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As Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a monster hurricane.

Serving as lead forecaster on duty, he predicted that in a single day the storm would become a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had previously made this confident forecast for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the guise of Google’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Growing Dependence on AI Predictions

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI ensemble members show Melissa reaching a Category 5 hurricane. Although I am not ready to forecast that strength at this time due to path variability, that is still plausible.

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

Outperforming Conventional Systems

Google DeepMind is the first artificial intelligence system focused on tropical cyclones, and currently the first to outperform standard weather forecasters at their specialty. Through all tropical systems this season, Google’s model is the best – even beating 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 likely gave residents additional preparation time to get ready for the disaster, potentially preserving people and assets.

How The Model Works

The AI system works by spotting patterns that conventional time-intensive scientific prediction systems may overlook.

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

“This season’s events has proven in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, more accurate than the less rapid physics-based forecasting tools we’ve relied upon,” Lowry said.

Clarifying Machine Learning

It’s important to note, the system is an example of machine learning – a method that has been used in research fields like weather science for years – and is distinct from generative AI like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a such a way that its model only takes a few 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 utilized for years that can take hours to process and require the largest supercomputers in the world.

Professional Reactions and Future Developments

Nevertheless, the reality that the AI could exceed earlier top-tier traditional systems so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to predict the most intense storms.

“I’m impressed,” said James Franklin, a retired expert. “The sample is now large enough that it’s evident this is not a case of chance.”

Franklin said that while Google DeepMind is beating all other models on predicting the trajectory of storms worldwide this year, like many AI models it occasionally gets high-end intensity predictions wrong. It struggled with another storm earlier this year, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

During the next break, he said he intends to discuss with Google about how it can make the AI results more useful for forecasters by providing extra internal information they can utilize to assess the reasons it is producing its answers.

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

Broader Industry Developments

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

The company is not alone in starting to use AI to solve challenging meteorological problems. The US and European governments are developing their own AI weather models in the development phase – which have also shown improved skill over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and better early alerts of tornado outbreaks and sudden deluges – and they have secured federal support to do so. One company, WindBorne Systems, is also deploying its own weather balloons to fill the gaps in the US weather-observing network.