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As Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin had confidence it would soon grow into a monster hurricane.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the weather system would become a category 4 hurricane and start shifting towards the Jamaican shoreline. No forecaster had ever issued such a bold forecast for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the guise of Google’s recently introduced DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa did become a storm of remarkable power that tore through Jamaica.

Growing Dependence on AI Predictions

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his certainty: “Roughly 40/50 Google DeepMind simulation runs show Melissa reaching a most intense storm. Although I am unprepared to predict that strength yet due to track uncertainty, that remains a possibility.

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

Surpassing Traditional Systems

Google DeepMind is the pioneer AI model focused on hurricanes, and currently the initial to beat standard weather forecasters at their specialty. Through all tropical systems so far this year, Google’s model is top-performing – even beating human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at maximum strength, among the most powerful coastal impacts recorded in almost 200 years of record-keeping across the Atlantic basin. The confident prediction likely gave people in Jamaica extra time to get ready for the disaster, possibly saving lives and property.

How Google’s System Functions

Google’s model works by identifying trends that traditional time-intensive scientific prediction systems may miss.

“The AI performs far faster than their traditional counterparts, and the computing power is less expensive and time consuming,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in quick time is that the newcomer artificial intelligence systems are competitive with and, in some cases, superior than the slower physics-based weather models we’ve relied upon,” Lowry said.

Clarifying Machine Learning

It’s important to note, the system is an instance of machine learning – a technique that has been employed in research fields like meteorology for years – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a such a way that its system only requires minutes to generate an result, and can do so on a desktop computer – in strong contrast to the flagship models that governments have utilized for years that can require many hours to run and need the largest high-performance systems in the world.

Expert Responses and Future Developments

Still, the fact that the AI could exceed previous gold-standard traditional systems so quickly is truly remarkable to meteorologists who have spent their careers trying to forecast the most intense weather systems.

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

He noted that although the AI is outperforming all other models on forecasting the trajectory of hurricanes globally this year, like many AI models it occasionally gets extreme strength predictions inaccurate. It had difficulty with another storm previously, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

During the next break, Franklin said he intends to talk with Google about how it can make the DeepMind output even more helpful for forecasters by offering additional under-the-hood data they can utilize to evaluate the reasons it is producing its answers.

“The one thing that troubles me is that although these forecasts seem to be highly accurate, the results of the system is essentially a opaque process,” said Franklin.

Wider Industry Developments

Historically, no a private, for-profit company that has developed a top-level forecasting system which allows researchers a peek into its techniques – unlike nearly all systems which are offered free to the general audience in their entirety by the authorities that designed and maintain them.

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

Future developments in AI weather forecasts seem to be startup companies tackling previously difficult problems such as long-range forecasts and better early alerts of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also launching its own atmospheric sensors to address deficiencies in the national monitoring system.