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As Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon grow into a major tropical system.

As the primary meteorologist on duty, he predicted that in just 24 hours the weather system would intensify into a severe hurricane and begin a turn towards the coast of Jamaica. Not a single expert had ever issued this confident prediction for quick intensification.

However, Papin possessed a secret advantage: AI technology in the guise of Google’s recently introduced DeepMind hurricane model – launched 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 Forecasting

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin explained in his public discussion that the AI tool was a key factor for his certainty: “Approximately 40/50 AI simulation runs show Melissa reaching a most intense hurricane. While I am not ready to predict that intensity at this time due to track uncertainty, that is still plausible.

“It appears likely that a phase of rapid intensification is expected as the system drifts over exceptionally hot sea temperatures which is the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the pioneer AI model dedicated to hurricanes, and currently the initial to beat traditional meteorological experts at their specialty. Across all 13 Atlantic storms so far this year, the AI is the best – surpassing human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 intensity, one of the strongest coastal impacts ever documented in almost 200 years of data collection across the Atlantic basin. The confident prediction likely gave residents extra time to get ready for the catastrophe, potentially preserving lives and property.

How The System Functions

The AI system operates through identifying trends that conventional time-intensive physics-based weather models may miss.

“They do it far faster than their traditional counterparts, and the computing power is less expensive and time consuming,” said Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in short order is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the slower traditional forecasting tools we’ve traditionally leaned on,” Lowry said.

Understanding Machine Learning

It’s important to note, Google DeepMind is an example of AI training – a method that has been used in research fields like weather science for years – and is not generative AI like ChatGPT.

AI training takes mounds of data and extracts trends from them in a such a way that its system only takes a few minutes to generate an result, and can operate on a standard PC – in sharp difference to the primary systems that authorities have used for decades that can take hours to process and require some of the biggest supercomputers in the world.

Expert Responses and Future Developments

Nevertheless, the reality that Google’s model could exceed earlier top-tier legacy models so quickly is truly remarkable to meteorologists who have spent their careers trying to predict the most intense weather systems.

“It’s astonishing,” said James Franklin, a former forecaster. “The sample is now large enough that it’s evident this is not a case of beginner’s luck.”

Franklin said that while the AI is outperforming all other models on forecasting the trajectory of storms globally this year, similar to other systems it occasionally gets extreme strength predictions wrong. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

During the next break, Franklin stated he intends to talk with Google about how it can make the DeepMind output even more helpful for forecasters by providing extra under-the-hood data they can use to evaluate the reasons it is coming up with its conclusions.

“The one thing that nags at me is that while these forecasts seem to be really, really good, the output of the system is kind of a black box,” remarked Franklin.

Wider Sector Trends

Historically, no a private, for-profit company that has developed a top-level forecasting system which grants experts a view of its techniques – unlike most other models which are offered free to the general audience in their entirety by the governments that created and operate them.

Google is not alone in starting to use artificial intelligence to solve difficult weather forecasting problems. The authorities also have their respective AI weather models in the works – which have also shown better performance over earlier non-AI versions.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at previously difficult problems such as sub-seasonal outlooks and better advance warnings of severe weather and flash flooding – and they have secured federal support to pursue this. One company, WindBorne Systems, is even deploying its proprietary weather balloons to address deficiencies in the US weather-observing network.