🔗 Share this article

When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane.

Serving as lead forecaster on duty, he forecasted that in a single day the storm would intensify into a category 4 hurricane and begin a turn towards the Jamaican shoreline. No forecaster had ever issued such a bold prediction for rapid strengthening.

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

Growing Reliance on Artificial Intelligence Predictions

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin explained in his official briefing that the AI tool was a key factor for his certainty: “Roughly 40/50 AI ensemble members indicate Melissa reaching a Category 5 storm. Although I am unprepared to forecast that intensity at this time due to path variability, that is still plausible.

“It appears likely that a period of rapid intensification is expected as the system moves slowly over exceptionally hot sea temperatures which is the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the pioneer artificial intelligence system focused on hurricanes, and now the first to beat traditional weather forecasters at their specialty. Across all tropical systems this season, Google’s model 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 recorded in nearly two centuries of data collection across the region. The confident prediction likely gave residents extra time to prepare for the catastrophe, potentially preserving people and assets.

The Way Google’s Model Functions

The AI system operates through identifying trends that conventional time-intensive scientific prediction systems may miss.

“The AI performs far faster than their traditional counterparts, and the computing power is more affordable and demanding,” said Michael Lowry, a former meteorologist.

“This season’s events has proven in quick time is that the recent artificial intelligence systems are competitive with and, in certain instances, more accurate than the less rapid physics-based weather models we’ve traditionally leaned on,” he added.

Clarifying Machine Learning

To be sure, Google DeepMind is an example of AI training – a technique that has been employed in research fields like meteorology for years – and is not creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a such a way that its model only requires minutes to generate an answer, and can operate on a desktop computer – in strong contrast to the primary systems that authorities have used for decades that can require many hours to process and require some of the biggest supercomputers in the world.

Expert Reactions and Upcoming Advances

Nevertheless, the reality that Google’s model could exceed earlier gold-standard legacy models so quickly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the most intense weather systems.

“It’s astonishing,” commented James Franklin, a former forecaster. “The data is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

Franklin said that although Google DeepMind is outperforming all competing systems on forecasting the future path of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity predictions wrong. It struggled with another storm 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 internal information they can utilize to assess exactly why it is producing its answers.

“The one thing that nags at me is that although these predictions seem to be highly accurate, the results of the system is kind of a black box,” said Franklin.

Broader Sector Trends

Historically, no a private, for-profit company that has produced a top-level forecasting system which grants experts a peek into its methods – unlike nearly all other models 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 alone in starting to use artificial intelligence to solve challenging meteorological problems. The authorities are developing their own AI weather models in the works – which have demonstrated better performance over earlier non-AI versions.

Future developments in AI weather forecasts seem to be startup companies tackling formerly difficult problems such as long-range forecasts and improved advance warnings of tornado outbreaks and flash flooding – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also launching its proprietary atmospheric sensors to fill the gaps in the national monitoring system.