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When Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane.

As the lead forecaster on duty, he predicted that in a single day the weather system would intensify into a severe hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had previously made this confident forecast for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s new DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa did become a system of astonishing strength that ravaged Jamaica.

Increasing Reliance on AI Forecasting

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his confidence: “Approximately 40/50 Google DeepMind simulation runs show Melissa becoming a Category 5 storm. While I am not ready to forecast that intensity at this time due to track uncertainty, that remains a possibility.

“There is a high probability that a period of rapid intensification will occur as the storm moves slowly over exceptionally hot sea temperatures which is the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first AI model focused on hurricanes, and currently the initial to beat traditional weather forecasters at their specialty. Across all 13 Atlantic storms this season, Google’s model is top-performing – surpassing human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at maximum strength, one of the strongest coastal impacts ever documented in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction likely gave residents extra time to get ready for the catastrophe, potentially preserving lives and property.

The Way The System Functions

Google’s model operates through spotting patterns that conventional lengthy scientific weather models may miss.

“They do it much more quickly than their traditional counterparts, and the processing requirements is less expensive and demanding,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in short order is that the recent AI weather models are on par with and, in some cases, superior than the less rapid traditional forecasting tools we’ve relied upon,” Lowry added.

Clarifying AI Technology

It’s important to note, the system is an instance of AI training – a method that has been used in research fields like weather science for a long time – and is not generative AI like ChatGPT.

AI training processes mounds of data and extracts trends from them in a such a way that its model only takes a few minutes to generate an result, and can operate on a standard PC – in strong contrast to the primary systems that governments have used for decades that can require many hours to process and require some of the biggest supercomputers in the world.

Professional Reactions and Future Advances

Still, the fact that Google’s model could exceed previous gold-standard legacy models so quickly is nothing short of amazing to meteorologists who have spent their careers trying to predict the world’s strongest weather systems.

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

Franklin noted that although the AI is beating all other models on forecasting the trajectory of hurricanes worldwide this year, similar to other systems it occasionally gets high-end intensity forecasts wrong. It had difficulty with Hurricane Erin previously, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

In the coming offseason, he stated he plans to talk with the company about how it can make the DeepMind output more useful for forecasters by offering extra internal information they can utilize to evaluate exactly why it is producing its conclusions.

“A key concern that troubles me is that although these forecasts seem to be highly accurate, the output of the model is kind of a opaque process,” said Franklin.

Broader Sector Trends

There has never been a private, for-profit company that has developed a high-performance forecasting system which allows researchers a view of its techniques – in contrast to most other models which are provided free to the general audience in their entirety by the governments that designed and maintain them.

The company is not the only one in adopting AI to solve challenging weather forecasting problems. The authorities are developing their own AI weather models in the development phase – which have demonstrated improved skill over earlier non-AI versions.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of severe weather and sudden deluges – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is even deploying its own weather balloons to address deficiencies in the national monitoring system.