🔗 Share this article

As Developing Cyclone Melissa swirled off the coast 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 become a severe hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had ever issued such a bold forecast for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the form of the tech giant’s new DeepMind cyclone prediction system – launched for the initial occasion in June. True to the forecast, Melissa did become a system of astonishing strength that tore through Jamaica.

Growing Dependence on AI Predictions

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 Google DeepMind ensemble members show Melissa becoming a Category 5 hurricane. While I am not ready to predict that strength at this time due to path variability, that is still plausible.

“There is a high probability that a phase of rapid intensification is expected as the system moves slowly over very warm sea temperatures which represent the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the pioneer artificial intelligence system dedicated to tropical cyclones, and currently the first to outperform traditional weather forecasters at their specialty. Through all 13 Atlantic storms so far this year, the AI is top-performing – surpassing experts on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts ever documented in almost 200 years of data collection across the Atlantic basin. The confident prediction likely gave people in Jamaica additional preparation time to get ready for the disaster, potentially preserving people and assets.

The Way The System Functions

Google’s model operates through spotting patterns that conventional lengthy scientific prediction systems may miss.

“They do it much more quickly than their physics-based cousins, and the processing requirements is more affordable and time consuming,” 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 some cases, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” he said.

Clarifying AI Technology

It’s important to note, Google DeepMind is an example of machine learning – a technique that has been employed in research fields like meteorology for years – and is distinct from generative AI like ChatGPT.

AI training takes large datasets and extracts trends 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 strong contrast to the flagship models that governments have used for years that can require many hours to process and need some of the biggest high-performance systems in the world.

Professional Responses and Future Developments

Still, the fact that the AI could exceed earlier top-tier legacy models so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense weather systems.

“It’s astonishing,” commented James Franklin, a retired expert. “The sample is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

He said that although Google DeepMind is outperforming all competing systems on forecasting the future path of storms globally this year, similar to other systems it occasionally gets high-end intensity forecasts inaccurate. It struggled with Hurricane Erin previously, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

During the next break, he said he plans to talk with the company about how it can make the AI results more useful for forecasters by offering additional under-the-hood data they can utilize to assess exactly why 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 system is kind of a black box,” remarked Franklin.

Wider Industry Developments

There has never been a commercial entity that has developed a high-performance weather model which grants experts a view of its methods – in contrast to most other models which are offered free to the general audience in their entirety by the authorities that designed and maintain them.

Google is not alone in adopting artificial intelligence to solve challenging meteorological problems. The authorities also have their own AI weather models in the development phase – which have also shown improved skill over previous non-AI versions.

The next steps in AI weather forecasts appear to involve new firms taking swings at previously tough-to-solve problems such as long-range forecasts and improved early alerts of tornado outbreaks and sudden deluges – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is also deploying its own atmospheric sensors to address deficiencies in the US weather-observing network.