🔗 Share this article

When Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a monster hurricane.

Serving as lead forecaster on duty, he predicted that in a single day the storm would intensify into a category 4 hurricane and start shifting towards the Jamaican shoreline. No forecaster had ever issued this confident prediction for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his certainty: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa becoming a most intense storm. Although I am not ready to predict that intensity yet due to path variability, that is still plausible.

“It appears likely that a period of rapid intensification will occur as the system drifts over exceptionally hot ocean waters which represent the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the first AI model focused on tropical cyclones, and now the first to outperform traditional meteorological experts at their own game. Across all tropical systems so far this year, Google’s model is the best – even beating human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 strength, one of the strongest landfalls recorded in almost 200 years of record-keeping across the region. The confident prediction likely gave people in Jamaica additional preparation time to prepare for the catastrophe, potentially preserving people and assets.

How The System Works

The AI system operates through identifying trends that traditional lengthy scientific prediction systems may miss.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in short order is that the recent AI weather models are competitive with and, in some cases, superior than the less rapid traditional weather models we’ve relied upon,” Lowry added.

Understanding AI Technology

To be sure, the system is an instance of AI training – a method that has been used in data-heavy sciences like meteorology for years – and is distinct from creative artificial intelligence like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a manner that its model only takes a few minutes to generate an answer, and can operate on a standard PC – in strong contrast to the flagship models that governments have utilized for decades that can take hours to run and require the largest supercomputers in the world.

Expert Responses and Upcoming Advances

Still, the fact that the AI could exceed earlier gold-standard legacy models so rapidly is truly remarkable to meteorologists who have spent their careers trying to predict the world’s strongest weather systems.

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

He said that while Google DeepMind is beating all other models on predicting the future path of hurricanes worldwide this year, similar to other systems it sometimes errs on extreme strength predictions inaccurate. It struggled with another storm previously, as it was also undergoing quick strengthening to category 5 above the Caribbean.

In the coming offseason, Franklin stated he plans to discuss with Google about how it can make the DeepMind output more useful for experts 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 troubles me is that while these forecasts seem to be really, really good, the results of the model is kind of a black box,” remarked Franklin.

Broader Sector Developments

Historically, no a commercial entity that has developed a high-performance forecasting system which allows researchers a view of its techniques – in contrast to most other models which are provided free to the general audience in their entirety by the governments that created and operate them.

Google is not the only one in adopting artificial intelligence to address difficult weather forecasting problems. The US and European governments are developing their respective AI weather models in the development phase – which have demonstrated improved skill over earlier traditional systems.

The next steps in AI weather forecasts seem to be new firms taking swings at previously difficult problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also launching its own atmospheric sensors to fill the gaps in the national monitoring system.