🔗 Share this article

As Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a major tropical system.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the storm would become a severe hurricane and start shifting towards the coast of Jamaica. Not a single expert had previously made such a bold prediction for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the form of Google’s new DeepMind hurricane model – launched for the initial occasion in June. True to the forecast, Melissa did become a storm of remarkable power that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his certainty: “Roughly 40/50 AI simulation runs show Melissa becoming a Category 5 storm. Although I am unprepared to forecast that intensity yet given path variability, that remains a possibility.

“It appears likely that a phase of rapid intensification will occur as the system moves slowly over very warm sea temperatures which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Traditional Models

Google DeepMind is the first artificial intelligence system focused on tropical cyclones, and currently the initial to beat traditional weather forecasters at their specialty. Across all tropical systems so far this year, the AI is the best – surpassing human forecasters on track predictions.

Melissa ultimately struck in Jamaica at maximum strength, one of the strongest coastal impacts ever documented in nearly two centuries of data collection 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

The AI system operates through spotting patterns that traditional time-intensive physics-based weather models may overlook.

“The AI performs much more quickly than their traditional counterparts, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“What this hurricane season has proven in quick time is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the slower physics-based forecasting tools we’ve relied upon,” he said.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an instance of AI training – a technique that has been used in research fields like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to come up with an answer, and can do so on a standard PC – in strong contrast to the primary systems that governments have utilized for years that can require many hours to run and need the largest supercomputers in the world.

Expert Responses and Upcoming Developments

Nevertheless, the reality that the AI could outperform earlier gold-standard traditional systems so quickly is nothing short of amazing to meteorologists who have spent their careers trying to predict the most intense weather systems.

“It’s astonishing,” commented James Franklin, a former forecaster. “The sample is now large enough that it’s evident this is not just chance.”

He said that although the AI is outperforming all competing systems on forecasting the trajectory of storms globally this year, like many AI models it sometimes errs on extreme strength predictions wrong. It had difficulty with another storm earlier this year, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, Franklin said he plans to talk with Google about how it can enhance the DeepMind output even more helpful for forecasters by offering additional internal information they can use to assess the reasons it is coming up with its answers.

“A key concern that nags at me is that while these predictions appear highly accurate, the results of the system is kind of a black box,” said Franklin.

Wider Industry Trends

Historically, no a private, for-profit company that has produced a high-performance forecasting system which allows researchers a peek into its methods – unlike most other models which are offered free to the public in their entirety by the authorities that designed and maintain them.

Google is not the only one in starting to use AI to address challenging weather forecasting problems. The authorities also have their own artificial intelligence systems in the works – which have demonstrated better performance over earlier traditional systems.

Future developments in artificial intelligence predictions seem to be new firms taking swings at formerly difficult problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and sudden deluges – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is also deploying its proprietary weather balloons to fill the gaps in the national monitoring system.