🔗 Share this article

When Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it was about to escalate to a monster hurricane.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the weather system would become a category 4 hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had ever issued this confident forecast for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa did become a system of remarkable power that tore through Jamaica.

Growing Reliance on AI Forecasting

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his certainty: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa reaching a Category 5 hurricane. Although I am unprepared to predict that strength yet due to track uncertainty, that is still plausible.

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

Surpassing Conventional Models

The AI model is the pioneer artificial intelligence system focused on tropical cyclones, and now the first to outperform standard meteorological experts at their own game. Through all tropical systems this season, the AI is the best – surpassing experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 strength, one of the strongest landfalls recorded in nearly two centuries of data collection across the region. The confident prediction likely gave residents extra time to prepare for the disaster, possibly saving people and assets.

The Way Google’s System Functions

The AI system works by identifying trends that conventional time-intensive physics-based weather models may overlook.

“They do it far faster than their physics-based cousins, and the computing power is less expensive and time consuming,” stated Michael Lowry, a ex meteorologist.

“This season’s events has demonstrated in quick time is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the less rapid physics-based weather models we’ve relied upon,” he said.

Understanding AI Technology

To be sure, Google DeepMind is an instance of AI training – a method that has been used in data-heavy sciences like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a manner that its model only takes a few minutes to generate an result, and can operate on a standard PC – in strong contrast to the primary systems that authorities have utilized for decades that can take hours to run and require some of the biggest high-performance systems in the world.

Expert Reactions and Future Advances

Still, the reality that the AI could outperform previous top-tier legacy models so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the most intense storms.

“It’s astonishing,” commented James Franklin, a retired forecaster. “The sample is sufficient that it’s pretty clear this is not just chance.”

Franklin said that while Google DeepMind is outperforming all competing systems on predicting the trajectory of storms worldwide this year, similar to other systems it occasionally gets extreme strength predictions inaccurate. It had difficulty with Hurricane Erin previously, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, he said he plans to discuss with Google about how it can enhance the DeepMind output more useful for forecasters by providing extra under-the-hood data they can utilize to evaluate the reasons it is coming up with its answers.

“The one thing that nags at me is that while these forecasts appear highly accurate, the output of the system is essentially a black box,” remarked Franklin.

Wider Sector Trends

There has never been a private, for-profit company that has produced a high-performance weather model which grants experts a view of its methods – unlike nearly all systems which are provided at no cost to the general audience in their entirety by the authorities that created and operate them.

Google is not the only one in starting to use artificial intelligence to address difficult weather forecasting problems. The authorities are developing their own artificial intelligence systems in the works – which have also shown better performance over earlier traditional systems.

Future developments in artificial intelligence predictions appear to involve startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of severe weather and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also launching its proprietary atmospheric sensors to fill the gaps in the national monitoring system.