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As Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a major tropical system.

Serving as lead forecaster on duty, he predicted that in a single day the storm would intensify into a severe hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had ever issued this confident prediction for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s new DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa did become a system of astonishing strength that tore through Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his certainty: “Roughly 40/50 AI ensemble members indicate Melissa reaching a Category 5 hurricane. Although I am unprepared to predict that strength at this time given track uncertainty, that is still plausible.

“It appears likely that a period of quick strengthening will occur as the storm drifts over very warm sea temperatures which is the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

The AI model is the pioneer AI model focused on tropical cyclones, and currently the initial to outperform standard meteorological experts at their own game. Through all tropical systems this season, the AI is top-performing – surpassing experts on path forecasts.

Melissa ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls recorded in almost 200 years of data collection across the Atlantic basin. The confident prediction probably provided residents additional preparation time to get ready for the disaster, possibly saving people and assets.

The Way The Model Works

The AI system operates through spotting patterns 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 time consuming,” stated Michael Lowry, a former meteorologist.

“This season’s events has proven in short order is that the recent artificial intelligence systems are on par with and, in some cases, more accurate than the less rapid traditional forecasting tools we’ve traditionally leaned on,” Lowry said.

Clarifying AI Technology

To be sure, Google DeepMind is an example of AI training – a method that has been used in data-heavy sciences like weather science for a long time – and is distinct from generative AI like ChatGPT.

AI training processes large datasets 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 desktop computer – in strong contrast to the flagship models that governments have utilized for years that can require many hours to run and need some of the biggest high-performance systems in the world.

Professional Reactions and Future Advances

Nevertheless, the reality that Google’s model could outperform earlier gold-standard traditional systems so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the most intense weather systems.

“It’s astonishing,” said James Franklin, a former forecaster. “The data is sufficient that it’s evident this is not just chance.”

Franklin noted that while the AI is beating all competing systems on predicting the future path of storms worldwide this year, like many AI models it sometimes errs on extreme strength forecasts wrong. It struggled with another storm earlier this year, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

In the coming offseason, he stated he plans to discuss with the company about how it can make the AI results more useful for forecasters by providing extra under-the-hood data they can use to evaluate the reasons it is producing its conclusions.

“The one thing that troubles me is that while these forecasts seem to be really, really good, the output of the model is essentially a black box,” remarked Franklin.

Wider Sector Trends

There has never been a private, for-profit company that has produced a high-performance forecasting system which grants experts a peek into its methods – unlike most other models which are provided at no cost to the public in their entirety by the governments that created and operate them.

The company is not the only one in starting to use AI to solve difficult meteorological problems. The authorities also have their own AI weather models in the development phase – which have also shown better performance over earlier traditional systems.

Future developments in AI weather forecasts seem to be startup companies taking swings at previously tough-to-solve problems such as long-range forecasts and better early alerts of tornado outbreaks and flash flooding – and they have secured US government funding to pursue this. One company, WindBorne Systems, is even launching its proprietary weather balloons to fill the gaps in the national monitoring system.