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When Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a monster hurricane.

As the lead forecaster on duty, he forecasted that in a single day the weather system would intensify into a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had previously made this confident forecast for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the form of Google’s new DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Increasing Dependence on AI Predictions

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Roughly 40/50 Google DeepMind ensemble members show Melissa becoming a most intense storm. While I am unprepared to forecast that intensity yet due to track uncertainty, that is still plausible.

“There is a high probability that a phase of quick strengthening is expected as the system moves slowly over very warm ocean waters which is the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the pioneer artificial intelligence system dedicated to tropical cyclones, and currently the initial to outperform traditional weather forecasters at their specialty. Through all tropical systems this season, the AI is the best – surpassing human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at maximum intensity, among the most powerful landfalls recorded in nearly two centuries of data collection across the region. Papin’s bold forecast likely gave residents extra time to prepare for the disaster, potentially preserving people and assets.

The Way The System Functions

Google’s model works by identifying trends that conventional lengthy physics-based weather models may overlook.

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

“This season’s events has proven in quick time is that the recent AI weather models are competitive with and, in certain instances, more accurate than the less rapid physics-based weather models we’ve relied upon,” he said.

Clarifying Machine Learning

To be sure, Google DeepMind is an instance of AI training – a technique that has been used in research fields like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

AI training processes large datasets 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 operate on a standard PC – in strong contrast to the flagship models that governments have used for decades that can take hours to process and need the largest high-performance systems in the world.

Professional Reactions and Future Advances

Nevertheless, the fact that the AI could outperform previous top-tier legacy models so quickly is truly remarkable to meteorologists who have spent their careers trying to predict the world’s strongest weather systems.

“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.”

He said that while the AI is outperforming all other models on predicting the trajectory of hurricanes worldwide this year, similar to other systems it occasionally gets high-end intensity forecasts wrong. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

During the next break, he stated he intends to discuss with the company about how it can make the DeepMind output more useful for experts by providing additional internal information they can utilize to assess exactly why it is producing its conclusions.

“A key concern that nags at me is that while these predictions appear highly accurate, the results of the model is kind of a opaque process,” said Franklin.

Broader Sector Trends

Historically, no a private, for-profit company that has developed a high-performance weather model which grants experts a peek into its methods – in contrast to most systems which are offered 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 difficult meteorological problems. The US and European governments are developing their own artificial intelligence systems in the works – which have also shown better performance over earlier non-AI versions.

Future developments in AI weather forecasts appear to involve startup companies tackling formerly tough-to-solve problems such as long-range forecasts and better early alerts of tornado outbreaks and sudden deluges – and they have secured US government funding to pursue this. One company, WindBorne Systems, is also deploying its proprietary weather balloons to address deficiencies in the national monitoring system.