🔗 Share this article

When Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a major tropical system.

As the lead forecaster on duty, he predicted that in just 24 hours the storm would intensify into a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had ever issued this confident forecast for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s new DeepMind cyclone prediction system – launched for the initial occasion in June. True to the forecast, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Meteorologists are heavily relying upon the AI system. During 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his certainty: “Approximately 40/50 AI simulation runs indicate Melissa becoming a Category 5 hurricane. While I am unprepared to predict that intensity at this time due to path variability, that is still plausible.

“There is a high probability that a period of quick strengthening is expected as the storm drifts over very warm ocean waters which is the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the pioneer AI model dedicated to hurricanes, and currently the first to beat traditional weather forecasters at their specialty. Across all tropical systems this season, the AI is the best – even beating experts on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum intensity, among the most powerful landfalls recorded in almost 200 years of record-keeping across the Atlantic basin. The confident prediction probably provided people in Jamaica additional preparation time to get ready for the disaster, potentially preserving lives and property.

The Way Google’s Model Functions

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

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a ex meteorologist.

“This season’s events has proven in short order is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the slower physics-based weather models we’ve traditionally leaned on,” he added.

Clarifying AI Technology

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

AI training processes large datasets and pulls out patterns 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 strong contrast to the flagship models that authorities have used for decades that can take hours to process and require some of the biggest high-performance systems in the world.

Professional Responses and Future Advances

Still, the reality that the AI could outperform earlier gold-standard 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,” said James Franklin, a retired expert. “The sample is sufficient that it’s pretty clear this is not a case of chance.”

Franklin said that while the AI is outperforming all competing systems on predicting the trajectory of hurricanes worldwide this year, like many AI models it occasionally gets extreme strength predictions wrong. It had difficulty with another storm earlier this year, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, Franklin stated he intends to talk with the company about how it can make the AI results more useful for experts by offering extra under-the-hood data they can use to evaluate the reasons it is coming up with its conclusions.

“A key concern that nags at me is that while these forecasts appear highly accurate, the results of the system is kind of a black box,” remarked Franklin.

Broader Industry Developments

Historically, no a private, for-profit company that has developed a top-level forecasting system which grants experts a peek into its methods – unlike most systems which are offered at no cost to the public in their full form by the governments that created and operate them.

The company is not alone in adopting artificial intelligence to solve difficult meteorological problems. The authorities also have their own artificial intelligence systems in the development phase – which have demonstrated better performance over previous non-AI versions.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of severe weather and flash flooding – and they have secured federal support to do so. One company, WindBorne Systems, is also launching its own weather balloons to address deficiencies in the national monitoring system.