🔗 Share this article

As Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in a single day the weather system would intensify into a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had previously made such a bold prediction for rapid strengthening.

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

Growing Reliance on Artificial Intelligence Predictions

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his confidence: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa reaching a most intense hurricane. Although I am not ready to predict that strength at this time due to path variability, that remains a possibility.

“It appears likely that a phase of quick strengthening will occur as the storm moves slowly over exceptionally hot ocean waters which represent the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Models

The AI model is the first artificial intelligence system focused on tropical cyclones, and now the initial to outperform standard meteorological experts at their own game. Across all 13 Atlantic storms so far this year, the AI is the best – surpassing human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls recorded in almost 200 years of data collection across the region. The confident prediction probably provided people in Jamaica additional preparation time to get ready for the disaster, potentially preserving lives and property.

How Google’s Model Works

Google’s model operates through identifying trends that traditional lengthy scientific prediction systems may miss.

“The AI performs much more quickly than their traditional counterparts, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a former forecaster.

“This season’s events has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in certain instances, more accurate than the less rapid traditional weather models we’ve relied upon,” he added.

Understanding AI Technology

It’s important to note, the system is an instance of AI training – a method that has been employed in research fields like meteorology for years – and is not generative AI like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a such a way that its system only requires minutes to come up with an result, and can operate on a standard PC – in sharp difference to the primary systems that authorities have utilized for years that can require many hours to process and need some of the biggest supercomputers in the world.

Expert Responses and Upcoming Advances

Still, the fact that Google’s model could exceed earlier top-tier legacy models so rapidly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

“I’m impressed,” said James Franklin, a retired forecaster. “The data is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

Franklin noted that while Google DeepMind is outperforming all other models on forecasting the trajectory of storms globally this year, similar to other systems it sometimes errs on high-end intensity forecasts wrong. It struggled with another storm earlier this year, as it was also undergoing quick strengthening to category 5 above the Caribbean.

In the coming offseason, he said he plans to discuss with Google about how it can make the DeepMind output more useful for forecasters by offering extra under-the-hood data they can use to evaluate exactly why it is coming up with its conclusions.

“A key concern that troubles me is that although these forecasts appear really, really good, the output of the system is essentially a black box,” remarked Franklin.

Wider Industry Trends

Historically, no a private, for-profit company that has developed a high-performance 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 adopting AI to address challenging weather forecasting problems. The authorities also have their respective AI weather models in the development phase – which have demonstrated improved skill over previous non-AI versions.

The next steps in AI weather forecasts seem to be startup companies taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of severe weather and sudden deluges – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is also launching its own weather balloons to address deficiencies in the US weather-observing network.