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When Tropical Storm Melissa was churning south of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a major tropical system.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the weather system would become a severe hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had previously made this confident prediction for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa evolved into a system of remarkable power that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Forecasters are heavily relying upon the AI system. During 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his certainty: “Approximately 40/50 Google DeepMind simulation runs show Melissa reaching a Category 5 storm. While I am not ready to predict that intensity yet due to path variability, that is still plausible.

“There is a high probability that a period of quick strengthening is expected as the storm drifts over very warm ocean waters which is the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the first AI model focused on hurricanes, and now the first to outperform traditional meteorological experts at their specialty. Through all tropical systems this season, Google’s model is the best – even beating human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at maximum intensity, among the most powerful landfalls recorded in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica additional preparation time to get ready for the catastrophe, possibly saving people and assets.

How The System Functions

Google’s model operates through spotting patterns that traditional lengthy physics-based prediction systems may overlook.

“The AI performs much more quickly than their physics-based cousins, and the computing power is more affordable and demanding,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in quick time is that the newcomer AI weather models are on par with and, in certain instances, superior than the less rapid traditional weather models we’ve relied upon,” Lowry added.

Clarifying Machine Learning

To be sure, the system is an example of AI training – a technique that has been employed in research fields like weather science for a long time – and is not generative AI like ChatGPT.

Machine learning processes large datasets and extracts trends from them in a manner that its model only requires minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the flagship models that governments have utilized for decades that can require many hours to run and require some of the biggest supercomputers in the world.

Expert Responses and Upcoming Developments

Nevertheless, the reality that Google’s model could outperform previous gold-standard traditional systems so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the world’s strongest storms.

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

He noted that although the AI is outperforming all competing systems on forecasting the future path of hurricanes globally this year, similar to other systems it occasionally gets extreme strength forecasts wrong. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to category 5 above the Caribbean.

During the next break, Franklin stated he plans to talk with the company about how it can enhance the AI results even more helpful for forecasters by offering additional under-the-hood data they can use to assess the reasons it is coming up with its answers.

“The one thing that nags at me is that although these forecasts appear highly accurate, the results of the system is essentially a opaque process,” remarked Franklin.

Broader Sector Developments

Historically, no a private, for-profit company that has developed a top-level weather model which allows researchers a view of its techniques – in contrast to most systems which are provided free to the general audience in their entirety by the governments that created and operate them.

Google is not alone in starting to use artificial intelligence to address difficult weather forecasting problems. The US and European governments are developing 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 long-range forecasts and better advance warnings of severe weather and flash flooding – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even deploying its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.