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When Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a monster hurricane.

Serving as lead forecaster on duty, he forecasted that in a single day the storm would become a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had previously made this confident prediction for quick intensification.

However, 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 evolved into a system of remarkable power that ravaged Jamaica.

Increasing Reliance on AI Predictions

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 AI ensemble members indicate Melissa reaching a Category 5 storm. Although I am unprepared to predict that strength yet given track uncertainty, that is still plausible.

“There is a high probability that a period of rapid intensification will occur as the system moves slowly over very warm sea temperatures which is the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the first artificial intelligence system focused on hurricanes, and currently the initial to beat traditional weather forecasters at their specialty. Across all tropical systems so far this year, the AI is the best – even beating human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 strength, one of the strongest landfalls recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction probably provided people in Jamaica additional preparation time to get ready for the catastrophe, possibly saving people and assets.

How The System Works

Google’s model works by spotting patterns that conventional time-intensive scientific prediction systems may overlook.

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

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

Clarifying Machine Learning

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

AI training takes large datasets and pulls out patterns from them in a such a way that its system only takes a few minutes to come up with an result, and can do so on a desktop computer – in strong contrast to the primary systems that authorities have utilized for years that can require many hours to run and require the largest supercomputers in the world.

Professional Reactions and Future Developments

Nevertheless, the reality that the AI could exceed earlier top-tier traditional systems so rapidly is truly remarkable to meteorologists who have spent their careers trying to predict the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a former forecaster. “The sample is now large enough that it’s evident this is not just chance.”

He noted that while Google DeepMind is outperforming all competing systems on forecasting the trajectory of hurricanes globally this year, similar to other systems it occasionally gets extreme strength forecasts inaccurate. It struggled with another storm previously, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he stated he plans to talk with the company about how it can enhance the DeepMind output even more helpful for forecasters by providing additional internal information they can utilize 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 black box,” remarked Franklin.

Broader Industry Developments

There has never been a private, for-profit company that has developed a high-performance weather model which allows researchers a view of its methods – in contrast to nearly all systems which are offered at no cost to the general audience in their entirety by the governments that designed and maintain them.

Google is not alone in adopting artificial intelligence to solve difficult weather forecasting problems. The US and European governments 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 new firms tackling previously difficult problems such as long-range forecasts and improved advance warnings of severe weather and sudden deluges – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is also launching its own atmospheric sensors to address deficiencies in the national monitoring system.