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As Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon grow into a major tropical system.

As the primary meteorologist on duty, he predicted that in a single day the weather system would intensify into a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the guise of Google’s new DeepMind cyclone prediction system – launched for the initial occasion in June. True to the forecast, Melissa did become a system of astonishing strength that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his confidence: “Roughly 40/50 Google DeepMind simulation runs show Melissa becoming a Category 5 hurricane. While I am unprepared to predict that strength at this time given track uncertainty, that remains a possibility.

“There is a high probability that a period of quick strengthening will occur as the system drifts over very warm ocean waters which is the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Models

The AI model is the first artificial intelligence system dedicated to tropical cyclones, and now the initial to outperform traditional meteorological experts at their own game. Across all 13 Atlantic storms this season, the AI is the best – surpassing experts on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, among the most powerful landfalls ever documented in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast probably provided residents additional preparation time to prepare for the disaster, possibly saving people and assets.

The Way The System Functions

Google’s model operates through identifying trends that traditional lengthy scientific prediction systems may miss.

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

“What this hurricane season has demonstrated in quick time is that the recent AI weather models are competitive with and, in some cases, more accurate than the less rapid traditional weather models we’ve relied upon,” Lowry said.

Clarifying AI Technology

It’s important to note, Google DeepMind is an example of AI training – a technique that has been employed in data-heavy sciences like weather science for a long time – and is distinct from generative AI like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a such a way that its model only takes a few minutes to come up with an answer, and can do so on a desktop computer – in sharp difference to the flagship models that governments have used for decades that can take hours to process and need the largest high-performance systems in the world.

Professional Responses and Upcoming Developments

Nevertheless, the fact that Google’s model could outperform earlier gold-standard legacy models so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the world’s strongest weather systems.

“I’m impressed,” said James Franklin, a former expert. “The data is now large enough that it’s evident this is not just chance.”

Franklin said that although the AI is beating all other models on predicting the future path of hurricanes globally this year, like many AI models it occasionally gets extreme strength forecasts wrong. It struggled with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

During the next break, Franklin stated he intends to talk with the company about how it can enhance the DeepMind output even more helpful for forecasters by offering extra under-the-hood data they can use to assess exactly why it is producing its answers.

“A key concern that nags at me is that although these predictions seem to be highly accurate, the results of the model is kind of a opaque process,” remarked Franklin.

Wider Industry Developments

There has never been a private, for-profit company that has produced a top-level weather model which grants experts a view of its techniques – unlike nearly all other models which are provided free to the public in their entirety by the governments that designed and maintain them.

The company is not alone in starting to use artificial intelligence to solve challenging weather forecasting problems. The US and European governments also have their own AI weather models in the development phase – which have also shown improved skill over earlier traditional systems.

Future developments in AI weather forecasts seem to be startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and flash flooding – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is also launching its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.