🔗 Share this article The Way Google’s AI Research Tool is Transforming Hurricane Prediction with Speed As Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it would soon grow into a monster hurricane. Serving as lead forecaster on duty, he predicted that in just 24 hours the weather system would intensify into a severe hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had ever issued this confident forecast for quick intensification. But, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s new DeepMind hurricane model – launched for the initial occasion in June. True to the forecast, Melissa did become a system of astonishing strength that tore through Jamaica. Increasing Dependence on AI Predictions Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his confidence: “Roughly 40/50 AI ensemble members show Melissa reaching a Category 5 storm. While I am not ready to forecast that strength at this time given path variability, that is still plausible. “There is a high probability that a period of quick strengthening is expected as the system moves slowly over exceptionally hot ocean waters which is the most extreme marine thermal energy in the entire Atlantic basin.” Outperforming Conventional Systems The AI model is the pioneer artificial intelligence system dedicated to hurricanes, and now the first to beat traditional meteorological experts at their own game. Across all tropical systems so far this year, the AI is the best – surpassing experts on track predictions. Melissa eventually made landfall in Jamaica at category 5 intensity, one of the strongest coastal impacts recorded in nearly two centuries of data collection across the region. The confident prediction likely gave residents extra time to get ready for the catastrophe, potentially preserving lives and property. How The Model Works The AI system works by spotting patterns that traditional lengthy scientific prediction systems may miss. “The AI performs much more quickly than their physics-based cousins, and the computing power is more affordable and time consuming,” said Michael Lowry, a ex forecaster. “This season’s events has demonstrated in quick time is that the recent artificial intelligence systems are on par with and, in some cases, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” he said. Understanding AI Technology To be sure, the system is an instance of AI training – a method that has been employed in data-heavy sciences like weather science for years – and is distinct from creative artificial intelligence like ChatGPT. AI training takes large datasets and pulls out patterns from them in a such a way that its system only requires minutes to generate an answer, and can operate on a standard PC – in sharp difference to the primary systems that authorities have used for years that can require many hours to run and require the largest supercomputers in the world. Professional Responses and Future Advances Nevertheless, the fact that Google’s model could outperform earlier gold-standard traditional systems so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the most intense weather systems. “It’s astonishing,” commented James Franklin, a retired expert. “The sample is now large enough that it’s evident this is not a case of chance.” Franklin noted that although the AI is beating all other models on predicting the future path of storms worldwide this year, like many AI models it sometimes errs on high-end intensity forecasts wrong. It had difficulty with Hurricane Erin previously, as it was also undergoing rapid intensification to category 5 north of the Caribbean. During the next break, he said he plans to talk with the company about how it can enhance the AI results more useful for experts by offering extra under-the-hood data they can utilize to assess exactly why it is producing its conclusions. “The one thing that troubles me is that while these predictions appear really, really good, the results of the model is kind of a opaque process,” said Franklin. Broader Industry Developments There has never been a private, for-profit company that has developed a high-performance forecasting system which grants experts a peek into its methods – unlike nearly all systems which are offered at no cost to the general audience in their full form by the governments that created and operate them. The company is not the only one in adopting AI to solve challenging weather forecasting problems. The authorities also have their own AI weather models in the development phase – which have demonstrated improved skill over earlier traditional systems. The next steps in artificial intelligence predictions appear to involve startup companies tackling formerly difficult problems such as sub-seasonal outlooks and improved advance warnings of severe weather and sudden deluges – and they have secured federal support to pursue this. One company, WindBorne Systems, is also deploying its own weather balloons to fill the gaps in the US weather-observing network.