🔗 Share this article How Alphabet’s DeepMind Tool is Transforming Tropical Cyclone Forecasting with Speed When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a major tropical system. As the primary meteorologist on duty, he predicted that in a single day the weather system would intensify into a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made this confident prediction for quick intensification. However, Papin had an ace up his sleeve: AI technology in the guise of Google’s recently introduced DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa evolved into a system of astonishing strength that ravaged Jamaica. Growing Reliance on Artificial Intelligence Forecasting Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a key factor for his confidence: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa becoming a most intense storm. Although I am unprepared to predict that intensity yet due to path variability, that is still plausible. “There is a high probability that a phase of rapid intensification will occur as the storm moves slowly over very warm sea temperatures which represent the most extreme marine thermal energy in the whole Atlantic basin.” Outperforming Traditional Systems Google DeepMind is the pioneer AI model dedicated to hurricanes, and currently the initial to beat traditional meteorological experts at their own game. Across all tropical systems this season, Google’s model is the best – surpassing experts on track predictions. The hurricane ultimately struck in Jamaica at category 5 intensity, among the most powerful landfalls recorded in nearly two centuries of record-keeping across the region. The confident prediction likely gave residents additional preparation time to prepare for the catastrophe, possibly saving lives and property. The Way The Model Works The AI system works by identifying trends that conventional lengthy physics-based prediction systems may overlook. “They do it much more quickly than their traditional counterparts, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a ex forecaster. “What this hurricane season has proven in short order is that the recent artificial intelligence systems are competitive with and, in certain instances, more accurate than the less rapid traditional weather models we’ve traditionally leaned on,” Lowry said. Clarifying AI Technology It’s important to note, the system is an instance of machine learning – a method that has been employed in data-heavy sciences like meteorology for years – and is not creative artificial intelligence like ChatGPT. AI training takes large datasets and extracts trends from them in a such a way that its model only takes a few minutes to generate an result, and can operate on a desktop computer – in strong contrast to the primary systems that governments have used for years that can take hours to run and need the largest supercomputers in the world. Professional Responses and Upcoming Advances Nevertheless, the reality that the AI could outperform earlier top-tier traditional systems so quickly is truly remarkable to weather scientists who have dedicated their lives trying to predict the world’s strongest storms. “I’m impressed,” commented James Franklin, a retired expert. “The data is sufficient that it’s pretty clear this is not a case of chance.” Franklin said that although the AI is outperforming all competing systems on forecasting the future path of storms globally this year, like many AI models it sometimes errs on high-end intensity forecasts wrong. It struggled with another storm previously, as it was also undergoing quick strengthening to category 5 north of the Caribbean. During the next break, he said he intends to talk with Google about how it can make the DeepMind output even more helpful for experts by providing additional under-the-hood data they can utilize to evaluate the reasons it is coming up with its conclusions. “A key concern that nags at me is that although these predictions appear really, really good, the output of the system is essentially a opaque process,” remarked Franklin. Broader Sector Developments There has never been a private, for-profit company that has developed a top-level weather model which allows researchers a view of its techniques – unlike nearly all systems which are provided at no cost to the public in their full form by the authorities that designed and maintain them. The company is not alone in starting to use AI to solve difficult meteorological problems. The US and European governments are developing their own artificial intelligence systems in the works – which have demonstrated better performance over earlier non-AI versions. The next steps in artificial intelligence predictions seem to be new firms taking swings at 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 do so. One company, WindBorne Systems, is even launching its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.