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As Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it was about to grow into a major tropical system.

As the lead forecaster on duty, he forecasted that in a single day the weather system would become a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had ever issued such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa did become a system of remarkable power that ravaged Jamaica.

Increasing Reliance on AI Forecasting

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his confidence: “Approximately 40/50 Google DeepMind ensemble members show Melissa reaching a Category 5 storm. While I am not ready to forecast that intensity yet due to path variability, that is still plausible.

“It appears likely that a period of rapid intensification will occur as the storm moves slowly over very warm sea temperatures which is the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the pioneer artificial intelligence system dedicated to tropical cyclones, and now the first to outperform traditional meteorological experts at their specialty. Across all 13 Atlantic storms this season, Google’s model is the best – surpassing human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum strength, one of the strongest coastal impacts recorded in almost 200 years of record-keeping across the region. Papin’s bold forecast probably provided people in Jamaica extra time to prepare for the catastrophe, possibly saving people and assets.

The Way The System Functions

Google’s model operates through identifying trends that conventional time-intensive physics-based weather models may overlook.

“The AI performs much more quickly than their traditional counterparts, and the computing power is less expensive and demanding,” said Michael Lowry, a former forecaster.

“What this hurricane season has proven in short order is that the recent artificial intelligence systems are on par with and, in certain instances, more accurate than the less rapid traditional forecasting tools we’ve traditionally leaned on,” he said.

Understanding Machine Learning

It’s important to note, Google DeepMind is an example of machine learning – a method that has been used in research fields like meteorology 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 takes a few minutes to come up with an result, and can do so on a standard PC – in strong contrast to the flagship models that governments have utilized for decades that can take hours to run and require the largest high-performance systems in the world.

Expert Responses and Upcoming Developments

Nevertheless, the fact that the AI could exceed earlier top-tier legacy models so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the world’s strongest storms.

“It’s astonishing,” commented James Franklin, a former forecaster. “The sample is sufficient that it’s evident this is not a case of chance.”

Franklin noted that although the AI is outperforming all competing systems on predicting the trajectory of hurricanes worldwide this year, similar to other systems it occasionally gets extreme strength forecasts wrong. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

During the next break, he said he plans to discuss with the company about how it can make the DeepMind output more useful for forecasters by offering additional under-the-hood data they can use to evaluate exactly why it is producing its answers.

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

Broader Sector Trends

Historically, no a private, for-profit company that has produced a top-level forecasting system which grants experts a view of its techniques – unlike nearly all other models which are offered free to the general audience in their full form by the authorities that created and operate them.

The company is not alone in starting to use AI to solve difficult weather forecasting problems. The US and European governments are developing their own AI weather models in the works – which have also shown better performance over previous traditional systems.

The next steps in AI weather forecasts appear to involve new firms tackling formerly tough-to-solve problems such as long-range forecasts and improved early alerts of severe weather and flash flooding – and they are receiving US government funding to do so. One company, WindBorne Systems, is also launching its own weather balloons to address deficiencies in the US weather-observing network.