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As Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in a single day the weather system would intensify into a category 4 hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had previously made this confident forecast 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 first time in June. True to the forecast, Melissa did become a storm of remarkable power that ravaged Jamaica.

Growing Reliance on AI Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his certainty: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa reaching a Category 5 hurricane. Although I am not ready to predict that intensity at this time given path variability, that remains a possibility.

“It appears likely that a period of rapid intensification is expected as the system moves slowly over exceptionally hot sea temperatures which is the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the pioneer AI model dedicated to hurricanes, and currently the initial to beat standard meteorological experts at their specialty. Across 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 strength, among the most powerful coastal impacts ever documented in almost 200 years of record-keeping across the region. The confident prediction probably provided people in Jamaica additional preparation time to prepare for the catastrophe, potentially preserving people and assets.

The Way Google’s Model Functions

The AI system works by spotting patterns that conventional time-intensive physics-based weather models may overlook.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is more affordable and demanding,” said Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in quick time is that the recent AI weather models are on par with and, in some cases, more accurate than the less rapid traditional 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 used in research fields like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a manner that its model only requires minutes to generate an result, and can operate on a desktop computer – in strong contrast to the flagship models that authorities have utilized for decades that can require many hours to process and need the largest high-performance systems in the world.

Professional Reactions and Upcoming Advances

Still, the fact that Google’s model could outperform earlier gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to predict the most intense weather systems.

“I’m impressed,” said James Franklin, a former forecaster. “The data is now large enough that it’s evident this is not a case of beginner’s luck.”

He noted that although the AI is outperforming all competing systems on predicting the future path of hurricanes worldwide this year, like many AI models it sometimes errs on high-end intensity forecasts wrong. It had difficulty with Hurricane Erin previously, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, he stated he plans to talk with the company about how it can enhance the AI results even more helpful for forecasters by offering additional internal information they can use to assess exactly why it is producing its answers.

“The one thing that nags at me is that although these predictions appear really, really good, the results of the system is kind of a black box,” said Franklin.

Broader Industry Trends

Historically, no a private, for-profit company that has produced a top-level forecasting system which grants experts a peek into its methods – unlike most systems which are offered at no cost 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 meteorological problems. The US and European governments are developing their own AI weather models in the development phase – which have also shown better performance over earlier traditional systems.

The next steps in artificial intelligence predictions seem to be startup companies tackling formerly difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even launching its own weather balloons to address deficiencies in the US weather-observing network.