🔗 Share this article The Way Google’s DeepMind System is Transforming Hurricane Forecasting with Speed When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it was about to grow into a monster hurricane. Serving as primary meteorologist on duty, he forecasted that in a single day the weather system would intensify into a category 4 hurricane and begin a turn towards the coast of Jamaica. Not a single expert had previously made this confident forecast for quick intensification. But, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa evolved into a system of remarkable power that tore through Jamaica. Growing Dependence on Artificial Intelligence Forecasting Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI simulation runs indicate Melissa becoming a Category 5 storm. While I am unprepared to forecast that strength yet given track uncertainty, that remains a possibility. “It appears likely that a phase of rapid intensification will occur as the system drifts over very warm sea temperatures which is the highest oceanic heat content in the whole Atlantic basin.” Surpassing Traditional Systems The AI model is the first artificial intelligence system dedicated to tropical cyclones, and currently the first to beat standard meteorological experts at their specialty. Through all 13 Atlantic storms this season, Google’s model is top-performing – even beating human forecasters on path forecasts. Melissa eventually made landfall in Jamaica at maximum intensity, one of the strongest landfalls recorded in almost 200 years of data collection across the Atlantic basin. The confident prediction probably provided people in Jamaica extra time to get ready for the disaster, possibly saving lives and property. How Google’s System Functions Google’s model operates through spotting patterns that conventional time-intensive scientific weather models may miss. “They do it much more quickly than their traditional counterparts, and the computing power is more affordable and demanding,” said Michael Lowry, a former meteorologist. “This season’s events has proven in short order is that the newcomer artificial intelligence systems are competitive with and, in certain instances, superior than the slower traditional weather models we’ve relied upon,” Lowry said. Clarifying AI Technology It’s important to note, the system is an instance of AI training – a method that has been employed in data-heavy sciences like weather science for a long time – and is not creative artificial intelligence like ChatGPT. AI training processes large datasets and pulls out patterns from them in a manner that its system only requires minutes to generate an answer, and can do so on a desktop computer – in strong contrast to the primary systems that authorities have utilized for years that can take hours to process and require some of the biggest supercomputers in the world. Expert Responses and Future Advances Still, the fact that the AI could exceed earlier top-tier traditional systems so rapidly is truly remarkable to weather scientists who have spent their careers trying to predict the most intense storms. “I’m impressed,” said James Franklin, a retired forecaster. “The data is now large enough that it’s pretty clear this is not a case of beginner’s luck.” Franklin noted that while Google DeepMind is outperforming all competing systems on forecasting the future path of storms globally this year, like many AI models it occasionally gets high-end intensity forecasts wrong. It struggled with another storm previously, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean. In the coming offseason, he said he intends to talk with Google about how it can make the DeepMind output more useful for forecasters by offering extra under-the-hood data they can use to evaluate the reasons it is coming up with its conclusions. “A key concern that troubles me is that while these predictions appear really, really good, the output of the system is kind of a black box,” said Franklin. Broader Industry Trends There has never been a private, for-profit company that has produced a top-level forecasting system which grants experts a view of its techniques – in contrast to nearly all other models which are offered free to the public in their full form by the governments that designed and maintain them. Google is not the only one in adopting artificial intelligence to address difficult weather forecasting problems. The US and European governments also have their own AI weather models in the development phase – which have demonstrated better performance over previous traditional systems. The next steps in artificial intelligence predictions appear to involve new firms tackling previously difficult problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also launching its own weather balloons to fill the gaps in the US weather-observing network.