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When Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane.

As the primary meteorologist on duty, he forecasted that in a single day the weather system would become a severe hurricane and begin a turn towards the coast of Jamaica. Not a single expert had ever issued such a bold prediction for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the form of Google’s recently introduced DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his confidence: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa becoming a most intense storm. Although I am not ready to forecast that intensity yet due to track uncertainty, that remains a possibility.

“There is a high probability that a period of quick strengthening is expected as the storm drifts over exceptionally hot ocean waters which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first artificial intelligence system dedicated to tropical cyclones, and currently the initial to beat standard weather forecasters at their own game. Across all 13 Atlantic storms this season, the AI is the best – surpassing human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at maximum strength, among the most powerful coastal impacts ever documented in nearly two centuries of data collection across the region. Papin’s bold forecast likely gave people in Jamaica extra time to prepare for the disaster, potentially preserving lives and property.

The Way The Model Functions

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

“They do it far faster than their traditional counterparts, and the processing requirements is less expensive and time consuming,” stated Michael Lowry, a former forecaster.

“This season’s events has proven in short order is that the recent artificial intelligence systems are competitive with and, in some cases, more accurate than the slower physics-based forecasting tools we’ve traditionally leaned on,” he said.

Clarifying AI Technology

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

Machine learning processes large datasets and extracts trends from them in a manner that its system only takes a few minutes to generate an result, and can operate on a standard PC – in strong contrast to the flagship models that governments have used for decades that can require many hours to process and need the largest supercomputers in the world.

Professional Reactions and Future Advances

Still, the fact that the AI could exceed earlier top-tier traditional systems so rapidly is truly remarkable to meteorologists who have spent their careers trying to forecast the most intense weather systems.

“It’s astonishing,” commented James Franklin, a former expert. “The data is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

He noted that while Google DeepMind is outperforming all other models on forecasting the future path of hurricanes worldwide this year, like many AI models it occasionally gets high-end intensity predictions inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, Franklin said he intends to talk with the company about how it can enhance the DeepMind output even more helpful for forecasters by providing extra under-the-hood data they can use to assess exactly why it is producing its conclusions.

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

Broader Sector Trends

Historically, no a commercial entity that has produced a top-level weather model which allows researchers a peek into its techniques – in contrast to nearly all systems which are offered free to the public in their entirety by the governments that created and operate them.

Google is not the only one in adopting artificial intelligence to address difficult meteorological problems. The authorities are developing their respective artificial intelligence systems in the development phase – which have also shown better performance over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve new firms tackling previously difficult problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also deploying its own atmospheric sensors to fill the gaps in the US weather-observing network.