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When Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a monster hurricane.

As the primary meteorologist on duty, he forecasted that in a single day the weather system would intensify into a severe 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 had an ace up his sleeve: AI technology 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 astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI ensemble members indicate Melissa reaching a most intense hurricane. While I am unprepared to forecast that strength yet given track uncertainty, that remains a possibility.

“It appears likely that a period of quick strengthening is expected as the storm drifts over very warm sea temperatures which is the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Models

The AI model is the first artificial intelligence system focused on hurricanes, and now the initial to beat traditional meteorological experts at their own game. Through all tropical systems so far this year, Google’s model is the best – surpassing human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at category 5 intensity, one of the strongest coastal impacts ever documented in nearly two centuries of data collection across the Atlantic basin. The confident prediction probably provided residents extra time to prepare for the disaster, possibly saving people and assets.

The Way Google’s System Functions

Google’s model operates through identifying trends that traditional lengthy scientific prediction systems may miss.

“They do it much more quickly than their physics-based cousins, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in quick time is that the recent artificial intelligence systems are competitive with and, in certain instances, superior than the less rapid traditional forecasting tools we’ve traditionally leaned on,” Lowry added.

Clarifying AI Technology

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 not generative AI like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a manner that its model only takes a few minutes to come up with 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 run and require some of the biggest supercomputers in the world.

Professional Responses and Upcoming Advances

Nevertheless, the reality that the AI could exceed previous gold-standard legacy models so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the most intense storms.

“It’s astonishing,” commented James Franklin, a retired forecaster. “The sample is sufficient that it’s pretty clear this is not a case of chance.”

He said that while the AI is beating all other models on forecasting the trajectory of hurricanes worldwide this year, like many AI models it occasionally gets high-end intensity forecasts inaccurate. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, he said he plans to talk with the company about how it can make the DeepMind output even more helpful for experts by offering additional internal information they can utilize to assess exactly why it is coming up with its conclusions.

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

Broader Industry Developments

There has never been a private, for-profit company that has developed a high-performance forecasting system which allows researchers a peek into its methods – unlike most other models which are provided free to the general audience in their entirety by the authorities that created and operate them.

Google is not alone in adopting artificial intelligence to solve difficult meteorological problems. The authorities are developing their own artificial intelligence systems in the development phase – which have demonstrated improved skill over earlier traditional systems.

The next steps in AI weather forecasts seem to be new firms tackling previously difficult problems such as long-range forecasts and improved early alerts of severe weather and flash flooding – and they have secured federal support to do so. A particular firm, WindBorne Systems, is also launching its proprietary atmospheric sensors to address deficiencies in the national monitoring system.