🔗 Share this article How Alphabet’s DeepMind Tool is Revolutionizing Tropical Cyclone Prediction with Rapid Pace When Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a major tropical system. As the primary meteorologist on duty, he predicted that in just 24 hours the weather system would intensify into a severe hurricane and begin a turn towards the Jamaican shoreline. No forecaster had previously made this confident forecast for quick intensification. But, Papin possessed a secret advantage: artificial intelligence in the form of Google’s new DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa evolved into a system of remarkable power that ravaged Jamaica. Growing Reliance on Artificial Intelligence Predictions Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his confidence: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa reaching a most intense hurricane. Although I am not ready to predict that strength yet given track uncertainty, that remains a possibility. “It appears likely that a phase of rapid intensification will occur as the system moves slowly over very warm ocean waters which represent the most extreme marine thermal energy in the whole Atlantic basin.” Surpassing Traditional Models The AI model is the first artificial intelligence system dedicated to tropical cyclones, and currently the initial to beat standard weather forecasters at their specialty. Across all tropical systems so far this year, the AI is the best – surpassing experts on track predictions. The hurricane eventually made landfall in Jamaica at maximum intensity, among the most powerful landfalls recorded in almost 200 years of record-keeping across the region. Papin’s bold forecast likely gave residents additional preparation time to get ready for the disaster, possibly saving people and assets. The Way Google’s Model Functions Google’s model works by identifying trends that traditional lengthy scientific weather models may miss. “They do it much more quickly than their traditional counterparts, and the processing requirements is more affordable and demanding,” said Michael Lowry, a ex forecaster. “This season’s events has demonstrated in short order is that the recent artificial intelligence systems are competitive with and, in certain instances, superior than the slower physics-based forecasting tools we’ve relied upon,” he added. Clarifying Machine Learning It’s important to note, Google DeepMind is an example of machine learning – a method that has been used in research fields like meteorology for years – and is not creative artificial intelligence like ChatGPT. Machine learning takes mounds of data and pulls out patterns from them in a such a way that its model only requires minutes to generate an result, and can do so on a desktop computer – in sharp difference to the primary systems that authorities have used for years that can require many hours to process and require the largest high-performance systems in the world. Professional Responses and Future Developments Still, the reality that the AI could outperform earlier top-tier traditional systems so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to predict the world’s strongest weather systems. “I’m impressed,” commented James Franklin, a retired expert. “The data is now large enough that it’s pretty clear this is not just beginner’s luck.” He said that while the AI is outperforming all competing systems on forecasting the trajectory of hurricanes globally this year, similar to other systems it sometimes errs on extreme strength forecasts wrong. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean. In the coming offseason, he said he intends to discuss with the company about how it can make the AI results more useful for forecasters by offering additional internal information they can utilize to evaluate the reasons it is producing its conclusions. “The one thing that troubles me is that while these forecasts seem to be highly accurate, the output of the system is kind of a opaque process,” said Franklin. Broader Industry Developments There has never been a private, for-profit company that has produced a high-performance weather model which allows researchers a view of its techniques – in contrast to nearly all other models which are offered at no cost to the general audience in their entirety by the authorities that designed and maintain them. Google is not the only one in adopting artificial intelligence to solve challenging meteorological problems. The US and European governments also have their respective artificial intelligence systems in the works – which have demonstrated better performance over previous non-AI versions. The next steps in artificial intelligence predictions appear to involve startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and improved advance warnings of severe weather and sudden deluges – and they are receiving US government funding to do so. One company, WindBorne Systems, is even deploying its own atmospheric sensors to address deficiencies in the national monitoring system.