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When Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a monster hurricane.

As the lead forecaster on duty, he forecasted that in a single day the weather system would intensify into a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had ever issued this confident forecast for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Growing Reliance on AI Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his confidence: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa becoming a most intense hurricane. While I am not ready to predict that strength at this time due to path variability, that is still plausible.

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

Surpassing Conventional Models

The AI model is the pioneer AI model dedicated to hurricanes, and currently the initial to outperform standard weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, the AI is top-performing – surpassing experts on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in nearly two centuries of record-keeping across the region. The confident prediction likely gave residents extra time to prepare for the disaster, potentially preserving people and assets.

The Way Google’s System Functions

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

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

“This season’s events has demonstrated in short order is that the newcomer artificial intelligence systems are competitive with and, in some cases, superior than the slower physics-based forecasting tools we’ve traditionally leaned on,” he said.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an instance of AI training – a technique that has been used in data-heavy sciences like weather science for years – and is distinct from creative artificial intelligence like ChatGPT.

AI training processes large datasets and extracts trends from them in a such a way that its system only requires minutes to generate an answer, and can operate on a standard PC – in sharp difference to the flagship models that authorities have utilized for decades that can require many hours to run and require the largest supercomputers in the world.

Professional Reactions and Future Developments

Still, the fact that the AI could exceed earlier gold-standard traditional systems so rapidly 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 expert. “The sample is now large enough that it’s pretty clear this is not just chance.”

Franklin said that although the AI is outperforming all competing systems on forecasting the future path of hurricanes globally this year, like many AI models it occasionally gets high-end intensity predictions inaccurate. It had difficulty with Hurricane Erin previously, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

During the next break, Franklin said he plans to discuss with the company about how it can enhance the AI results even more helpful for experts by offering extra internal information they can use to assess the reasons it is coming up with its conclusions.

“The one thing that nags at me is that although these forecasts appear really, really good, the results of the model is essentially a black box,” remarked Franklin.

Wider Sector Trends

There has never been a commercial entity that has developed 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 general audience in their entirety by the authorities that designed and maintain them.

The company is not the only one in starting to use artificial intelligence to solve challenging weather forecasting problems. The authorities also have their respective AI weather models in the works – which have also shown better performance over previous non-AI versions.

The next steps in artificial intelligence predictions seem to be startup companies taking swings at previously difficult problems such as long-range forecasts and better advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is even deploying its proprietary weather balloons to address deficiencies in the US weather-observing network.