🔗 Share this article The Way Alphabet’s AI Research Tool is Transforming Hurricane Prediction with Speed As Developing Cyclone Melissa swirled off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane. As the lead forecaster on duty, he forecasted that in a single day the storm would become a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had ever issued this confident forecast for rapid strengthening. However, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s new DeepMind hurricane model – launched for the initial occasion in June. True to the forecast, Melissa evolved into a storm of remarkable power that tore through Jamaica. Growing Reliance on Artificial Intelligence Forecasting Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 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 storm. While I am not ready to forecast that intensity yet given path variability, that is still plausible. “It appears likely that a phase of rapid intensification is expected as the system moves slowly over exceptionally hot sea temperatures which represent the highest oceanic heat content in the entire Atlantic basin.” Outperforming Conventional Systems The AI model is the first artificial intelligence system dedicated to hurricanes, and now the initial to beat standard meteorological experts at their own game. Across all 13 Atlantic storms so far this year, the AI is the best – even beating experts on track predictions. The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls ever documented in nearly two centuries of data collection across the region. Papin’s bold forecast likely gave people in Jamaica extra time to get ready for the catastrophe, possibly saving lives and property. The Way The System Works The AI system works by spotting patterns that conventional lengthy physics-based weather models may overlook. “The AI performs much more quickly than their traditional counterparts, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a ex forecaster. “What this hurricane season has demonstrated in short order is that the recent AI weather models are on par with and, in certain instances, superior than the slower traditional forecasting tools we’ve relied upon,” Lowry added. Understanding AI Technology It’s important to note, the system is an instance of AI training – a method that has been employed in data-heavy sciences like meteorology for a long time – and is distinct from generative AI like ChatGPT. AI training processes mounds of data and pulls out patterns from them in a manner that its model only takes a few minutes to come up with an result, and can do so on a standard PC – in strong contrast to the flagship models that governments have used for decades that can require many hours to run and need some of the biggest supercomputers in the world. Expert Reactions and Future Developments Still, the reality that the AI could outperform earlier top-tier legacy models so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the world’s strongest storms. “I’m impressed,” said James Franklin, a former expert. “The data is now large enough that it’s evident this is not a case of beginner’s luck.” He said that although the AI is outperforming all competing systems on predicting the future path of storms worldwide this year, like many AI models it sometimes errs on extreme strength forecasts wrong. It struggled with another storm previously, as it was also undergoing quick strengthening to maximum intensity above the Caribbean. During the next break, Franklin stated he plans to discuss with Google about how it can make the AI results even more helpful for forecasters by offering additional under-the-hood data they can utilize to evaluate the reasons it is producing its answers. “A key concern that troubles me is that although these predictions seem to be highly accurate, the results of the system is kind of a opaque process,” said Franklin. Wider Sector Developments Historically, no a commercial entity that has produced a top-level weather model which allows researchers a view of its methods – in contrast to nearly all systems which are provided free to the general audience in their full form by the governments that designed and maintain them. Google is not the only one in adopting AI to solve difficult weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the development phase – which have also shown better performance over earlier traditional systems. The next steps in AI weather forecasts appear to involve new firms taking swings at formerly tough-to-solve problems such as long-range forecasts and improved advance warnings of tornado outbreaks and flash flooding – and they have secured US government funding to do so. One company, WindBorne Systems, is even deploying its proprietary weather balloons to fill the gaps in the US weather-observing network.