🔗 Share this article How Alphabet’s DeepMind Tool is Revolutionizing Tropical Cyclone Prediction with Speed As Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it was about to grow into a major tropical system. Serving as primary meteorologist on duty, he forecasted that in just 24 hours the storm would intensify into a category 4 hurricane and begin a turn towards the coast of Jamaica. Not a single expert had ever issued this confident forecast for quick intensification. However, Papin had an ace up his sleeve: AI technology in the form of Google’s new DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa did become a storm of remarkable power that ravaged Jamaica. Growing Dependence on Artificial Intelligence Forecasting Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his confidence: “Approximately 40/50 Google DeepMind simulation runs show Melissa reaching a Category 5 hurricane. Although I am not ready to forecast that intensity at this time given path variability, that is still plausible. “It appears likely that a period of rapid intensification will occur as the storm drifts over very warm ocean waters which is the most extreme oceanic heat content in the whole Atlantic basin.” Surpassing Traditional Models Google DeepMind is the first AI model dedicated to hurricanes, and currently the first to beat standard meteorological experts at their specialty. Across all 13 Atlantic storms this season, Google’s model is the best – surpassing human forecasters on track predictions. Melissa ultimately struck in Jamaica at category 5 strength, among the most powerful coastal impacts ever documented in almost 200 years of data collection across the region. Papin’s bold forecast likely gave residents extra time to get ready for the catastrophe, possibly saving people and assets. The Way The System Functions The AI system works by spotting patterns that traditional lengthy scientific prediction systems may miss. “They do it much more quickly than their traditional counterparts, and the computing power is more affordable and demanding,” said Michael Lowry, a former forecaster. “What this hurricane season has proven in short order is that the newcomer AI weather models are on par with and, in some cases, superior than the slower traditional forecasting tools we’ve traditionally leaned on,” Lowry said. Clarifying Machine Learning It’s important to note, the system is an instance of machine learning – a method that has been used in research fields like meteorology for years – and is distinct from generative AI like ChatGPT. AI training takes mounds of data and extracts trends from them in a manner that its model only takes a few minutes to come up with an answer, and can operate on a desktop computer – in sharp difference to the flagship models that governments have utilized for years that can require many hours to process and need some of the biggest high-performance systems in the world. Professional Reactions and Future Advances Still, the reality that the AI could exceed earlier gold-standard legacy models so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to predict the world’s strongest storms. “It’s astonishing,” commented James Franklin, a retired forecaster. “The sample is sufficient that it’s pretty clear this is not just beginner’s luck.” Franklin said that while Google DeepMind is beating all competing systems on predicting the future path of hurricanes worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty with another storm earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean. In the coming offseason, he said he intends to talk with Google about how it can enhance the AI results even more helpful for experts by providing additional internal information they can utilize to assess exactly why it is producing its answers. “A key concern that nags at me is that although these predictions seem to be highly accurate, the output of the system is essentially a black box,” remarked Franklin. Broader Industry Trends Historically, no a commercial entity that has produced a top-level forecasting system which allows researchers a view of its techniques – unlike nearly all other models which are provided at no cost to the public in their full form by the governments that created and operate them. Google is not alone in adopting AI to solve challenging meteorological problems. The authorities also have their own artificial intelligence systems in the works – which have demonstrated better performance over previous non-AI versions. The next steps in AI weather forecasts seem to be startup companies tackling previously tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and sudden deluges – and they are receiving US government funding to do so. One company, WindBorne Systems, is also launching its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.