🔗 Share this article How Alphabet’s AI Research Tool is Transforming Tropical Cyclone Forecasting with Rapid Pace As Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a major tropical system. As the primary meteorologist on duty, he forecasted that in just 24 hours the storm would become a category 4 hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had previously made such a bold prediction for rapid strengthening. However, 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 first time in June. And, as predicted, Melissa did become a storm of remarkable power that tore through Jamaica. Growing Dependence on AI Forecasting Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a key factor for his confidence: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa becoming a Category 5 hurricane. While I am not ready to predict that intensity at this time due to path variability, that is still plausible. “There is a high probability that a period of rapid intensification is expected as the storm drifts over exceptionally hot ocean waters which is the highest oceanic heat content in the entire Atlantic basin.” Surpassing Traditional Models The AI model is the pioneer AI model focused on hurricanes, and currently the initial to outperform traditional meteorological experts at their own game. Through all 13 Atlantic storms this season, the AI is top-performing – even beating human forecasters on track predictions. Melissa eventually made landfall in Jamaica at maximum intensity, one of the strongest landfalls recorded in nearly two centuries of data collection across the region. The confident prediction likely gave people in Jamaica additional preparation time to get ready for the catastrophe, possibly saving people and assets. The Way The System Functions Google’s model operates through spotting patterns that traditional time-intensive scientific weather models may miss. “The AI performs far faster than their traditional counterparts, and the processing requirements is more affordable and demanding,” said Michael Lowry, a former meteorologist. “This season’s events has proven in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, superior than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” Lowry added. Understanding Machine Learning It’s important to note, Google DeepMind is an example of AI training – a technique that has been used in data-heavy sciences like meteorology for years – and is distinct from generative AI like ChatGPT. Machine learning takes mounds of data and pulls out patterns from them in a manner that its system only requires minutes to generate an result, and can do so on a desktop computer – in strong contrast to the primary systems that authorities have used for decades that can take hours to process and need the largest high-performance systems in the world. Expert Responses and Future Developments Still, the fact that the AI could exceed earlier gold-standard legacy models so quickly is nothing short of amazing to weather scientists who have spent their careers trying to predict the world’s strongest storms. “I’m impressed,” said James Franklin, a former expert. “The sample is now large enough that it’s pretty clear this is not a case of beginner’s luck.” He noted that while the AI is beating all competing systems on forecasting the trajectory of storms globally this year, like many AI models it occasionally gets extreme strength forecasts wrong. It struggled with another storm earlier this year, as it was similarly experiencing rapid intensification to category 5 above the Caribbean. During the next break, Franklin stated he intends to talk with the company about how it can enhance the AI results more useful for experts by providing additional internal information they can utilize to assess exactly why it is coming up with its answers. “A key concern that troubles me is that although these predictions appear highly accurate, the output of the system is kind of a black box,” said Franklin. Broader Industry Trends There has never been a commercial entity that has produced a top-level forecasting system which grants experts a view of its methods – unlike nearly all systems which are provided 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 challenging meteorological problems. The authorities also have their own artificial intelligence systems in the works – which have also shown better performance over previous non-AI versions. The next steps in AI weather forecasts seem to be new firms taking swings at previously difficult problems such as long-range forecasts and better advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also deploying its proprietary weather balloons to address deficiencies in the national monitoring system.