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As Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in a single day the storm would intensify into a category 4 hurricane and start shifting towards the coast of Jamaica. No forecaster had previously made this confident forecast for quick intensification.

But, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s recently introduced DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a system of remarkable power that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his confidence: “Roughly 40/50 Google DeepMind simulation runs show Melissa becoming a Category 5 storm. Although I am unprepared to forecast that intensity yet due to path variability, that remains a possibility.

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

Outperforming Conventional Systems

Google DeepMind is the pioneer AI model dedicated to tropical cyclones, and currently the first to outperform traditional meteorological experts at their own game. Across all tropical systems this season, the AI is top-performing – surpassing human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at category 5 strength, among the most powerful coastal impacts ever documented in nearly two centuries of data collection across the region. The confident prediction probably provided residents additional preparation time to prepare for the catastrophe, potentially preserving people and assets.

The Way The System Functions

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

“The AI performs much more quickly than their traditional counterparts, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in short order is that the newcomer artificial intelligence systems are competitive with and, in some cases, more accurate than the slower traditional weather models we’ve relied upon,” he added.

Understanding Machine Learning

To be sure, the system is an example of AI training – a method that has been used in data-heavy sciences like meteorology for a long time – and is distinct from 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 result, and can do so on a desktop computer – in sharp difference to the flagship models that governments have used for years that can require many hours to run and require some of the biggest high-performance systems in the world.

Professional Responses and Future Developments

Still, the fact that Google’s model could exceed earlier top-tier legacy models so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the most intense storms.

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

Franklin said that although Google DeepMind is outperforming all competing systems on predicting the trajectory of hurricanes worldwide this year, similar to other systems it sometimes errs on high-end intensity predictions inaccurate. It struggled with Hurricane Erin previously, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

During the next break, Franklin said he plans to discuss with Google about how it can enhance the DeepMind output more useful for forecasters by offering extra under-the-hood data they can utilize to assess the reasons it is producing its answers.

“A key concern that troubles me is that while these forecasts appear really, really good, the output of the system is essentially a opaque process,” remarked Franklin.

Broader Industry Developments

Historically, no a commercial entity that has produced a top-level forecasting system which grants experts a peek into its methods – unlike most systems which are provided free to the general audience in their entirety by the authorities that designed and maintain them.

The company is not alone in adopting AI to address difficult meteorological problems. The US and European governments are developing their respective artificial intelligence systems in the works – which have demonstrated improved skill over previous traditional systems.

Future developments in AI weather forecasts appear to involve startup companies tackling previously tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and flash flooding – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is even launching its proprietary atmospheric sensors to fill the gaps in the national monitoring system.