🔗 Share this article How Google’s AI Research Tool is Revolutionizing Tropical Cyclone Forecasting with Speed When Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a monster hurricane. Serving as lead forecaster on duty, he forecasted that in a single day the weather system would become a severe hurricane and start shifting towards the Jamaican shoreline. No forecaster had previously made this confident prediction for rapid strengthening. But, Papin possessed a secret advantage: AI technology in the form of Google’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. True to the forecast, Melissa did become a system of remarkable power that ravaged Jamaica. Growing Dependence on Artificial Intelligence Predictions Forecasters are increasingly leaning hard on Google DeepMind. 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 show Melissa reaching a Category 5 hurricane. Although I am not ready to forecast that intensity yet due to path variability, that is still plausible. “There is a high probability that a phase of quick strengthening will occur as the system drifts over very warm sea temperatures which is the highest marine thermal energy in the whole Atlantic basin.” Outperforming Conventional Models Google DeepMind is the first AI model focused on hurricanes, and currently the first to outperform traditional weather forecasters at their own game. Across all tropical systems this season, Google’s model is top-performing – even beating experts on track predictions. The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls ever documented in almost 200 years of record-keeping 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. How Google’s System Works Google’s model operates through identifying trends that traditional time-intensive scientific prediction systems may miss. “They do it far faster than their physics-based cousins, and the computing power is more affordable and demanding,” stated Michael Lowry, a ex forecaster. “What this hurricane season has demonstrated in short order is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the less rapid physics-based weather models we’ve relied upon,” Lowry added. Understanding AI Technology To be sure, Google DeepMind is an example of machine learning – a technique that has been employed in research fields like weather science for a long time – and is not generative AI like ChatGPT. AI training takes large datasets and extracts trends from them in a such a way that its system only takes a few minutes to generate an result, and can operate on a desktop computer – in strong contrast to the primary systems that governments have utilized for decades that can require many hours to run and require the largest supercomputers in the world. Expert Reactions and Future Advances Nevertheless, the fact that Google’s model could outperform previous gold-standard legacy models so quickly 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 sample is now large enough that it’s pretty clear this is not just chance.” He said that although Google DeepMind is beating all other models on forecasting the trajectory of hurricanes worldwide this year, similar to other systems it occasionally gets extreme strength predictions inaccurate. It had difficulty with Hurricane Erin previously, as it was also undergoing quick strengthening to category 5 north of the Caribbean. During the next break, he stated he plans to talk with Google about how it can enhance the AI results more useful for experts by providing additional internal information they can use to evaluate the reasons it is coming up with its conclusions. “The one thing that troubles me is that while these forecasts seem to be really, really good, the results of the model is kind of a opaque process,” said Franklin. Broader Sector Trends There has never been a commercial entity that has produced a high-performance forecasting system which grants experts a view of its techniques – unlike most systems which are offered free to the general audience in their full form by the governments that designed and maintain them. Google is not the only one in starting to use AI to address difficult weather forecasting problems. The authorities are developing their respective artificial intelligence systems in the development phase – which have also shown improved skill over previous traditional systems. Future developments in artificial intelligence predictions appear to involve startup companies tackling formerly tough-to-solve problems such as long-range forecasts and improved early alerts of tornado outbreaks and flash flooding – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the US weather-observing network.