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When Developing Cyclone 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 begin a turn in the direction of the Jamaican shoreline. Not a single expert had previously made such a bold prediction for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s new DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his certainty: “Roughly 40/50 Google DeepMind ensemble members show Melissa reaching a most intense hurricane. While I am unprepared to predict that intensity at this time due to track uncertainty, that remains a possibility.

“It appears likely that a period of rapid intensification is expected as the storm drifts over exceptionally hot ocean waters which is the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the first artificial intelligence system dedicated to tropical cyclones, and now the initial to outperform traditional weather forecasters at their specialty. Across all tropical systems this season, Google’s model is top-performing – surpassing human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, among the most powerful coastal impacts ever documented in nearly two centuries 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.

The Way Google’s System Works

Google’s model works by spotting patterns that traditional time-intensive scientific weather models may overlook.

“They do it much more quickly than their traditional counterparts, and the processing requirements is less expensive and time consuming,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in quick time is that the newcomer artificial intelligence systems are competitive with and, in certain instances, more accurate than the slower traditional forecasting tools we’ve relied upon,” Lowry added.

Clarifying Machine Learning

To be sure, the system is an instance of AI training – a technique that has been used in research fields like meteorology for a long time – and is distinct from generative AI like ChatGPT.

AI training processes large datasets and extracts trends from them in a manner that its model only requires minutes to come up with an result, and can operate on a standard PC – in sharp difference to the flagship models that governments have used for decades that can take hours to run and require the largest high-performance systems in the world.

Professional Responses and Upcoming Developments

Nevertheless, the reality that Google’s model could exceed earlier top-tier legacy models so rapidly is nothing short of amazing to weather scientists who have spent their careers trying to predict the most intense storms.

“It’s astonishing,” commented James Franklin, a retired expert. “The sample is sufficient that it’s evident this is not just beginner’s luck.”

Franklin said that although the AI is outperforming all other models on predicting the future path of hurricanes worldwide this year, like many AI models it occasionally gets extreme strength forecasts wrong. It had difficulty with Hurricane Erin previously, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

During the next break, Franklin stated he plans to talk with Google about how it can enhance the AI results more useful for forecasters by providing extra under-the-hood data they can utilize to evaluate the reasons it is producing its conclusions.

“A key concern that troubles me is that while these predictions seem to be highly accurate, the results of the system is kind of a opaque process,” said Franklin.

Wider Industry Trends

There has never been a private, for-profit company that has produced a top-level weather model which grants experts a peek into its techniques – unlike most other models which are provided free to the general audience in their full form by the governments that created and operate them.

The company is not the only one in adopting artificial intelligence to address difficult meteorological problems. The authorities also have their own artificial intelligence systems in the works – which have also shown better performance over earlier non-AI versions.

The next steps in artificial intelligence predictions seem to be new firms tackling previously difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and sudden deluges – and they have secured federal support to do so. One company, WindBorne Systems, is also launching its own weather balloons to fill the gaps in the US weather-observing network.