🔗 Share this article The Way Google’s DeepMind System is Transforming Tropical Cyclone Prediction with Speed When Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it was about to grow into a major tropical system. As the primary meteorologist on duty, he forecasted that in just 24 hours the weather system would intensify into a category 4 hurricane and start shifting towards the coast of Jamaica. Not a single expert had previously made this confident prediction for rapid strengthening. But, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s recently introduced DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa evolved into a storm of remarkable power that ravaged Jamaica. Increasing Dependence on Artificial Intelligence Predictions Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his confidence: “Approximately 40/50 AI ensemble members indicate Melissa becoming a Category 5 hurricane. While I am unprepared to forecast that strength yet due to track uncertainty, that is still plausible. “There is a high probability that a phase of quick strengthening will occur as the system drifts over exceptionally hot sea temperatures which represent the most extreme marine thermal energy in the entire Atlantic basin.” Outperforming Conventional Models Google DeepMind is the pioneer artificial intelligence system focused on hurricanes, and now the first to outperform standard meteorological experts at their own game. Across all tropical systems this season, the AI is the best – surpassing human forecasters on track predictions. Melissa ultimately struck in Jamaica at maximum strength, one of the strongest coastal impacts recorded in nearly two centuries of data collection across the Atlantic basin. The confident prediction probably provided people in Jamaica additional preparation time to prepare for the catastrophe, possibly saving people and assets. The Way Google’s System Works The AI system operates through identifying trends that traditional time-intensive scientific prediction systems may overlook. “They do it far faster than their physics-based cousins, and the computing power is less expensive and demanding,” stated Michael Lowry, a ex meteorologist. “What this hurricane season has proven in quick time is that the recent artificial intelligence systems are competitive with and, in certain instances, superior than the slower traditional weather models we’ve relied upon,” he said. Understanding Machine Learning It’s important to note, the system is an instance of AI training – a method that has been used in data-heavy sciences like weather science for years – and is not generative AI like ChatGPT. Machine learning processes mounds of data and pulls out patterns from them in a such a way that its model only takes a few minutes to come up with an answer, and can do so on a desktop computer – in strong contrast to the primary systems that authorities have utilized for decades that can take hours to run and require some of the biggest supercomputers in the world. Expert Responses and Future Advances Nevertheless, the fact that the AI could outperform earlier top-tier legacy models so quickly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the most intense weather systems. “It’s astonishing,” commented James Franklin, a retired expert. “The sample is sufficient that it’s pretty clear this is not a case of chance.” He said that while the AI is beating all other models on forecasting the future path of storms globally this year, like many AI models it sometimes errs on extreme strength forecasts wrong. It had difficulty with another storm earlier this year, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean. In the coming offseason, Franklin said he plans to discuss with the company about how it can enhance the AI results even more helpful for forecasters by offering additional under-the-hood data they can use to evaluate the reasons it is coming up with its answers. “A key concern that nags at me is that while these forecasts appear highly accurate, the results of the system is kind of a black box,” remarked Franklin. Wider Sector Trends Historically, no 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 provided free to the public in their full form by the authorities that designed and maintain them. The company is not the only one in adopting AI to solve challenging meteorological problems. The US and European governments also have their respective artificial intelligence systems in the works – which have also shown improved skill over earlier non-AI versions. The next steps in AI weather forecasts appear to involve startup companies tackling previously tough-to-solve problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and flash flooding – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is even deploying its own weather balloons to address deficiencies in the US weather-observing network.