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As Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a major tropical system.

Serving as lead forecaster 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 ever issued this confident prediction for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s recently introduced DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Increasing Reliance on AI Forecasting

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his certainty: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa becoming a Category 5 storm. While I am unprepared to forecast that intensity yet due to track uncertainty, that is still plausible.

“It appears likely that a period of rapid intensification is expected as the storm drifts over very warm sea temperatures which is the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first artificial intelligence system focused on hurricanes, and currently the first to beat standard weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, the AI is the best – even beating human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 strength, one of the strongest landfalls ever documented in nearly two centuries of record-keeping across the region. Papin’s bold forecast likely gave residents additional preparation time to get ready for the disaster, potentially preserving people and assets.

How The System Functions

Google’s model operates through identifying trends that traditional time-intensive scientific weather models may overlook.

“The AI performs far faster than their physics-based cousins, and the processing requirements is less expensive and time consuming,” stated Michael Lowry, a former forecaster.

“This season’s events has proven in quick time is that the recent AI weather models are on par with and, in some cases, superior than the less rapid physics-based forecasting tools we’ve relied upon,” Lowry said.

Clarifying Machine Learning

To be sure, the system is an example of machine learning – a technique that has been used in research fields like meteorology for years – and is distinct from generative AI like ChatGPT.

Machine learning takes large datasets and pulls out patterns from them in a manner that its model only requires minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the primary systems that authorities have used for years that can require many hours to run and need some of the biggest high-performance systems in the world.

Expert Reactions and Upcoming Advances

Nevertheless, the fact that Google’s model could exceed earlier top-tier legacy models so quickly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the most intense weather systems.

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

Franklin said that although Google DeepMind is outperforming all competing systems on forecasting the future path of hurricanes globally this year, like many AI models it sometimes errs on extreme strength forecasts inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

In the coming offseason, Franklin said he plans to talk with Google about how it can make the AI results more useful for forecasters by providing additional under-the-hood data they can use to evaluate exactly why it is coming up with its answers.

“A key concern that nags at me is that while these forecasts appear really, really good, the results of the model is kind of a opaque process,” said Franklin.

Wider Industry Developments

There has never been a private, for-profit company 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 free to the public in their entirety by the authorities that created and operate them.

Google is not alone in adopting artificial intelligence to solve challenging weather forecasting problems. The authorities are developing their own AI weather models in the development phase – which have also shown better performance over previous traditional systems.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at previously difficult problems such as long-range forecasts and improved early alerts of severe weather and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also launching its own atmospheric sensors to fill the gaps in the US weather-observing network.