How Google’s AI Research Tool is Revolutionizing Hurricane Forecasting with Speed
When Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it was about to grow into a major tropical system.
As the primary meteorologist on duty, he forecasted that in a single day the storm would become a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had previously made such a bold forecast for rapid strengthening.
However, 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 AI Forecasting
Meteorologists are heavily relying upon the AI system. During 25 October, Papin explained in his official briefing that the AI tool was a key factor for his certainty: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa becoming a Category 5 hurricane. Although I am unprepared to predict that intensity yet given path variability, that is still plausible.
“It appears likely that a phase of quick strengthening is expected as the system drifts over very warm ocean waters which is the most extreme oceanic heat content in the whole Atlantic basin.”
Outperforming Traditional Models
The AI model is the first AI model focused on tropical cyclones, and now the initial to beat standard weather forecasters at their own game. Through all tropical systems this season, the AI is the best – even beating experts on path forecasts.
Melissa eventually made landfall in Jamaica at maximum strength, among the most powerful landfalls recorded in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave residents additional preparation time to prepare for the disaster, potentially preserving lives and property.
The Way Google’s System Works
Google’s model operates through identifying trends that traditional time-intensive scientific weather models may miss.
“The AI performs far faster than their physics-based cousins, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a former meteorologist.
“This season’s events has demonstrated in short order is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the slower traditional weather models we’ve relied upon,” Lowry added.
Understanding AI Technology
It’s important to note, Google DeepMind is an instance of AI training – a method that has been employed in research fields like meteorology for years – and is distinct from creative artificial intelligence like ChatGPT.
AI training processes mounds of data and pulls out patterns from them in a such a way that its system only takes a few minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the primary systems that authorities have utilized for years that can take hours to process and need some of the biggest high-performance systems in the world.
Expert Responses and Upcoming Advances
Still, the reality that Google’s model could outperform previous gold-standard legacy models so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the most intense weather systems.
“I’m impressed,” commented James Franklin, a retired expert. “The data is now large enough that it’s evident this is not a case of beginner’s luck.”
He noted that while Google DeepMind is beating all other models on predicting the trajectory of hurricanes globally this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty with Hurricane Erin previously, as it was also undergoing quick strengthening to category 5 above the Caribbean.
During the next break, he said he plans to discuss with the company about how it can make the DeepMind output more useful for forecasters by providing additional internal information they can utilize to assess the reasons it is coming up with its answers.
“The one thing that troubles me is that while these predictions seem to be really, really good, the output of the system is essentially a black box,” said Franklin.
Broader Sector Trends
There has never been a private, for-profit company that has produced a top-level weather model which grants experts a peek into its methods – in contrast to most systems which are offered free to the public in their entirety by the authorities that designed and maintain them.
The company is not alone in starting to use AI to address challenging 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 non-AI versions.
The next steps in artificial intelligence predictions seem to be startup companies tackling formerly difficult problems such as long-range forecasts and better early alerts of tornado outbreaks and sudden deluges – and they are receiving federal support to do so. One company, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the national monitoring system.
