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

Serving as lead forecaster on duty, he forecasted that in just 24 hours the storm would intensify into a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had ever issued this confident forecast for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa did become a system of remarkable power that ravaged Jamaica.

Increasing Dependence on AI Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his confidence: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa reaching a Category 5 storm. While I am not ready to predict that strength at this time due to track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening will occur as the storm moves slowly over exceptionally hot sea temperatures which is the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Models

The AI model is the first artificial intelligence system focused on tropical cyclones, and currently the first to beat standard weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, the AI is top-performing – even beating experts on track predictions.

Melissa eventually made landfall in Jamaica at maximum strength, one of the strongest coastal impacts recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction probably provided people in Jamaica additional preparation time to prepare for the catastrophe, potentially preserving lives and property.

How Google’s System Works

The AI system works by spotting patterns that traditional lengthy scientific weather models may miss.

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

“This season’s events has proven in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, more accurate than the slower traditional weather models we’ve traditionally leaned on,” he added.

Understanding AI Technology

It’s important to note, Google DeepMind is an instance 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 processes large datasets and pulls out patterns from them in a manner that its model only requires minutes to generate an answer, and can do so on a standard PC – in strong contrast to the primary systems that authorities have used for years that can take hours to run and need the largest high-performance systems in the world.

Expert Responses and Upcoming Developments

Still, the reality that Google’s model could outperform earlier top-tier legacy models so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to predict the most intense weather systems.

“I’m impressed,” said James Franklin, a former forecaster. “The sample is sufficient that it’s pretty clear this is not just chance.”

Franklin noted that although Google DeepMind is beating all competing systems on forecasting the future path of storms 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 similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, he said he intends to talk with Google about how it can make the AI results even more helpful for experts by offering extra under-the-hood data they can utilize to assess the reasons it is producing its conclusions.

“The one thing that troubles me is that although these forecasts seem to be highly accurate, the output of the model is kind of a black box,” said Franklin.

Broader Industry Trends

There has never been a private, for-profit company that has developed a top-level weather model which grants experts a view of its techniques – unlike nearly all other models which are provided at no cost to the public in their full form by the authorities that created and operate them.

The company is not the only one in starting to use AI to address challenging weather forecasting problems. The authorities also have their own AI weather models in the development phase – which have demonstrated improved skill over previous non-AI versions.

Future developments in artificial intelligence predictions appear to involve startup companies taking swings at previously difficult problems such as long-range forecasts and improved advance warnings of tornado outbreaks and flash flooding – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is even deploying its proprietary atmospheric sensors to fill the gaps in the national monitoring system.