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When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in a single day the storm would become a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had ever issued such a bold prediction for quick intensification.

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

Growing Dependence on AI Predictions

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 AI simulation runs indicate Melissa reaching a Category 5 hurricane. While I am unprepared to forecast that intensity yet due to track uncertainty, that remains a possibility.

“It appears likely that a period of rapid intensification is expected as the system moves slowly over exceptionally hot ocean waters which is the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first artificial intelligence system dedicated to tropical cyclones, and currently the initial to beat traditional meteorological experts at their specialty. Through all 13 Atlantic storms so far this year, Google’s model is the best – surpassing human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls ever documented in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave residents extra time to prepare for the disaster, potentially preserving people and assets.

The Way Google’s System Works

The AI system works by identifying trends that conventional time-intensive physics-based prediction systems may miss.

“They do it much more quickly than their traditional counterparts, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in short order is that the newcomer artificial intelligence systems are competitive with and, in some cases, more accurate than the less rapid physics-based forecasting tools we’ve relied upon,” he said.

Clarifying AI Technology

It’s important to note, Google DeepMind is an example of machine learning – a method that has been used in data-heavy sciences like weather science for years – and is distinct from generative AI like ChatGPT.

AI training processes mounds of data and extracts trends from them in a manner that its system only requires minutes to generate an result, and can do so on a desktop computer – in sharp difference to the flagship models that governments have utilized for decades that can require many hours to run and require some of the biggest high-performance systems in the world.

Expert Responses and Upcoming Developments

Still, the fact that the AI could outperform previous top-tier legacy models so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to predict the most intense weather systems.

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

Franklin said that while the AI is beating all competing systems on forecasting the future path of storms globally this year, similar to other systems it occasionally gets high-end intensity predictions wrong. It had difficulty with another storm previously, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he stated he plans to talk with the company about how it can make the DeepMind output more useful for forecasters by providing extra internal information they can utilize to assess the reasons it is producing its conclusions.

“The one thing that troubles me is that although these predictions seem to be highly accurate, the output of the system is kind of a black box,” remarked Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has developed a top-level weather model which allows researchers a peek into its methods – unlike nearly all other models which are offered at no cost to the general audience in their entirety by the authorities that created and operate them.

The company is not alone in adopting artificial intelligence to solve challenging weather forecasting problems. The authorities also have their own AI weather models in the works – which have demonstrated improved skill over previous non-AI versions.

The next steps in artificial intelligence predictions appear to involve startup companies tackling formerly tough-to-solve problems such as long-range forecasts and improved early alerts of severe weather and sudden deluges – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is also deploying its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.