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

As the lead forecaster on duty, he forecasted that in just 24 hours the weather system 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 possessed a secret advantage: AI technology in the form of Google’s recently introduced DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Meteorologists are heavily relying upon Google DeepMind. During 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 most intense hurricane. While I am not ready to forecast that intensity yet due to track uncertainty, that remains a possibility.

“There is a high probability that a period of rapid intensification will occur as the storm moves slowly over exceptionally hot ocean waters which represent the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Models

The AI model is the first AI model dedicated to tropical cyclones, and currently the first to outperform standard weather forecasters at their own game. Across all 13 Atlantic storms this season, Google’s model is top-performing – surpassing human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 strength, among the most powerful coastal impacts recorded in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica additional preparation time to prepare for the disaster, potentially preserving people and assets.

How Google’s Model Functions

The AI system operates through spotting patterns that conventional lengthy physics-based weather models may overlook.

“The AI performs far faster than their traditional counterparts, and the computing power is less expensive and demanding,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in short order is that the recent AI weather models are competitive with and, in certain instances, more accurate than the less rapid traditional forecasting tools we’ve traditionally leaned on,” he added.

Understanding AI Technology

To be sure, Google DeepMind is an example of AI training – a method that has been used in research fields like meteorology for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a manner that its model only requires minutes to generate an result, and can do so on a standard PC – in sharp difference to the primary systems that governments have utilized for years that can require many hours to process and require the largest high-performance systems in the world.

Expert Reactions and Future Advances

Still, the fact that the AI could outperform previous gold-standard legacy models so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the most intense storms.

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

Franklin said that although the AI is outperforming all competing systems on forecasting the future path of storms globally this year, like many AI models it occasionally gets extreme strength predictions wrong. It had difficulty with another storm previously, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

During the next break, he said he intends to talk with Google about how it can enhance the AI results more useful for experts by providing extra internal information they can use to assess the reasons it is producing its conclusions.

“The one thing that nags at me is that although these forecasts seem to be highly accurate, the results of the model is kind of a black box,” said Franklin.

Wider Sector Trends

There has never been a commercial entity that has produced a top-level weather model which grants experts a view of its techniques – in contrast to most other models which are offered free to the public in their entirety by the authorities that designed and maintain them.

Google is not alone in adopting artificial intelligence to solve challenging meteorological problems. The US and European governments are developing their own artificial intelligence systems in the development phase – which have also shown improved skill over previous traditional systems.

The next steps in AI weather forecasts appear to involve startup companies taking swings at formerly difficult problems such as sub-seasonal outlooks and improved advance warnings of severe weather and flash flooding – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also launching its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.