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When Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it would soon grow into a monster hurricane.

As the lead forecaster on duty, he predicted that in just 24 hours the weather system would become a category 4 hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made such a bold prediction for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the form of Google’s recently introduced DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa did become a system of remarkable power that ravaged Jamaica.

Increasing Dependence on AI Predictions

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that Google’s model was a key factor for his certainty: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa becoming a Category 5 hurricane. While I am unprepared to forecast that strength at this time given track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening is expected as the system moves slowly over exceptionally hot sea temperatures which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Traditional Systems

The AI model is the first artificial intelligence system dedicated to hurricanes, and currently the first to outperform standard weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, Google’s model is the best – surpassing experts on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum strength, one of the strongest landfalls ever documented in almost 200 years of record-keeping across the region. The confident prediction probably provided people in Jamaica extra time to prepare for the catastrophe, possibly saving lives and property.

How The Model Functions

The AI system works by identifying trends that conventional lengthy physics-based prediction systems may overlook.

“The AI performs far faster than their traditional counterparts, and the processing requirements is less expensive and time consuming,” stated 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 certain instances, superior than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” Lowry said.

Clarifying AI Technology

It’s important to note, the system is an instance of AI training – a method that has been employed in data-heavy sciences like meteorology for years – and is distinct from generative AI like ChatGPT.

AI training processes mounds of data and extracts trends from them in a such a way that its model only takes a few minutes to generate an answer, and can operate on a desktop computer – in strong contrast to the flagship models that governments have utilized for years that can require many hours to process and require the largest supercomputers in the world.

Professional Reactions and Future Developments

Nevertheless, the fact that the AI could exceed earlier top-tier legacy models so quickly is truly remarkable to weather scientists who have spent their careers trying to forecast the most intense storms.

“It’s astonishing,” said James Franklin, a retired forecaster. “The sample is sufficient that it’s pretty clear this is not just beginner’s luck.”

Franklin said that although the AI is beating all competing systems on forecasting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. It struggled with Hurricane Erin previously, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

During the next break, Franklin said he plans to talk with Google about how it can enhance the DeepMind output even more helpful for forecasters by providing additional under-the-hood data they can use to assess exactly why it is producing its conclusions.

“The one thing that troubles me is that while these forecasts appear really, really good, the results of the model is kind of a black box,” remarked Franklin.

Wider Sector Developments

There has never been a commercial entity that has developed a top-level forecasting system which allows researchers a view of its techniques – unlike nearly all systems which are provided at no cost to the general audience in their entirety by the governments that created and operate them.

Google is not the only one in adopting AI to solve challenging weather forecasting problems. The US and European governments are developing their respective artificial intelligence systems in the works – which have demonstrated improved skill over earlier traditional systems.

Future developments in AI weather forecasts appear to involve new firms taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of severe weather and sudden deluges – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is even deploying its own weather balloons to fill the gaps in the US weather-observing network.