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When Developing Cyclone Melissa swirled off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in a single day the weather system would intensify into a severe hurricane and start shifting towards the coast of Jamaica. No forecaster had previously made this confident prediction for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa evolved into a system of remarkable power that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon the AI system. During 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his confidence: “Roughly 40/50 Google DeepMind simulation runs show Melissa reaching a most intense hurricane. While I am unprepared to predict that intensity at this time given track uncertainty, that is still plausible.

“It appears likely that a period of rapid intensification will occur as the storm drifts over very warm ocean waters which is the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Models

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

Melissa eventually made landfall in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in almost 200 years of data collection across the region. Papin’s bold forecast probably provided residents extra time to prepare for the disaster, possibly saving people and assets.

How The System Functions

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

“The AI performs far faster than their physics-based cousins, and the computing power is less expensive and demanding,” said Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in quick time is that the newcomer AI weather models are on par with and, in certain instances, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” he said.

Clarifying AI Technology

To be sure, the system is an example of machine learning – a method that has been used in research fields like weather science for years – and is not 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 takes a few minutes to come up with an result, and can do so on a desktop computer – in strong contrast to the primary systems that authorities have used for years that can require many hours to process and need some of the biggest high-performance systems in the world.

Professional Responses and Future Advances

Nevertheless, the fact that the AI could exceed earlier top-tier traditional systems so quickly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the world’s strongest weather systems.

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

Franklin noted that although Google DeepMind is outperforming all competing systems on forecasting the trajectory of storms worldwide this year, similar to other systems it occasionally gets high-end intensity predictions inaccurate. It struggled with Hurricane Erin previously, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

During the next break, Franklin stated he intends to discuss with Google about how it can enhance the AI results even more helpful for forecasters by providing additional internal information they can utilize to assess the reasons it is producing its conclusions.

“The one thing that troubles me is that while these forecasts appear highly accurate, the results of the model is essentially a black box,” said Franklin.

Wider Sector Developments

Historically, no a commercial entity that has developed a high-performance forecasting system which allows researchers a view of its techniques – unlike nearly all other models which are provided free to the public in their full form by the governments that designed and maintain them.

Google is not alone in starting to use AI to solve difficult weather forecasting problems. The US and European governments also have their respective artificial intelligence systems in the development phase – which have demonstrated improved skill over previous non-AI versions.

Future developments in AI weather forecasts seem to be new firms tackling previously difficult problems such as sub-seasonal outlooks and better advance warnings 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 proprietary atmospheric sensors to fill the gaps in the national monitoring system.