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When Developing Cyclone Melissa was churning south of Haiti, weather expert 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. No forecaster had previously made this confident forecast for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the guise of Google’s recently introduced DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa evolved into a storm 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 the AI tool was a primary reason for his confidence: “Roughly 40/50 Google DeepMind ensemble members show Melissa becoming a most intense storm. Although I am unprepared to predict that strength at this time given track uncertainty, that remains a possibility.

“It appears likely that a phase of rapid intensification is expected as the storm drifts over exceptionally hot ocean waters which is the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first artificial intelligence system dedicated to hurricanes, and currently the first to beat standard weather forecasters at their specialty. Through all 13 Atlantic storms this season, Google’s model is top-performing – even beating experts on path forecasts.

The hurricane ultimately struck in Jamaica at maximum strength, one of the strongest coastal impacts ever documented in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast probably provided residents extra time to prepare for the disaster, potentially preserving lives and property.

The Way Google’s System Works

The AI system works by identifying trends that traditional lengthy scientific prediction systems may overlook.

“They do it much more quickly than their physics-based cousins, and the computing power is more affordable and demanding,” said Michael Lowry, a former meteorologist.

“What this hurricane season has proven in quick time is that the newcomer artificial intelligence systems are on par with and, in some cases, more accurate than the less rapid traditional forecasting tools we’ve relied upon,” he added.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an instance of AI training – a method that has been used in research fields like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes large datasets and pulls out patterns from them in a manner that its model only takes a few minutes to come up with an result, and can operate on a desktop computer – in sharp difference to the primary systems that governments have utilized for years that can require many hours to process and need the largest supercomputers in the world.

Professional Responses and Upcoming Developments

Nevertheless, the reality that Google’s model could exceed earlier gold-standard traditional systems so quickly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the most intense storms.

“I’m impressed,” commented James Franklin, a retired expert. “The data is sufficient that it’s evident this is not a case of beginner’s luck.”

Franklin said that while the AI is outperforming all other models on forecasting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts wrong. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, Franklin said he intends to talk with the company about how it can make the AI results more useful for experts by offering additional internal information they can utilize to assess exactly why it is producing its answers.

“The one thing that nags at me is that while these predictions seem to be highly accurate, the results of the model is kind of a opaque process,” said Franklin.

Wider Industry Developments

Historically, no a commercial entity that has produced a top-level weather model which allows researchers a view of its methods – unlike nearly all systems which are provided at no cost to the public in their entirety by the authorities that designed and maintain them.

The company is not the only one in adopting artificial intelligence to address difficult weather forecasting problems. The US and European governments are developing their respective AI weather models in the development phase – which have demonstrated better performance over previous non-AI versions.

Future developments in AI weather forecasts seem to be new firms tackling formerly tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of severe weather and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even deploying its proprietary weather balloons to fill the gaps in the US weather-observing network.