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When Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it would soon grow into a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the weather system would intensify into a category 4 hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had ever issued such a bold prediction for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s new DeepMind hurricane model – released for the initial occasion in June. True to the forecast, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI simulation runs indicate Melissa reaching a Category 5 hurricane. Although I am unprepared to forecast that strength at this time due to path variability, that remains a possibility.

“It appears likely that a phase of rapid intensification is expected as the system drifts over exceptionally hot sea temperatures which is the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Systems

The AI model is the pioneer artificial intelligence system dedicated to tropical cyclones, and now the first to outperform standard weather forecasters at their own game. Through all tropical systems so far this year, Google’s model is the best – even beating experts on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 intensity, among the most powerful landfalls recorded in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica additional preparation time to get ready for the disaster, possibly saving people and assets.

The Way Google’s Model Functions

Google’s model operates through spotting patterns that conventional time-intensive physics-based weather models may overlook.

“They do it much more quickly than their physics-based cousins, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a ex meteorologist.

“This season’s events has proven in short order is that the recent AI weather models are on par with and, in certain instances, superior than the slower traditional forecasting tools we’ve traditionally leaned on,” he added.

Understanding AI Technology

It’s important to note, the system is an example of machine learning – a technique that has been employed in research fields like weather science for a long time – and is not generative AI like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a such a way that its model only requires minutes to generate an answer, and can operate on a desktop computer – in sharp difference to the flagship models that governments have utilized for decades that can take hours to run and require the largest supercomputers in the world.

Expert Reactions and Upcoming Developments

Nevertheless, the reality that the AI could exceed earlier top-tier traditional systems so quickly is truly remarkable 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 a case of beginner’s luck.”

Franklin said that although the AI is beating all competing systems on predicting the future path of hurricanes worldwide this year, like many AI models it occasionally gets extreme strength forecasts wrong. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

During the next break, he said he intends to discuss with Google about how it can enhance the DeepMind output more useful for forecasters by offering additional internal information they can use to evaluate exactly why it is producing its answers.

“The one thing that troubles me is that although these predictions seem to be really, really good, the results of the system is essentially a opaque process,” said Franklin.

Wider Sector Developments

There has never been a private, for-profit company that has produced a high-performance forecasting system which grants experts a peek into its methods – in contrast to most systems which are provided at no cost to the general audience in their entirety by the governments that designed and maintain them.

Google is not the only one in adopting AI to address difficult meteorological problems. The US and European governments are developing their respective AI weather models in the development phase – which have demonstrated improved skill over previous traditional systems.

The next steps in AI weather forecasts seem to be new firms taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and flash flooding – and they have secured federal support to do so. One company, WindBorne Systems, is also launching its proprietary atmospheric sensors to address deficiencies in the national monitoring system.