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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.

As the primary meteorologist on duty, he forecasted that in a single day the storm would intensify into a severe hurricane and begin a turn towards the coast of Jamaica. Not a single expert had previously made this confident prediction for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s new DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa did become a system of remarkable power that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Forecasting

Forecasters are heavily relying upon the AI system. During 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his certainty: “Roughly 40/50 Google DeepMind ensemble members show Melissa reaching a most intense hurricane. While I am not ready to forecast that intensity at this time given path variability, that is still plausible.

“It appears likely that a phase of rapid intensification is expected as the system drifts over very warm ocean waters which represent the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Systems

The AI model is the first AI model focused on tropical cyclones, and currently the first to outperform traditional weather forecasters at their own game. Across all tropical systems so far this year, Google’s model is top-performing – even beating human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 intensity, one of the strongest landfalls recorded in nearly two centuries of data collection across the region. Papin’s bold forecast probably provided residents extra time to get ready for the disaster, possibly saving people and assets.

How The System Functions

Google’s model works by spotting patterns that conventional lengthy scientific prediction systems may overlook.

“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.

“This season’s events has proven in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, superior than the less rapid physics-based weather models we’ve relied upon,” Lowry said.

Clarifying AI Technology

It’s important to note, the system is an instance of machine learning – a technique that has been used in research fields like meteorology for years – and is not creative artificial intelligence like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a manner that its model only requires minutes to generate an answer, and can do so on a desktop computer – in strong contrast to the flagship models that governments have used for decades that can take hours to run and need some of the biggest high-performance systems in the world.

Professional Responses and Future Advances

Still, the reality that the AI could exceed previous top-tier traditional systems so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the world’s strongest storms.

“It’s astonishing,” commented James Franklin, a retired forecaster. “The sample is now large enough that it’s pretty clear this is not just beginner’s luck.”

Franklin said that although the AI is outperforming all other models on forecasting the trajectory of hurricanes worldwide this year, like many AI models it occasionally gets extreme strength forecasts wrong. It had difficulty with another storm previously, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

During the next break, Franklin stated he plans to talk with Google about how it can make the AI results even more helpful for experts by offering additional internal information they can utilize to evaluate the reasons it is coming up with its answers.

“A key concern that nags at me is that although these forecasts seem to be highly accurate, the output of the model is essentially a opaque process,” said Franklin.

Wider Sector Trends

There has never been a private, for-profit company that has produced a top-level forecasting system which grants experts a peek into its methods – in contrast to most other models which are offered free to the public in their entirety by the governments that designed and maintain them.

The company is not the only one in adopting AI to address challenging weather forecasting problems. The US and European governments are developing their respective AI weather models in the development phase – which have also shown better performance over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies taking swings at formerly difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they have secured US government funding to do so. One company, WindBorne Systems, is even deploying its proprietary atmospheric sensors to address deficiencies in the national monitoring system.