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

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the storm would intensify into a category 4 hurricane and start shifting towards the Jamaican shoreline. No forecaster had ever issued this confident prediction for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa evolved into a system of remarkable power that tore through Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his certainty: “Approximately 40/50 AI ensemble members indicate Melissa becoming a most intense storm. While I am unprepared to predict that strength at this time due to track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening will occur as the storm drifts over very warm ocean waters which is the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Models

The AI model is the first artificial intelligence system dedicated to hurricanes, and currently the initial to outperform standard weather forecasters at their own game. Across all tropical systems this season, the AI is the best – surpassing human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 intensity, among the most powerful landfalls ever documented in almost 200 years of data collection across the region. The confident prediction likely gave people in Jamaica extra time to get ready for the catastrophe, potentially preserving lives and property.

How The Model Functions

The AI system operates through identifying trends that traditional time-intensive scientific prediction systems may overlook.

“The AI performs far faster than their traditional counterparts, and the processing requirements is more affordable and demanding,” said Michael Lowry, a former forecaster.

“What this hurricane season has demonstrated in quick time is that the recent AI weather models are on par with and, in certain instances, superior than the less rapid traditional forecasting tools we’ve traditionally leaned on,” Lowry said.

Understanding 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 a long time – and is distinct from generative AI like ChatGPT.

Machine learning processes large datasets and extracts trends from them in a such a way that its system only requires minutes to generate an result, and can operate on a standard PC – in sharp difference to the primary systems that governments have used for years that can require many hours to process and need the largest supercomputers in the world.

Professional Reactions and Upcoming Developments

Still, the fact that the AI could outperform previous top-tier legacy models so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the world’s strongest weather systems.

“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 said that while Google DeepMind is outperforming all other models on predicting the future path of storms globally this year, like many AI models it occasionally gets extreme strength predictions inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

During the next break, Franklin stated he intends to discuss with Google about how it can enhance the DeepMind output more useful for forecasters by providing additional internal information they can utilize to assess exactly why it is producing its answers.

“A key concern that troubles me is that while these forecasts seem to be really, really good, the output of the model is essentially a black box,” said 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 methods – unlike most systems which are offered at no cost to the general audience in their full form by the authorities that designed and maintain them.

The company is not alone in adopting AI to address difficult weather forecasting problems. The US and European governments also have their respective artificial intelligence systems in the development phase – which have demonstrated better performance over previous traditional systems.

The next steps in artificial intelligence predictions appear to involve new firms taking swings at formerly difficult problems such as long-range forecasts and better advance warnings of tornado outbreaks and sudden deluges – and they have secured federal support to do so. A particular firm, WindBorne Systems, is also deploying its proprietary weather balloons to fill the gaps in the US weather-observing network.