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As Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it would soon grow into a monster hurricane.

Serving as lead forecaster on duty, he predicted that in just 24 hours the weather system would become a severe hurricane and begin a turn towards the coast of Jamaica. Not a single expert had previously made this confident forecast for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s recently introduced DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his confidence: “Roughly 40/50 AI ensemble members indicate Melissa reaching a most intense hurricane. Although I am unprepared to forecast that strength yet given track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening is expected as the system drifts over very warm sea temperatures which represent the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the pioneer AI model dedicated to hurricanes, and now the first to beat standard meteorological experts at their own game. Through all 13 Atlantic storms so far this year, the AI is the best – even beating experts on track predictions.

Melissa eventually made landfall in Jamaica at maximum intensity, one of the strongest coastal impacts ever documented in almost 200 years of record-keeping across the Atlantic basin. The confident prediction likely gave residents extra time to prepare for the disaster, possibly saving people and assets.

How Google’s Model Functions

Google’s model works by spotting patterns that conventional lengthy physics-based prediction systems may miss.

“The AI performs much more quickly than their physics-based cousins, and the computing power is more affordable and time consuming,” said Michael Lowry, a former forecaster.

“What this hurricane season has proven in short order is that the newcomer AI weather models are on par with and, in certain instances, more accurate than the less rapid traditional weather models we’ve traditionally leaned on,” he added.

Understanding AI Technology

To be sure, Google DeepMind is an example of machine learning – a technique that has been used in data-heavy sciences like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a manner that its system only requires minutes to generate an result, and can operate on a standard PC – in sharp difference to the primary systems that authorities have utilized for years that can take hours to process and require some of the biggest supercomputers in the world.

Expert Reactions and Future Advances

Nevertheless, the fact that Google’s model could exceed previous top-tier legacy models so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to predict the most intense weather systems.

“I’m impressed,” said James Franklin, a former expert. “The data is sufficient that it’s pretty clear this is not a case of chance.”

He said that while the AI is beating all other models on predicting the trajectory of storms globally this year, similar to other systems it sometimes errs on extreme strength forecasts wrong. It struggled with Hurricane Erin previously, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

During the next break, Franklin stated he intends to discuss with Google about how it can enhance the AI results even more helpful for forecasters by offering additional internal information they can use to evaluate exactly why it is producing its conclusions.

“A key concern that nags at me is that although these predictions seem to be really, really good, the results of the system is kind of a opaque process,” remarked Franklin.

Broader Industry 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 techniques – unlike most systems which are provided at no cost to the general audience in their full form by the authorities that designed and maintain them.

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

The next steps in artificial intelligence predictions appear to involve startup companies taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of severe weather and flash flooding – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is also launching its own weather balloons to fill the gaps in the national monitoring system.