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As Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it would soon grow into a major tropical system.

As the primary meteorologist on duty, he forecasted that in just 24 hours the weather system would become a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. No forecaster had ever issued this confident prediction for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa evolved into a system of remarkable power that tore through Jamaica.

Growing Reliance on Artificial Intelligence Forecasting

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his certainty: “Roughly 40/50 AI ensemble members indicate Melissa reaching a Category 5 storm. Although I am unprepared to forecast that intensity at this time given path variability, that remains a possibility.

“There is a high probability that a period of rapid intensification is expected as the storm moves slowly over very warm ocean waters which represent the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the first artificial intelligence system dedicated to tropical cyclones, and currently the first to outperform standard weather forecasters at their specialty. Across all tropical systems this season, the AI is the best – even beating human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum strength, among the most powerful landfalls recorded in nearly two centuries of record-keeping across the region. The confident prediction likely gave residents additional preparation time to get ready for the catastrophe, possibly saving lives and property.

The Way The System Works

The AI system works by spotting patterns that traditional lengthy physics-based prediction systems may overlook.

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

“What this hurricane season has demonstrated in short order is that the newcomer artificial intelligence systems are on par with and, in certain instances, superior than the slower traditional forecasting tools we’ve traditionally leaned on,” Lowry added.

Understanding AI Technology

To be sure, the system is an example of AI training – a method that has been used in data-heavy sciences like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

AI training takes 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 strong contrast to the flagship models that authorities have utilized for years that can take hours to run and require the largest supercomputers in the world.

Expert Reactions and Future Developments

Still, the reality that Google’s model could outperform previous top-tier legacy models so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense storms.

“It’s astonishing,” commented James Franklin, a former forecaster. “The sample is sufficient that it’s evident this is not just chance.”

He said that while Google DeepMind is outperforming all other models on predicting the future path of storms globally this year, similar to other systems it occasionally gets extreme strength forecasts inaccurate. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing 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 DeepMind output even more helpful for forecasters by providing additional internal information they can utilize to evaluate the reasons it is coming up with its answers.

“A key concern that nags at me is that although these predictions seem to be highly accurate, the results of the system is essentially a black box,” remarked Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has developed a high-performance weather model which allows researchers a peek into its techniques – unlike nearly all systems which are offered free to the general audience in their entirety by the authorities that designed and maintain them.

The company is not alone in adopting artificial intelligence to address difficult weather forecasting problems. The US and European governments are developing their respective AI weather models in the development phase – which have also shown improved skill over earlier non-AI versions.

Future developments in artificial intelligence predictions seem to be startup companies tackling previously tough-to-solve problems such as long-range forecasts and improved early alerts of tornado outbreaks and flash flooding – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also launching its own atmospheric sensors to fill the gaps in the national monitoring system.