🔗 Share this article How Google’s AI Research Tool is Transforming Hurricane Prediction with Rapid Pace When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it was about to grow into a monster hurricane. Serving as primary meteorologist on duty, he predicted that in a single day the weather system would intensify into a severe hurricane and begin a turn towards the coast of Jamaica. Not a single expert had ever issued such a bold forecast for quick intensification. But, Papin had an ace up his sleeve: AI technology in the form of the tech giant’s new DeepMind cyclone prediction system – launched for the initial occasion in June. True to the forecast, Melissa did become a storm of astonishing strength that tore through Jamaica. Growing Reliance on Artificial Intelligence Predictions Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his confidence: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa reaching a most intense storm. While I am not ready to predict that strength at this time due to path variability, that remains a possibility. “It appears likely that a period of rapid intensification will occur as the storm moves slowly over very warm sea temperatures which is the highest oceanic heat content in the whole Atlantic basin.” Outperforming Traditional Systems Google DeepMind is the first AI model focused on tropical cyclones, and currently the initial to beat traditional weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, Google’s model is the best – even beating human forecasters on path forecasts. The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest landfalls recorded in almost 200 years of record-keeping across the Atlantic basin. The confident prediction likely gave residents extra time to get ready for the disaster, possibly saving lives and property. How Google’s Model Functions Google’s model operates through spotting patterns that conventional time-intensive physics-based prediction systems may miss. “They do it much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” stated Michael Lowry, a ex meteorologist. “What this hurricane season has proven in quick time is that the newcomer artificial intelligence systems are competitive with and, in certain instances, more accurate than the less rapid traditional forecasting tools we’ve traditionally leaned on,” he said. Understanding AI Technology To be sure, the system is an example of AI training – a technique that has been used in data-heavy sciences like weather science for years – and is not creative artificial intelligence like ChatGPT. Machine learning takes large datasets and pulls out patterns from them in a manner that its model only requires minutes to generate an result, and can operate on a standard PC – in sharp difference to the flagship models that governments have used for decades that can require many hours to run and need the largest high-performance systems in the world. Professional Reactions and Future Developments Still, the reality that Google’s model could outperform earlier top-tier legacy models so quickly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the world’s strongest weather systems. “I’m impressed,” commented James Franklin, a former forecaster. “The data is sufficient that it’s pretty clear this is not a case of chance.” He said that while the AI is beating all competing systems on forecasting the trajectory of storms globally this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. It struggled with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean. In the coming offseason, Franklin stated he intends to talk with the company about how it can make the DeepMind output even more helpful for forecasters by providing extra under-the-hood data they can use to assess exactly why it is coming up with its conclusions. “A key concern that troubles me is that although these predictions seem to be highly accurate, the output of the system is essentially a opaque process,” said Franklin. Wider Industry Developments There has never been a private, for-profit company that has developed a top-level weather model which grants experts a view of its methods – unlike most other models which are provided free to the public in their entirety by the governments that designed and maintain them. The company is not alone in starting to use AI to solve difficult meteorological problems. The US and European governments are developing their respective AI weather models in the works – which have also shown better performance over earlier non-AI versions. The next steps in AI weather forecasts seem to be startup companies tackling previously tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and flash flooding – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even deploying its proprietary weather balloons to address deficiencies in the national monitoring system.