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As Developing Cyclone Melissa was churning off the coast 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 a single day the storm would intensify into a category 4 hurricane and start shifting towards the coast of Jamaica. No forecaster had ever issued such a bold prediction for quick intensification.

However, Papin possessed a secret advantage: AI technology in the guise of Google’s new DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Growing Reliance on AI Predictions

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin explained in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 Google DeepMind ensemble members show Melissa reaching a most intense storm. Although I am unprepared to predict that intensity yet due to path variability, that remains a possibility.

“It appears likely that a period of rapid intensification will occur as the system drifts over exceptionally hot ocean waters which is the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the first AI model dedicated to hurricanes, and currently the initial to outperform standard weather forecasters at their own game. Through all 13 Atlantic storms so far this year, the AI is the best – even beating human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 intensity, among the most powerful landfalls recorded in almost 200 years of data collection across the region. The confident prediction probably provided residents extra time to prepare for the catastrophe, possibly saving people and assets.

The Way The Model Functions

Google’s model works by identifying trends that conventional time-intensive physics-based prediction systems may overlook.

“The AI performs much more quickly than their traditional counterparts, and the processing requirements is less expensive and demanding,” said Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in quick time is that the recent AI weather models are competitive with and, in some cases, superior than the slower physics-based weather models we’ve relied upon,” Lowry said.

Clarifying AI Technology

To be sure, Google DeepMind is an example of machine learning – a method that has been used in data-heavy sciences like meteorology for years – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a such a way that its model only requires minutes to generate an result, and can do so on a desktop computer – in sharp difference to the flagship models that authorities have utilized for years that can take hours to run and need some of the biggest high-performance systems in the world.

Professional Reactions and Upcoming Advances

Nevertheless, the fact that the AI could exceed earlier top-tier traditional systems so rapidly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a former forecaster. “The data is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

He noted that while Google DeepMind is beating all other models on forecasting the future path of storms worldwide this year, similar to other systems it occasionally gets extreme strength forecasts wrong. It had difficulty with another storm previously, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, he stated he intends to talk with the company about how it can enhance the AI results even more helpful for forecasters by providing extra internal information they can use to assess the reasons it is producing its answers.

“The one thing that nags at me is that although these forecasts appear highly accurate, the results of the model is kind of a opaque process,” said Franklin.

Broader Industry Trends

There has never been a private, for-profit company that has developed a top-level weather model which grants experts a view of its techniques – in contrast to most other models which are provided at no cost to the general audience in their entirety by the authorities that created and operate them.

The company is not alone in starting to use artificial intelligence to address difficult meteorological problems. The US and European governments also have their own artificial intelligence systems in the works – which have demonstrated improved skill over previous non-AI versions.

Future developments in AI weather forecasts seem to be new firms tackling previously tough-to-solve problems such as long-range forecasts and better early alerts of severe weather and flash flooding – and they are receiving federal support to do so. One company, WindBorne Systems, is also launching its own weather balloons to address deficiencies in the US weather-observing network.