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When Tropical Storm Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in a single day the weather system would intensify into a category 4 hurricane and start shifting towards the coast of Jamaica. No forecaster had ever issued such a bold forecast for quick intensification.

But, Papin possessed a secret advantage: AI technology in the form of Google’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa did become a system of astonishing strength that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his confidence: “Approximately 40/50 AI ensemble members indicate Melissa becoming a Category 5 hurricane. Although I am unprepared to predict that strength at this time given path variability, that remains a possibility.

“It appears likely that a period of quick strengthening is expected as the storm moves slowly over very warm sea temperatures which is the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Models

The AI model is the pioneer AI model dedicated to tropical cyclones, and now the initial to outperform standard weather forecasters at their specialty. Through all 13 Atlantic storms so far this year, the AI is top-performing – even beating experts on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest landfalls ever documented in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided residents extra time to prepare for the disaster, possibly saving people and assets.

How The Model Works

The AI system operates through identifying trends that traditional lengthy scientific weather models may overlook.

“They do it far faster than their traditional counterparts, and the computing power is less expensive and time consuming,” said Michael Lowry, a ex forecaster.

“What this hurricane season has proven in short order is that the recent artificial intelligence systems are on par with and, in certain instances, more accurate than the slower traditional forecasting tools we’ve relied upon,” he said.

Clarifying Machine Learning

It’s important to note, the system is an example of machine learning – a method that has been employed in data-heavy sciences like weather science for years – 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 takes a few minutes to generate an answer, and can operate on a desktop computer – in strong contrast to the primary systems that governments have used for decades that can require many hours to process and need the largest high-performance systems in the world.

Expert Responses and Future Developments

Nevertheless, the reality that the AI could exceed earlier gold-standard legacy models so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the world’s strongest storms.

“It’s astonishing,” commented James Franklin, a retired forecaster. “The sample is sufficient that it’s pretty clear this is not just beginner’s luck.”

Franklin noted that although Google DeepMind is beating all other models on predicting the future path of storms globally this year, like many AI models it sometimes errs on high-end intensity forecasts wrong. It struggled with another storm earlier this year, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, he said he intends to discuss with the company about how it can enhance the AI results even more helpful for forecasters by providing extra under-the-hood data they can use to assess the reasons it is producing its conclusions.

“The one thing that nags at me is that although these forecasts appear really, really good, the output of the model is kind of a black box,” said Franklin.

Wider Sector Trends

There has never been a private, for-profit company that has produced a high-performance forecasting system which allows researchers a peek into its methods – in contrast to most other models which are offered free to the public in their entirety by the authorities that created and operate them.

Google is not the only one in starting to use artificial intelligence to address difficult meteorological problems. The US and European governments also have their respective AI weather models in the works – which have also shown improved skill over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies tackling formerly tough-to-solve problems such as long-range forecasts and improved advance warnings of severe weather and flash flooding – and they have secured 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.