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When Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a major tropical system.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the weather system would intensify into a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made such a bold forecast for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. True to the forecast, Melissa did become a storm of remarkable power that ravaged Jamaica.

Growing Reliance on AI Predictions

Meteorologists are heavily relying upon the AI system. During 25 October, Papin explained in his official briefing that Google’s model was a key factor for his certainty: “Roughly 40/50 AI ensemble members indicate Melissa reaching a Category 5 hurricane. Although I am not ready to forecast that strength at this time due to path variability, that is still plausible.

“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 most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Systems

The AI model is the pioneer artificial intelligence system focused on hurricanes, and currently the first to beat standard meteorological experts at their own game. Across all 13 Atlantic storms so far this year, the AI is top-performing – surpassing experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 strength, one of the strongest landfalls recorded in almost 200 years of record-keeping across the Atlantic basin. The confident prediction likely gave residents extra time to prepare for the disaster, possibly saving lives and property.

How The System Works

The AI system operates through spotting patterns that traditional lengthy scientific weather models may overlook.

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

“What this hurricane season has demonstrated in quick time is that the newcomer AI weather models are on par with and, in some cases, superior than the slower traditional forecasting tools we’ve relied upon,” he added.

Understanding Machine Learning

To be sure, the system is an instance of AI training – a method that has been employed in research fields like weather science for years – and is not creative artificial intelligence like ChatGPT.

AI training processes mounds of data and pulls out patterns from them in a such a way that its system only takes a few minutes to generate an answer, and can operate on a desktop computer – in sharp difference to the flagship models that governments have used for decades that can take hours to run and need the largest supercomputers in the world.

Professional Responses and Future Developments

Still, the fact that Google’s model could outperform previous top-tier traditional systems so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense weather systems.

“I’m impressed,” commented James Franklin, a former forecaster. “The data is sufficient that it’s evident this is not just chance.”

Franklin noted that although the AI is outperforming all other models on predicting the future path of storms globally this year, like many AI models it sometimes errs on high-end intensity forecasts inaccurate. It struggled with another storm previously, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

During the next break, he said he intends to discuss with the company about how it can make the AI results more useful for experts by offering extra under-the-hood data they can use to evaluate the reasons it is coming up with its answers.

“A key concern that troubles me is that while these predictions seem to be really, really good, the output of the model is kind of a black box,” remarked Franklin.

Wider Industry Trends

Historically, no a private, for-profit company that has developed a high-performance forecasting system which allows researchers a peek into its techniques – unlike most systems which are provided free to the general audience in their entirety by the authorities that created and operate them.

The company is not the only one 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 demonstrated improved skill over earlier non-AI versions.

Future developments in artificial intelligence predictions seem to be new firms tackling formerly difficult problems such as sub-seasonal outlooks and better early alerts of severe weather and sudden deluges – and they have secured federal support to pursue this. One company, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the national monitoring system.