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When Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a monster hurricane.

As the lead forecaster on duty, he predicted that in just 24 hours the weather system would intensify into a category 4 hurricane and start shifting towards the Jamaican shoreline. No forecaster had previously made such a bold forecast for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s new DeepMind cyclone prediction system – released for the initial occasion in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his certainty: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa reaching a Category 5 hurricane. While I am not ready to predict that strength yet given path variability, that is still plausible.

“It appears likely that a period of quick strengthening is expected as the system moves slowly over very warm sea temperatures which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the pioneer AI model focused on tropical cyclones, and now the first to outperform traditional weather forecasters at their own game. Across all 13 Atlantic storms this season, the AI is top-performing – surpassing human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum strength, one of the strongest landfalls recorded in nearly two centuries of data collection across the region. The confident prediction likely gave people in Jamaica extra time to prepare for the catastrophe, possibly saving lives and property.

The Way The Model Works

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

“They do it much more quickly than their physics-based cousins, and the computing power is more affordable and time consuming,” stated Michael Lowry, a ex forecaster.

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

Understanding AI Technology

It’s important to note, the system is an instance of AI training – a technique that has been employed in data-heavy sciences like meteorology for a long time – and is not generative AI like ChatGPT.

Machine learning takes mounds of data 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 standard PC – in strong contrast to the flagship models that authorities have utilized for decades that can take hours to run and need some of the biggest supercomputers in the world.

Professional Responses and Upcoming Developments

Nevertheless, the reality that the AI could outperform earlier gold-standard legacy models so quickly is truly remarkable to meteorologists who have spent their careers trying to predict the most intense 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 just beginner’s luck.”

Franklin said that although Google DeepMind is beating all other models on predicting the future path of hurricanes globally this year, like many AI models it sometimes errs on high-end intensity forecasts wrong. It had difficulty with another storm earlier this year, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, he stated he plans to talk with Google about how it can make the DeepMind output even more helpful for forecasters by providing extra internal information they can use to assess the reasons it is coming up with its answers.

“A key concern that troubles me is that although these predictions appear highly accurate, the output of the system is essentially a opaque process,” said Franklin.

Broader Industry Trends

Historically, no a commercial entity that has produced a high-performance weather model which grants experts a view of its methods – in contrast to nearly all systems which are offered free to the public in their entirety by the authorities 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 improved skill over previous traditional systems.

Future developments in artificial intelligence predictions seem to be new firms taking swings at formerly difficult problems such as long-range forecasts and improved early alerts of severe weather and sudden deluges – and they have secured US government funding to pursue this. One company, WindBorne Systems, is even launching its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.