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

As the lead forecaster on duty, he forecasted that in a single day the weather system would become a severe hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had previously made such a bold forecast for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the form of Google’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon the AI system. During 25 October, Papin explained in his public discussion that the AI tool was a key factor for his confidence: “Roughly 40/50 AI ensemble members show Melissa reaching a Category 5 hurricane. While I am not ready to predict that intensity yet due to path variability, that is still plausible.

“It appears likely that a period of quick strengthening is expected as the storm moves slowly over very warm ocean waters which is the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Models

The AI model is the pioneer artificial intelligence system focused on tropical cyclones, and now the first to beat traditional meteorological experts at their own game. Through all tropical systems so far this year, Google’s model is the best – surpassing experts on track predictions.

The hurricane eventually made landfall in Jamaica at maximum strength, one of the strongest landfalls ever documented in almost 200 years of record-keeping across the region. Papin’s bold forecast likely gave people in Jamaica extra time to get ready for the catastrophe, possibly saving people and assets.

The Way The Model Works

Google’s model operates through spotting patterns that traditional lengthy scientific prediction systems may overlook.

“The AI performs much more quickly than their traditional counterparts, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a former forecaster.

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

Clarifying AI Technology

To be sure, the system is an instance 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 takes large datasets and pulls out patterns from them in a such a way that its system only requires minutes to come up with an result, and can operate on a standard PC – in sharp difference to the primary systems that governments have used for decades that can require many hours to run and require the largest supercomputers in the world.

Professional Responses and Future Developments

Still, the fact that the AI could outperform earlier gold-standard traditional systems so quickly is nothing short of amazing 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 data is now large enough that it’s evident this is not a case of chance.”

He noted that while the AI is outperforming all competing systems on predicting the future path of hurricanes globally this year, like many AI models it occasionally gets high-end intensity forecasts inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, he stated he intends to discuss with Google about how it can make the AI results more useful for forecasters by offering additional internal information they can utilize to assess exactly why it is producing its answers.

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

Broader Sector Trends

Historically, no a commercial entity that has produced a high-performance weather model which allows researchers a peek into its techniques – in contrast to nearly all other models which are offered free to the general audience in their full form by the authorities that created and operate them.

Google is not the only one in adopting AI to solve challenging weather forecasting problems. The authorities are developing their respective AI weather models in the development phase – which have also shown improved skill over previous traditional systems.

The next steps in artificial intelligence predictions appear to involve new firms taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of severe weather and flash flooding – and they are receiving federal support to do so. One company, WindBorne Systems, is also launching its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.