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

As the primary meteorologist on duty, he predicted that in just 24 hours the weather system would become a category 4 hurricane and begin a turn towards the coast of Jamaica. No forecaster had ever issued this confident forecast for quick intensification.

But, Papin possessed a secret advantage: 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 evolved into a system of remarkable power that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his confidence: “Approximately 40/50 Google DeepMind ensemble members show Melissa becoming a Category 5 storm. While I am unprepared to predict that strength at this time due to track uncertainty, that is still plausible.

“There is a high probability that a period of rapid intensification will occur as the system drifts over very warm ocean waters which is the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Models

Google DeepMind is the first artificial intelligence system dedicated to hurricanes, and currently the first to beat traditional weather forecasters at their specialty. Across all tropical systems so far this year, Google’s model is top-performing – surpassing human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at category 5 strength, among the most powerful coastal impacts recorded in nearly two centuries of record-keeping across the region. Papin’s bold forecast likely gave people in Jamaica extra time to get ready for the catastrophe, potentially preserving lives and property.

How Google’s Model Functions

Google’s model works by spotting patterns that traditional time-intensive scientific prediction systems may miss.

“The AI performs much more quickly than their traditional counterparts, and the computing power is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“This season’s events has proven in quick time is that the newcomer AI weather models are competitive with and, in some cases, superior than the less rapid physics-based weather models we’ve traditionally leaned on,” he added.

Understanding AI Technology

To be sure, Google DeepMind is an instance of machine learning – a method that has been used in research fields like weather science for years – and is not generative AI like ChatGPT.

AI training takes large datasets and extracts trends from them in a manner that its model only requires minutes to generate an result, and can operate on a standard PC – in sharp difference to the flagship models that authorities have used for years that can require many hours to run and require some of the biggest supercomputers in the world.

Expert Reactions and Upcoming Advances

Still, the reality that Google’s model could outperform earlier top-tier traditional systems so quickly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the most intense weather systems.

“It’s astonishing,” commented James Franklin, a former expert. “The data is now large enough that it’s evident this is not just beginner’s luck.”

Franklin said that although Google DeepMind is outperforming all competing systems on predicting the trajectory of hurricanes worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty with another storm earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, he stated he intends to discuss with Google about how it can make the DeepMind output more useful for forecasters by offering extra internal information they can utilize to assess the reasons it is producing its answers.

“A key concern that troubles me is that while these forecasts seem to be highly accurate, the results of the model is essentially a black box,” remarked Franklin.

Broader Sector Developments

There has never been a commercial entity that has produced a top-level weather model which grants experts a peek into its methods – unlike most other models which are provided free to the general audience in their full form by the governments that created and operate them.

The company is not the only one in adopting AI to solve difficult weather forecasting problems. The authorities also have their respective AI weather models in the works – which have also shown improved skill over previous non-AI versions.

Future developments in AI weather forecasts appear to involve new firms taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and better early alerts of severe weather and sudden deluges – and they have secured US government funding to do so. One company, WindBorne Systems, is also launching its proprietary weather balloons to fill the gaps in the national monitoring system.