🔗 Share this article

As Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it was about to grow into a monster hurricane.

As the primary meteorologist on duty, he forecasted that in a single day the weather system would become a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had previously made such a bold forecast for quick intensification.

But, Papin possessed a secret advantage: AI technology in the form of Google’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa evolved into a system of remarkable power that ravaged Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his certainty: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa reaching a most intense hurricane. While I am not ready 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 exceptionally hot ocean waters which represent the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Systems

The AI model is the first artificial intelligence system focused on hurricanes, and currently the first to outperform standard weather forecasters at their specialty. Across all tropical systems so far this year, Google’s model is top-performing – even beating human forecasters on track predictions.

Melissa ultimately struck in Jamaica at category 5 strength, 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 prepare for the disaster, potentially preserving lives and property.

The Way Google’s System Functions

The AI system operates through identifying trends that traditional time-intensive physics-based weather models may miss.

“They do it far faster than their traditional counterparts, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a former forecaster.

“This season’s events has proven in short order is that the newcomer artificial intelligence systems are on par with and, in certain instances, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” he added.

Understanding AI Technology

It’s important to note, Google DeepMind is an instance of AI training – a technique that has been employed in research fields like meteorology for a long time – and is distinct from 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 come up with an answer, and can do so on a standard PC – in sharp difference to the primary systems that authorities have utilized for years that can require many hours to process and need the largest high-performance systems in the world.

Professional Responses and Upcoming Advances

Nevertheless, the reality that Google’s model could outperform earlier top-tier legacy models so quickly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the world’s strongest weather systems.

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

He said that although the AI is beating all competing systems on forecasting the trajectory of storms globally this year, like many AI models it sometimes errs on high-end intensity forecasts wrong. It had difficulty with another storm previously, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he stated he intends to discuss with the company about how it can make the AI results more useful for forecasters 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 troubles me is that although these predictions seem to be really, really good, the output of the model is kind of a opaque process,” remarked Franklin.

Broader Sector Developments

There has never been a commercial entity that has developed a high-performance weather model which grants experts a peek into its methods – in contrast to most other models which are offered free to the public in their entirety by the governments that created and operate them.

The company is not alone in adopting AI to solve difficult meteorological problems. The US and European governments are developing their respective artificial intelligence systems in the development phase – which have demonstrated improved skill over previous traditional systems.

The next steps in artificial intelligence predictions seem to be new firms taking swings at previously tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and sudden deluges – and they have secured US government funding to pursue this. One company, WindBorne Systems, is also launching its proprietary weather balloons to fill the gaps in the national monitoring system.