🔗 Share this article The Way Google’s DeepMind System is Transforming Hurricane Forecasting with Speed As Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a major tropical system. As the primary meteorologist on duty, he forecasted that in a single day the storm would become a severe hurricane and begin a turn towards the coast of Jamaica. No forecaster had ever issued such a bold forecast for quick intensification. But, Papin had an ace up his sleeve: AI technology in the form of the tech giant’s new DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa did become a storm of remarkable power that ravaged Jamaica. Increasing Dependence on AI Predictions Forecasters are heavily relying upon the AI system. During 25 October, Papin explained in his public discussion that the AI tool was a key factor for his certainty: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa becoming a most intense hurricane. Although I am unprepared to forecast that strength yet given path variability, that remains a possibility. “There is a high probability that a phase of quick strengthening is expected as the system moves slowly over very warm sea temperatures which is the most extreme oceanic heat content in the whole Atlantic basin.” Surpassing Traditional Systems The AI model is the pioneer artificial intelligence system focused on hurricanes, and now the first to beat standard meteorological experts at their specialty. Through all 13 Atlantic storms so far this year, Google’s model is top-performing – even beating human forecasters on track predictions. Melissa ultimately struck in Jamaica at maximum intensity, one of the strongest 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, potentially preserving lives and property. The Way The System Works Google’s model operates through spotting patterns that conventional time-intensive physics-based weather models may overlook. “They do it much more quickly than their physics-based cousins, and the computing power is more affordable and time consuming,” stated Michael Lowry, a former meteorologist. “This season’s events has demonstrated in short order is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” he added. Understanding AI Technology To be sure, Google DeepMind is an example of AI training – a technique that has been employed in research fields like meteorology for years – and is distinct from creative artificial intelligence like ChatGPT. Machine learning processes mounds of data and pulls out patterns from them in a such a way 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 authorities have used for years that can require many hours to process and require some of the biggest supercomputers in the world. Professional Responses and Future Developments Still, the fact that Google’s model could outperform earlier top-tier legacy models so quickly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the most intense storms. “I’m impressed,” said James Franklin, a former expert. “The sample is now large enough that it’s pretty clear this is not just chance.” He said that while the AI is beating all other models on forecasting the trajectory of hurricanes globally this year, similar to other systems it occasionally gets extreme strength forecasts wrong. It struggled with another storm previously, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean. During the next break, Franklin stated he intends to talk with Google about how it can make the DeepMind output more useful for experts by providing additional internal information they can utilize to evaluate the reasons it is coming up with its answers. “A key concern that nags at me is that although these predictions appear really, really good, the output of the system is essentially a black box,” remarked Franklin. Broader Industry Developments Historically, no a commercial entity that has developed a top-level forecasting system which allows researchers a peek into its methods – unlike nearly all other models which are offered free to the public in their full form by the governments that designed and maintain them. The company is not the only one in adopting AI to solve difficult meteorological problems. The US and European governments also have their respective AI weather models in the works – which have also shown improved skill over previous traditional systems. Future developments in AI weather forecasts appear to involve new firms tackling formerly tough-to-solve problems such as sub-seasonal outlooks and better early alerts of severe weather and sudden deluges – and they have secured federal support to pursue this. One company, WindBorne Systems, is even deploying its own atmospheric sensors to fill the gaps in the national monitoring system.