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As Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane.

Serving as 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 previously made such a bold prediction for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa did become a storm of remarkable power that tore through Jamaica.

Increasing Reliance on AI Forecasting

Forecasters are heavily relying upon the AI system. During 25 October, Papin explained in his official briefing that Google’s model was a key factor for his confidence: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa becoming a most intense storm. Although I am not ready to predict that strength yet due to path variability, that is still plausible.

“There is a high probability that a phase of rapid intensification is expected as the system moves slowly over very warm ocean waters which is the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

The AI model is the pioneer AI model dedicated to tropical cyclones, and currently the initial to beat standard meteorological experts at their specialty. Across all 13 Atlantic storms this season, Google’s model is top-performing – surpassing experts on track predictions.

Melissa ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls ever documented in almost 200 years of data collection across the region. The confident prediction probably provided people in Jamaica extra time to prepare for the disaster, possibly saving lives and property.

How The System Works

The AI system works by spotting patterns that traditional time-intensive physics-based weather models may overlook.

“They do it much more quickly than their traditional counterparts, and the computing power is more affordable and demanding,” stated Michael Lowry, a former forecaster.

“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 slower traditional forecasting tools we’ve relied upon,” Lowry said.

Understanding AI Technology

It’s important to note, the system is an example of AI training – a technique that has been employed in data-heavy sciences like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training 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 flagship models that governments have used for decades that can require many hours to process and require some of the biggest high-performance systems in the world.

Professional Reactions and Future Developments

Nevertheless, the fact that the AI could exceed earlier gold-standard traditional systems so quickly is truly remarkable to meteorologists who have spent their careers trying to predict the most intense storms.

“I’m impressed,” commented James Franklin, a retired forecaster. “The sample is sufficient that it’s pretty clear this is not a case of chance.”

Franklin noted that while the AI is outperforming all competing systems on forecasting the trajectory of hurricanes worldwide this year, like many AI models it occasionally gets extreme strength predictions wrong. It had difficulty with another storm earlier this year, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, Franklin said he intends to talk with Google about how it can make the DeepMind output even more helpful for experts by providing extra under-the-hood data they can use to assess exactly why it is coming up with its answers.

“A key concern that troubles me is that while these predictions appear really, really good, the results of the model is kind of a black box,” remarked Franklin.

Wider Sector Trends

There has never been a private, for-profit company that has produced a top-level forecasting system which allows researchers a view of its techniques – in contrast to nearly all other models which are provided at no cost to the general audience in their entirety by the governments that created and operate them.

Google is not the only one in starting to use artificial intelligence to solve difficult weather forecasting problems. The authorities also have their own artificial intelligence systems in the works – which have demonstrated better performance over previous traditional systems.

The next steps in artificial intelligence predictions appear to involve new firms taking swings at previously difficult problems such as long-range forecasts and better advance warnings of severe weather and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even deploying its proprietary weather balloons to address deficiencies in the US weather-observing network.