short order is that the newcomer AI weather models are competitive with and, in some cases, superior than the slower traditional weather models we’ve traditionally leaned on,” Lowry said. Clarifying Machine Learning It’s important to note, the system is an instance of machine learning – a technique that has been employed in data-heavy sciences like meteorology for years – and is distinct from generative AI like ChatGPT. AI training takes mounds of data and pulls out patterns from them in a manner that its model only takes a few minutes to generate an answer, and can operate on a desktop computer – in strong contrast to the flagship models that authorities have used for years that can require many hours to process and need the largest high-performance systems in the world. Expert Responses and Upcoming Advances Nevertheless, the reality that the AI could outperform earlier gold-standard legacy models so rapidly is nothing short of amazing to weather scientists who have spent their careers trying to predict the world’s strongest weather systems. “It’s astonishing,” commented James Franklin, a former forecaster. “The sample is now large enough that it’s evident this is not just beginner’s luck.” Franklin noted that although Google DeepMind is outperforming all other models on forecasting the future path of storms globally this year, like many AI models it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean. During the next break, he stated he intends to talk with the company about how it can enhance the AI results more useful for experts by providing additional internal information they can utilize to assess exactly why it is coming up with its conclusions. “The one thing that troubles me is that while these predictions appear highly accurate, the output of the system is kind of a black box,” said Franklin. Broader Industry Developments Historically, no a commercial entity that has developed a top-level weather model which grants experts a view of its techniques – in contrast to nearly all other models which are provided free to the public in their full form by the governments that designed and maintain them. Google is not the only one in adopting AI to address challenging meteorological problems. The authorities also have their respective AI weather models in the works – which have demonstrated better performance over previous non-AI versions. The next steps in artificial intelligence predictions seem to be new firms taking swings at previously difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and sudden deluges – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is also launching its proprietary atmospheric sensors to fill the gaps in the national monitoring system.