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When Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a monster hurricane.

As the primary meteorologist on duty, he forecasted that in just 24 hours the weather system would become a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had ever issued such a bold forecast for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s recently introduced DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa did become a system of remarkable power that tore through Jamaica.

Increasing Dependence on AI Forecasting

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI ensemble members indicate Melissa reaching a most intense hurricane. Although I am not ready to predict that intensity at this time due to track uncertainty, that remains a possibility.

“There is a high probability that a period of quick strengthening will occur as the system moves slowly over exceptionally hot sea temperatures which represent the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first artificial intelligence system focused on hurricanes, and now the initial to beat traditional weather forecasters at their own game. Across all tropical systems this season, Google’s model is the best – even beating human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at category 5 intensity, one of the strongest landfalls recorded in nearly two centuries of data collection across the Atlantic basin. The confident prediction likely gave people in Jamaica extra time to get ready for the catastrophe, possibly saving lives and property.

How The System Functions

The AI system operates through identifying trends that traditional lengthy scientific prediction systems may overlook.

“They do it far faster than their traditional counterparts, and the computing power is less expensive and time consuming,” stated Michael Lowry, a ex forecaster.

“This season’s events has proven in quick time is that the newcomer artificial intelligence systems are competitive with and, in certain instances, more accurate than the slower traditional forecasting tools we’ve relied upon,” Lowry said.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of AI training – a technique that has been employed in data-heavy sciences like meteorology for a long time – and is distinct from generative AI like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to generate an answer, and can do so on a desktop computer – in strong contrast to the primary systems that governments have utilized for decades that can require many hours to run and need some of the biggest high-performance systems in the world.

Expert Reactions and Future Developments

Still, the reality that the AI could exceed previous gold-standard legacy models so quickly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

“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 the AI is beating all competing systems on predicting the future path of hurricanes worldwide this year, similar to other systems it sometimes errs on extreme strength forecasts wrong. It had difficulty with another storm previously, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

During the next break, Franklin said he intends to discuss with Google about how it can make the AI results more useful for experts by offering extra under-the-hood data they can utilize to evaluate the reasons it is coming up with its answers.

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

Wider Industry Trends

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

Google is not alone in starting to use AI to address difficult weather forecasting problems. The US and European governments are developing their own AI weather models in the works – which have also shown better performance over earlier traditional systems.

The next steps in AI weather forecasts seem to be new firms taking swings at previously tough-to-solve problems such as long-range forecasts and improved advance warnings of severe weather and flash flooding – and they have secured federal support to do so. One company, WindBorne Systems, is even deploying its own weather balloons to address deficiencies in the US weather-observing network.