🔗 Share this article How Alphabet’s DeepMind Tool is Transforming Tropical Cyclone Forecasting with Speed When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it was about to grow into a monster hurricane. Serving as lead forecaster on duty, he predicted that in a single day the weather system would become a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had previously made such a bold forecast for quick intensification. However, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa evolved into a system of remarkable power that ravaged Jamaica. Increasing Reliance on Artificial Intelligence Predictions Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his certainty: “Approximately 40/50 Google DeepMind simulation runs show Melissa reaching a Category 5 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 quick strengthening will occur as the system drifts over very warm sea temperatures which is the most extreme marine thermal energy in the entire Atlantic basin.” Outperforming Traditional Models The AI model is the pioneer AI model focused on hurricanes, and now the initial to outperform traditional weather forecasters at their specialty. Through all tropical systems this season, the AI is the best – surpassing experts on path forecasts. Melissa eventually made landfall in Jamaica at maximum intensity, among the most powerful landfalls ever documented in nearly two centuries of data collection across the Atlantic basin. The confident prediction probably provided residents extra time to get ready for the disaster, potentially preserving lives and property. The Way Google’s System Functions The AI system works by spotting patterns that conventional time-intensive physics-based prediction systems may miss. “The AI performs far faster than their physics-based cousins, and the computing power is more affordable and demanding,” stated Michael Lowry, a former meteorologist. “What this hurricane season has demonstrated in quick time is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the slower physics-based forecasting tools we’ve relied upon,” he added. Clarifying AI Technology It’s important to note, Google DeepMind is an example of AI training – a technique that has been employed in research fields like weather science for a long time – and is not creative artificial intelligence like ChatGPT. Machine learning processes mounds of data and extracts trends from them in a such a way that its system only requires minutes to come up with an answer, and can operate on a desktop computer – in sharp difference to the primary systems that authorities have used for decades that can take hours to run and require some of the biggest high-performance systems in the world. Professional Responses and Future Developments Nevertheless, the fact that Google’s model could exceed previous top-tier traditional systems so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the most intense weather systems. “It’s astonishing,” commented James Franklin, a retired expert. “The data is sufficient that it’s pretty clear this is not just chance.” Franklin said that while Google DeepMind is beating all other models on predicting the future path of storms globally this year, like many AI models it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty with Hurricane Erin previously, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean. In the coming offseason, he stated he intends to discuss with Google about how it can make the AI results more useful for experts by offering extra internal information they can use to assess exactly why it is producing its conclusions. “A key concern that troubles me is that although these forecasts appear highly accurate, the output of the system is essentially a black box,” said Franklin. Broader Industry Developments Historically, no a commercial entity that has developed a top-level forecasting system which grants experts a view of its methods – unlike most systems which are offered free to the general audience in their entirety by the governments that designed and maintain them. Google is not alone in adopting artificial intelligence to address difficult weather forecasting problems. The authorities also have their respective artificial intelligence systems in the development phase – which have demonstrated improved skill over previous traditional systems. The next steps in artificial intelligence predictions appear to involve new firms tackling formerly tough-to-solve problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and sudden deluges – and they have secured US government funding to do so. One company, WindBorne Systems, is also deploying its own weather balloons to fill the gaps in the national monitoring system.