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As Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a major tropical system.

As the primary meteorologist on duty, he forecasted that in a single day the storm would become a severe hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had previously made this confident forecast for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s recently introduced DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a system of remarkable power that tore through Jamaica.

Increasing Dependence on AI Predictions

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his confidence: “Roughly 40/50 AI simulation runs indicate Melissa becoming a Category 5 storm. Although I am not ready to predict that strength at this time due to path variability, that remains a possibility.

“There is a high probability that a phase of rapid intensification will occur as the storm moves slowly over exceptionally hot sea temperatures which represent the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the pioneer artificial intelligence system focused on hurricanes, and now the first to outperform standard weather forecasters at their own game. Through all 13 Atlantic storms so far this year, the AI is top-performing – surpassing human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at category 5 strength, among the most powerful coastal impacts ever documented in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica additional preparation time to prepare for the disaster, possibly saving people and assets.

The Way Google’s Model Functions

Google’s model operates through spotting patterns that conventional lengthy physics-based prediction systems may miss.

“They do it much more quickly than their traditional counterparts, and the computing power is less expensive and time consuming,” said Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in short order is that the recent AI weather models are competitive with and, in certain instances, superior than the slower physics-based forecasting tools we’ve relied upon,” he said.

Understanding AI Technology

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

AI training takes 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 answer, and can operate on a desktop computer – in sharp difference to the primary systems that governments have used for decades that can take hours to process and need the largest supercomputers in the world.

Professional Reactions and Future Developments

Still, the fact that Google’s model could outperform earlier gold-standard legacy models so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to predict the world’s strongest weather systems.

“It’s astonishing,” said James Franklin, a retired expert. “The sample is now large enough that it’s pretty clear this is not a case of chance.”

Franklin said that while Google DeepMind is beating all competing systems on predicting the trajectory of hurricanes worldwide this year, similar to other systems it occasionally gets extreme strength predictions inaccurate. It had difficulty with another storm previously, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he said he intends to discuss with Google about how it can enhance the DeepMind output even more helpful for forecasters by providing additional under-the-hood data they can use to assess the reasons it is coming up with its answers.

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

Wider Industry Trends

There has never been a commercial entity that has produced a top-level weather model which grants experts a view of its methods – in contrast to most systems which are provided at no cost to the general audience in their entirety by the governments that created and operate them.

The company is not the only one in adopting artificial intelligence to address challenging meteorological problems. The US and European governments are developing their respective AI weather models in the works – which have demonstrated improved skill over previous non-AI versions.

The next steps in artificial intelligence predictions seem to be startup companies tackling formerly tough-to-solve problems such as long-range forecasts and better early alerts of tornado outbreaks and flash flooding – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is also deploying its proprietary atmospheric sensors to address deficiencies in the national monitoring system.