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

Serving as lead forecaster on duty, he forecasted that in just 24 hours the storm would become a category 4 hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made such a bold prediction for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the form of Google’s new DeepMind hurricane model – released for the initial occasion in June. True to the forecast, Melissa did become a storm of remarkable power that tore through Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his certainty: “Roughly 40/50 AI ensemble members indicate Melissa reaching a Category 5 hurricane. While I am unprepared to forecast that intensity at this time due to track uncertainty, that is still plausible.

“There is a high probability that a period of rapid intensification will occur as the system drifts over exceptionally hot sea temperatures which is the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the pioneer AI model focused on hurricanes, and now the initial to beat standard meteorological experts at their specialty. Across all 13 Atlantic storms so far this year, Google’s model is top-performing – even beating experts on path forecasts.

The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls recorded in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided residents additional preparation time to get ready for the catastrophe, potentially preserving people and assets.

How The Model Functions

Google’s model works by identifying trends that conventional time-intensive scientific weather models may overlook.

“The AI performs far faster 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 recent AI weather models are on par with and, in certain instances, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” Lowry added.

Understanding Machine Learning

It’s important to note, the system is an instance of machine learning – a method that has been employed in research fields like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

AI training takes large datasets and extracts trends from them in a such a way that its model only requires minutes to come up with an answer, and can operate on a desktop computer – in strong contrast to the flagship models that governments have used for years that can require many hours to run and need the largest high-performance systems in the world.

Professional Reactions and Future Developments

Still, the reality that Google’s model could outperform previous gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a retired forecaster. “The sample is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

Franklin said that while the AI is beating all other models on predicting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty with Hurricane Erin previously, as it was also undergoing rapid intensification to category 5 above the Caribbean.

In the coming offseason, Franklin said he intends to discuss with the company about how it can enhance the AI results more useful for forecasters by providing additional internal information they can utilize to evaluate exactly why it is coming up with its answers.

“The one thing that nags at me is that while these forecasts appear highly accurate, the output of the model is essentially a opaque process,” said Franklin.

Broader Industry Trends

Historically, no a private, for-profit company that has developed a high-performance forecasting system which grants experts a view of its techniques – unlike most other models which are offered at no cost to the public in their entirety by the governments that created and operate them.

The company is not alone in starting to use AI to solve challenging meteorological problems. The authorities also have their respective artificial intelligence systems in the works – which have also shown improved skill over earlier traditional systems.

The next steps in artificial intelligence predictions appear to involve startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of severe weather and sudden deluges – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is even deploying its own atmospheric sensors to fill the gaps in the US weather-observing network.