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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.

As the lead forecaster on duty, he forecasted that in a single day the weather system would intensify into a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. No forecaster had ever issued such a bold prediction for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – released for the initial occasion 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. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his confidence: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa becoming a Category 5 storm. Although I am not ready to predict that strength at this time due to path variability, that is still plausible.

“It appears likely that a phase of rapid intensification will occur as the system moves slowly over exceptionally hot sea temperatures which is the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Systems

The AI model is the pioneer artificial intelligence system dedicated to tropical cyclones, and now the first to outperform traditional meteorological experts at their specialty. Through all tropical systems this season, the AI is the best – surpassing experts on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 strength, one of the strongest coastal impacts ever documented in almost 200 years of data collection across the region. Papin’s bold forecast likely gave residents additional preparation time to prepare for the disaster, possibly saving people and assets.

The Way The System Functions

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

“The AI performs far faster than their traditional counterparts, and the computing power is less expensive and time consuming,” stated Michael Lowry, a former meteorologist.

“This season’s events has proven in quick time is that the newcomer AI weather models are competitive with and, in certain instances, more accurate than the slower traditional forecasting tools we’ve relied upon,” he said.

Clarifying AI Technology

It’s important to note, Google DeepMind is an instance of machine learning – a technique that has been used in data-heavy sciences like weather science for years – and is distinct from creative artificial intelligence like ChatGPT.

AI training processes large datasets and extracts trends from them in a manner that its system only requires minutes to come up with an result, and can do so on a standard PC – 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 supercomputers in the world.

Professional Reactions and Upcoming Advances

Still, the reality that Google’s model could outperform earlier gold-standard legacy models so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the most intense storms.

“It’s astonishing,” said James Franklin, a former expert. “The sample is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

He noted that although the AI is outperforming all competing systems on forecasting the future path of storms worldwide this year, similar to other systems it occasionally gets high-end intensity forecasts wrong. It had difficulty with another storm earlier this year, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he said he plans to discuss with Google about how it can make the AI results more useful for experts by offering extra internal information they can utilize to evaluate exactly why it is producing its answers.

“The one thing that nags at me is that although these predictions seem to be highly accurate, the output of the system is essentially a black box,” remarked Franklin.

Broader Industry Trends

Historically, no a private, for-profit company that has produced a high-performance forecasting system which grants experts a peek into its methods – in contrast to most systems which are offered free to the general audience in their entirety by the governments that designed and maintain them.

Google is not alone in starting to use AI to solve difficult meteorological problems. The authorities also have their own artificial intelligence systems in the works – which have also shown improved skill over earlier non-AI versions.

Future developments in AI weather forecasts appear to involve startup companies taking swings at previously difficult problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to do so. One company, WindBorne Systems, is also deploying its proprietary weather balloons to fill the gaps in the US weather-observing network.