🔗 Share this article The Way Alphabet’s AI Research Tool is Transforming Tropical Cyclone Forecasting with Rapid Pace When Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin had confidence it was about to grow into a major tropical system. Serving as lead forecaster on duty, he predicted that in just 24 hours the weather system would become a severe hurricane and start shifting towards the coast of Jamaica. No forecaster 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 hurricane model – launched for the first time in June. True to the forecast, Melissa evolved into a storm of remarkable power that ravaged Jamaica. Growing Dependence on AI Forecasting Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his certainty: “Approximately 40/50 AI simulation runs indicate Melissa becoming a most intense storm. 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 storm drifts over very warm ocean waters which is the most extreme marine thermal energy in the whole Atlantic basin.” Surpassing Traditional Systems Google DeepMind is the first artificial intelligence system focused on tropical cyclones, and now the first to outperform standard meteorological experts at their own game. Through all 13 Atlantic storms so far this year, the AI is top-performing – surpassing human forecasters on path forecasts. The hurricane eventually made landfall in Jamaica at maximum intensity, among the most powerful coastal impacts recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction likely gave people in Jamaica extra time to get ready for the disaster, potentially preserving people and assets. How The Model Functions The AI system operates through identifying trends that traditional lengthy scientific prediction systems may miss. “They do it much more quickly than their traditional counterparts, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a ex forecaster. “This season’s events has proven in short order is that the newcomer artificial intelligence systems are competitive with and, in some cases, superior than the slower physics-based forecasting tools we’ve relied upon,” Lowry said. Understanding AI Technology It’s important to note, Google DeepMind is an instance of AI training – a method that has been used in data-heavy sciences like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT. Machine learning takes mounds of data and pulls out patterns from them in a manner that its model only requires minutes to generate an answer, and can do so on a desktop computer – in sharp difference to the primary systems that authorities have used for decades that can take hours to process and require the largest high-performance systems in the world. Expert Responses and Upcoming Advances Still, the fact that the AI could exceed earlier gold-standard legacy models so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the most intense weather systems. “I’m impressed,” said James Franklin, a former forecaster. “The sample is now large enough that it’s evident this is not just chance.” He noted that although Google DeepMind is beating all competing systems on predicting the trajectory of storms globally this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. It had difficulty with another storm earlier this year, as it was also undergoing quick strengthening to maximum intensity above the Caribbean. In the coming offseason, he stated he plans to talk with the company about how it can enhance the DeepMind output even more helpful for forecasters by providing extra under-the-hood data they can use to evaluate exactly why it is coming up with its answers. “The one thing that troubles me is that while these forecasts appear really, really good, the results of the model is essentially a opaque process,” said Franklin. Wider Sector Trends There has never been a commercial entity that has developed a high-performance weather model which grants experts a view of its methods – unlike nearly all other models which are offered free to the general audience in their entirety by the authorities that created and operate them. Google is not the only one in adopting AI to address difficult weather forecasting problems. The authorities are developing their own AI weather models in the works – which have demonstrated better performance over earlier non-AI versions. Future developments in AI weather forecasts appear to involve startup companies tackling formerly tough-to-solve problems such as long-range forecasts and improved early alerts of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even launching its own atmospheric sensors to fill the gaps in the US weather-observing network.