As Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a monster hurricane.
Serving as primary meteorologist on duty, he forecasted that in just 24 hours the weather system would intensify into a category 4 hurricane and begin a turn towards the coast of Jamaica. Not a single expert had previously made this confident prediction for rapid strengthening.
But, Papin possessed a secret advantage: artificial intelligence in the form of Google’s new DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa evolved into a storm of remarkable power that ravaged Jamaica.
Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his certainty: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa reaching a Category 5 hurricane. While I am not ready to predict that intensity yet due to track uncertainty, that remains a possibility.
“There is a high probability that a period of rapid intensification will occur as the storm moves slowly over exceptionally hot sea temperatures which represent the highest oceanic heat content in the whole Atlantic basin.”
Google DeepMind is the first artificial intelligence system dedicated to hurricanes, and now the initial to outperform standard meteorological experts at their own game. Across all 13 Atlantic storms this season, the AI is top-performing – even beating experts on path forecasts.
The hurricane eventually made landfall in Jamaica at category 5 strength, among the most powerful landfalls recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction probably provided residents additional preparation time to prepare for the catastrophe, potentially preserving people and assets.
The AI system works by identifying trends that conventional time-intensive physics-based weather models may miss.
“The AI performs far faster than their physics-based cousins, and the processing requirements is less expensive and demanding,” said Michael Lowry, a former meteorologist.
“What this hurricane season has proven in short order is that the recent artificial intelligence systems are competitive with and, in certain instances, superior than the less rapid physics-based forecasting tools we’ve relied upon,” he added.
To be sure, Google DeepMind is an instance of machine learning – a method that has been used in data-heavy sciences like meteorology for a long time – and is not generative AI like ChatGPT.
AI training takes mounds of data and pulls out patterns from them in a manner that its model only requires minutes to come up with an answer, and can do so on a desktop computer – in strong contrast to the flagship models that authorities have utilized for years that can take hours to process and need the largest supercomputers in the world.
Nevertheless, the reality that the AI could exceed earlier top-tier legacy models so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the most intense storms.
“It’s astonishing,” commented James Franklin, a former expert. “The sample is sufficient that it’s evident this is not just beginner’s luck.”
He noted that although the AI is beating all competing systems on predicting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It struggled with another storm earlier this year, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.
During the next break, Franklin stated he plans to discuss with Google about how it can enhance the AI results even more helpful for forecasters by providing extra under-the-hood data they can utilize to assess exactly why it is coming up with its answers.
“The one thing that troubles me is that although these predictions seem to be really, really good, the output of the system is kind of a opaque process,” said Franklin.
Historically, no a private, for-profit company that has produced a high-performance weather model which grants experts a peek into its techniques – in contrast to most systems which are offered at no cost to the general audience in their full form by the governments that created and operate them.
The company is not alone in adopting AI to solve difficult weather forecasting problems. The authorities are developing their respective AI weather models in the works – which have also shown improved skill over earlier non-AI versions.
Future developments in AI weather forecasts appear to involve new firms tackling formerly difficult problems such as long-range forecasts and improved early alerts of tornado outbreaks and sudden deluges – and they have secured federal support to pursue this. One company, WindBorne Systems, is even deploying its own atmospheric sensors to fill the gaps in the national monitoring system.
A tech enthusiast and writer passionate about emerging technologies and their impact on society, with a background in software development.
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