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When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a monster hurricane.

Serving as primary meteorologist on duty, he predicted that in a single day the weather system would become a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued this confident prediction for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s new DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on 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: “Roughly 40/50 AI simulation runs show Melissa reaching a Category 5 storm. While I am unprepared to predict that strength yet due to track uncertainty, that remains a possibility.

“There is a high probability that a period of quick strengthening is expected 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 Systems

Google DeepMind is the first AI model dedicated to tropical cyclones, and currently the first to outperform standard weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, the AI is the best – surpassing human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 strength, one of the strongest coastal impacts recorded in almost 200 years of record-keeping across the region. Papin’s bold forecast probably provided people in Jamaica extra time to prepare for the disaster, potentially preserving people and assets.

How Google’s System Works

Google’s model operates through spotting patterns that conventional lengthy scientific weather models may miss.

“They do it much more quickly than their physics-based cousins, and the computing power is more affordable and time consuming,” stated Michael Lowry, a former forecaster.

“This season’s events has proven in short order is that the newcomer artificial intelligence systems are on par with and, in some cases, more accurate than the less rapid traditional weather models we’ve relied upon,” he said.

Clarifying Machine Learning

To be sure, the system is an instance of AI training – a technique that has been employed in research fields like meteorology for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a manner that its model only takes a few minutes to generate an answer, and can do so on a desktop computer – in sharp difference to the flagship models that authorities have used for decades that can require many hours to run and require the largest supercomputers in the world.

Professional Reactions and Future Advances

Nevertheless, the fact that the AI could outperform earlier top-tier traditional systems so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense storms.

“It’s astonishing,” commented James Franklin, a former forecaster. “The data is now large enough that it’s evident this is not a case of beginner’s luck.”

Franklin said that although Google DeepMind is beating all other models on forecasting the future path of storms worldwide this year, like many AI models it occasionally gets high-end intensity predictions wrong. It had difficulty with Hurricane Erin previously, as it was also undergoing rapid intensification to category 5 above the Caribbean.

During the next break, Franklin stated he intends to talk with the company about how it can make the AI results more useful for forecasters by providing extra internal information they can use to evaluate the reasons it is coming up with its answers.

“The one thing 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.

Broader Industry Trends

There has never been a private, for-profit company that has produced a high-performance weather model which grants experts a peek into its techniques – unlike most systems which are provided free to the public in their full form by the authorities that designed and maintain them.

The company is not alone in starting to use artificial intelligence to solve difficult meteorological problems. The authorities are developing their respective artificial intelligence systems in the works – which have also shown better performance over previous non-AI versions.

The next steps in artificial intelligence predictions appear to involve startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and sudden deluges – and they have secured federal support to pursue this. One company, WindBorne Systems, is even deploying its proprietary atmospheric sensors to fill the gaps in the national monitoring system.