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When Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a monster hurricane.

As the primary meteorologist on duty, he predicted that in just 24 hours the storm would intensify into a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had ever issued such a bold forecast for quick intensification.

But, Papin possessed a secret advantage: AI technology in the form of Google’s new DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa evolved into a system of remarkable power that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Forecasting

Meteorologists 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 key factor for his certainty: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa reaching a Category 5 storm. Although I am unprepared to predict that intensity yet given path variability, that is still plausible.

“It appears likely that a period of quick strengthening will occur as the system drifts over very warm ocean waters which is the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Models

The AI model is the pioneer AI model focused on hurricanes, and now the initial to outperform standard weather forecasters at their specialty. Through all 13 Atlantic storms this season, the AI is top-performing – surpassing experts on track predictions.

The hurricane ultimately struck in Jamaica at maximum strength, among the most powerful coastal impacts ever documented in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica extra time to get ready for the catastrophe, potentially preserving people and assets.

How The Model Functions

The AI system works by identifying trends that conventional lengthy physics-based weather models may miss.

“They do it far faster 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 some cases, superior than the less rapid traditional weather models we’ve traditionally leaned on,” he added.

Understanding AI Technology

It’s important to note, Google DeepMind is an instance of machine learning – a method that has been employed in data-heavy sciences like meteorology for years – and is not creative artificial intelligence like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a manner that its model only takes a few minutes to generate an result, and can do so on a desktop computer – in sharp difference to the flagship models that authorities have used for decades that can require many hours to process and need some of the biggest supercomputers in the world.

Professional Responses and Future Developments

Still, the fact that Google’s model could exceed earlier gold-standard legacy models so quickly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the world’s strongest storms.

“I’m impressed,” commented James Franklin, a former expert. “The data is now large enough that it’s pretty clear this is not just chance.”

He noted that although the AI is beating all competing systems on predicting the trajectory of hurricanes globally this year, like many AI models it sometimes errs on extreme strength predictions inaccurate. It had difficulty with Hurricane Erin previously, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, Franklin stated he plans to discuss with Google about how it can enhance the DeepMind output even more helpful for forecasters by offering extra under-the-hood data they can utilize to assess the reasons it is coming up with its conclusions.

“The one thing that troubles me is that although these predictions appear really, really good, the output of the model is essentially a black box,” remarked Franklin.

Broader Sector Developments

There has never been a commercial entity that has developed a top-level forecasting system which grants experts a view of its techniques – in contrast to nearly all other models which are offered at no cost to the public in their full form by the governments that created and operate them.

The company is not alone in adopting AI to solve challenging weather forecasting problems. The US and European governments are developing their respective AI weather models in the development phase – which have also shown improved skill over earlier non-AI versions.

Future developments in artificial intelligence predictions seem to be new firms tackling formerly tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of severe weather and sudden deluges – and they are receiving federal support to do so. One company, WindBorne Systems, is also launching its own weather balloons to fill the gaps in the national monitoring system.