🔗 Share this article The Way Google’s DeepMind Tool is Transforming Hurricane Forecasting with Rapid Pace As Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it would soon grow into a monster hurricane. Serving as lead forecaster on duty, he forecasted that in just 24 hours the storm would intensify into a severe hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had previously made such a bold prediction for quick intensification. But, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s new DeepMind hurricane model – 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 Forecasting Meteorologists 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 primary reason for his certainty: “Roughly 40/50 Google DeepMind simulation runs show Melissa reaching a Category 5 storm. While I am not ready to forecast that strength at this time given track uncertainty, that is still plausible. “There is a high probability that a phase of quick strengthening will occur as the storm moves slowly over very warm sea temperatures which is the highest oceanic heat content in the whole Atlantic basin.” Outperforming Traditional Models The AI model is the first artificial intelligence system focused on tropical cyclones, and currently the initial to beat traditional weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, the AI is top-performing – surpassing human forecasters on path forecasts. The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest coastal impacts ever documented in almost 200 years of data collection across the region. Papin’s bold forecast probably provided residents extra time to get ready for the disaster, potentially preserving lives and property. The Way Google’s System Functions Google’s model operates through spotting patterns that traditional lengthy physics-based weather models may miss. “They do it much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” said Michael Lowry, a former meteorologist. “This season’s events has proven in short order is that the recent AI weather models are on par with and, in some cases, superior than the less rapid traditional weather models we’ve relied upon,” Lowry said. Clarifying Machine Learning It’s important to note, Google DeepMind is an instance of machine learning – a technique that has been employed in data-heavy sciences like weather science for years – and is distinct from generative AI like ChatGPT. AI training processes mounds of data and pulls out patterns from them in a manner that its system only takes a few minutes to come up with an answer, and can do so on a desktop computer – in sharp difference to the flagship models that governments have used for decades that can take hours to run and require some of the biggest supercomputers in the world. Professional Reactions and Future Advances Still, the fact that Google’s model could exceed previous gold-standard traditional systems so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the most intense storms. “It’s astonishing,” said James Franklin, a former forecaster. “The sample is now large enough that it’s evident this is not a case of beginner’s luck.” Franklin noted that while the AI is beating all other models on forecasting the trajectory of hurricanes worldwide this year, like many AI models it occasionally gets extreme strength forecasts wrong. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean. In the coming offseason, he said he intends to discuss with Google about how it can make the AI results more useful for forecasters by offering additional under-the-hood data they can use to evaluate the reasons it is producing its answers. “A key concern that nags at me is that while these forecasts seem to be really, really good, the output of the model is essentially a black box,” said Franklin. Wider Industry Trends There has never been a private, for-profit company that has produced a top-level weather model which grants experts a view of its techniques – unlike nearly all other models which are provided at no cost to the public in their full form by the governments that created and operate them. The company is not alone in adopting AI to address challenging meteorological problems. The authorities also have their respective AI weather models in the development phase – which have also shown improved skill over previous traditional systems. The next steps in AI weather forecasts appear to involve startup companies tackling previously tough-to-solve problems such as long-range forecasts and improved advance warnings of tornado outbreaks and flash flooding – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is also deploying its own atmospheric sensors to fill the gaps in the national monitoring system.