When Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a major tropical system.
Serving as lead forecaster on duty, he predicted that in just 24 hours the weather system would intensify into a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had ever issued this confident prediction for quick intensification.
However, Papin possessed a secret advantage: AI technology in the form of Google’s recently introduced DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa evolved into a system of astonishing strength that tore through Jamaica.
Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI ensemble members indicate Melissa reaching a most intense hurricane. While I am not ready to forecast that strength at this time given 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 most extreme oceanic heat content in the entire Atlantic basin.”
The AI model is the pioneer AI model focused on tropical cyclones, and currently the initial to outperform traditional weather forecasters at their specialty. Through all 13 Atlantic storms this season, Google’s model is the best – even beating human forecasters on track predictions.
Melissa eventually made landfall in Jamaica at maximum intensity, among the most powerful landfalls recorded in almost 200 years of data collection across the region. Papin’s bold forecast probably provided residents extra time to get ready for the catastrophe, possibly saving people and assets.
Google’s model operates through identifying trends that conventional lengthy physics-based prediction systems may overlook.
“They do it much more quickly than their physics-based cousins, and the processing requirements is more affordable and demanding,” said Michael Lowry, a ex forecaster.
“What this hurricane season has proven in quick time is that the newcomer artificial intelligence systems are on par with and, in some cases, superior than the less rapid physics-based weather models we’ve traditionally leaned on,” Lowry said.
To be sure, the system is an example of AI training – a method that has been employed in data-heavy sciences like weather science for years – and is distinct from generative AI like ChatGPT.
Machine learning takes mounds of data and extracts trends from them in a such a way that its system only requires minutes to come up with an result, and can do so on a standard PC – in sharp difference to the primary systems that governments have utilized for years that can take hours to run and need some of the biggest high-performance systems in the world.
Nevertheless, the reality that the AI could outperform earlier top-tier traditional systems so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the world’s strongest storms.
“I’m impressed,” commented James Franklin, a former forecaster. “The sample is now large enough that it’s pretty clear this is not a case of beginner’s luck.”
Franklin noted that although the AI is outperforming all other models on forecasting the trajectory of hurricanes globally this year, like many AI models it sometimes errs on extreme strength predictions wrong. It struggled with Hurricane Erin previously, as it was also undergoing rapid intensification to category 5 above the Caribbean.
In the coming offseason, Franklin stated he intends to discuss with Google about how it can enhance the DeepMind output more useful for forecasters by providing additional under-the-hood data they can use to evaluate exactly why it is coming up with its conclusions.
“The one thing that nags at me is that although these predictions seem to be really, really good, the results of the model is kind of a black box,” remarked Franklin.
There has never been a commercial entity that has produced a top-level forecasting system which grants experts a view of its methods – in contrast to most other models which are provided at no cost to the general audience in their full form by the governments that designed and maintain them.
The company is not the only one in starting to use AI to address difficult meteorological problems. The authorities also have their own AI weather models in the works – which have demonstrated improved skill over earlier non-AI versions.
The next steps in AI weather forecasts appear to involve new firms taking swings at previously tough-to-solve problems such as long-range forecasts and better early alerts of severe weather and flash flooding – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is even launching its own atmospheric sensors to address deficiencies in the US weather-observing network.
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