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As Developing Cyclone Melissa swirled off the coast of Haiti, weather expert Philippe Papin had confidence it was about to grow into a major tropical system.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the weather system would become a category 4 hurricane and start shifting towards the coast of Jamaica. Not a single expert had previously made such a bold forecast for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the guise 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 ravaged Jamaica.

Increasing Reliance on AI Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his certainty: “Approximately 40/50 AI ensemble members indicate Melissa becoming a Category 5 hurricane. While I am unprepared to forecast that intensity yet due to path variability, that remains a possibility.

“There is a high probability that a period of rapid intensification is expected as the storm drifts over exceptionally hot sea temperatures which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the pioneer artificial intelligence system focused on hurricanes, and currently the first to beat standard weather forecasters at their own game. Across all 13 Atlantic storms so far this year, Google’s model is top-performing – even beating experts on track predictions.

Melissa eventually made landfall in Jamaica at maximum intensity, among the most powerful landfalls ever documented in nearly two centuries of record-keeping across the region. The confident prediction likely gave residents additional preparation time to prepare for the disaster, potentially preserving people and assets.

How Google’s Model Functions

Google’s model operates through spotting patterns that traditional lengthy scientific prediction systems may miss.

“The AI performs far faster than their traditional counterparts, and the computing power is less expensive and time consuming,” stated Michael Lowry, a ex forecaster.

“This season’s events has proven in quick time is that the newcomer 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,” Lowry said.

Clarifying AI Technology

To be sure, Google DeepMind is an example of AI training – a technique that has been used in data-heavy sciences like meteorology for years – and is distinct from generative AI like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a manner that its system only requires minutes to generate an answer, and can operate on a desktop computer – in sharp difference to the flagship models that authorities have utilized for years that can take hours to process and require some of the biggest high-performance systems in the world.

Professional Reactions and Upcoming Developments

Still, the reality that the AI could exceed previous gold-standard traditional systems so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the world’s strongest weather systems.

“I’m impressed,” said James Franklin, a retired forecaster. “The sample is sufficient that it’s pretty clear this is not just beginner’s luck.”

He said that while Google DeepMind is outperforming all competing systems on predicting the trajectory of storms globally this year, similar to other systems it occasionally gets extreme strength forecasts wrong. It had difficulty with another storm earlier this year, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

During the next break, he said he plans to discuss with Google about how it can enhance the AI results even more helpful for experts by providing additional under-the-hood data they can utilize to assess the reasons it is coming up with its answers.

“The one thing that troubles me is that although these forecasts seem to be highly accurate, the output of the model is kind of a black box,” remarked Franklin.

Broader Industry Developments

There has never been a commercial entity that has produced a high-performance forecasting system which grants experts a view of its methods – unlike nearly all other models which are offered at no cost to the public in their full form by the authorities that created and operate them.

The company is not alone in starting to use AI to address difficult weather forecasting problems. The US and European governments are developing their own artificial intelligence systems in the works – which have demonstrated improved skill over earlier non-AI versions.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even deploying its proprietary weather balloons to address deficiencies in the national monitoring system.