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As Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a monster hurricane.

As the lead forecaster on duty, he predicted that in just 24 hours the weather system would become a category 4 hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had previously made this confident prediction for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s new DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa did become a system of remarkable power that tore through Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his confidence: “Roughly 40/50 AI ensemble members show Melissa reaching a Category 5 storm. Although I am unprepared to forecast that intensity at this time due to path variability, that remains a possibility.

“It appears likely that a phase of rapid intensification is expected as the storm moves slowly over very warm sea temperatures which is the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the first artificial intelligence system dedicated to tropical cyclones, and currently the initial to beat standard weather forecasters at their specialty. Across all tropical systems this season, Google’s model is top-performing – even beating experts on track predictions.

The hurricane ultimately struck in Jamaica at maximum intensity, among the most powerful landfalls ever documented in nearly two centuries of data collection across the region. The confident prediction probably provided people in Jamaica additional preparation time to prepare for the catastrophe, potentially preserving lives and property.

The Way The System Functions

The AI system works by identifying trends that traditional time-intensive scientific weather models may overlook.

“They do it much more quickly than their traditional counterparts, and the computing power is more affordable and demanding,” said Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in short order is that the newcomer artificial intelligence systems are competitive with and, in some cases, more accurate than the less rapid traditional forecasting tools we’ve traditionally leaned on,” he said.

Understanding AI Technology

To be sure, the system is an instance of machine learning – a technique that has been employed in research fields like weather science for years – and is not generative AI like ChatGPT.

AI training takes mounds of data and extracts trends from them in a such a way that its model only takes a few minutes to come up with an result, and can do so on a standard PC – in strong contrast to the primary systems that governments have used for decades that can take hours to run and need the largest supercomputers in the world.

Expert Responses and Upcoming Developments

Nevertheless, the reality that Google’s model could outperform earlier top-tier legacy models so rapidly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest weather systems.

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

Franklin noted that while the AI is outperforming all competing systems on forecasting the future path of hurricanes globally this year, like many AI models it occasionally gets extreme strength predictions wrong. It struggled with Hurricane Erin previously, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he said he intends to talk with the company about how it can enhance the DeepMind output even more helpful for experts by offering additional internal information they can utilize to assess the reasons it is producing its conclusions.

“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,” remarked Franklin.

Wider Sector Trends

There has never been a commercial entity that has developed a high-performance weather model which grants experts a view of its techniques – unlike nearly all other models which are provided at no cost to the public in their full form by the governments that designed and maintain them.

The company is not alone in adopting AI to address difficult weather forecasting problems. The US and European governments are developing their own AI weather models in the development phase – which have also shown better performance over previous non-AI versions.

The next steps in artificial intelligence predictions appear to involve startup companies tackling previously difficult problems such as long-range forecasts and improved advance warnings of tornado outbreaks and flash flooding – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is even deploying its proprietary atmospheric sensors to fill the gaps in the national monitoring system.