🔗 Share this article

When Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a monster hurricane.

As the lead forecaster on duty, he forecasted 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 such a bold forecast for rapid strengthening.

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

Increasing Dependence on AI Forecasting

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his confidence: “Approximately 40/50 Google DeepMind ensemble members show Melissa becoming a Category 5 hurricane. Although I am not ready to forecast that intensity yet given track uncertainty, that is still plausible.

“It appears likely that a period of quick strengthening is expected as the storm drifts over very warm ocean waters which is the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

The AI model is the first AI model focused on hurricanes, and currently the initial to outperform standard weather forecasters at their specialty. Through all 13 Atlantic storms this season, the AI is top-performing – surpassing experts on track predictions.

The hurricane eventually made landfall in Jamaica at maximum strength, one of the strongest coastal impacts ever documented in nearly two centuries of record-keeping across the region. Papin’s bold forecast probably provided residents extra time to prepare for the catastrophe, possibly saving lives and property.

How Google’s Model Works

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

“They do it much more quickly than their traditional counterparts, and the computing power is more affordable and time consuming,” said Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in short order is that the recent artificial intelligence systems are competitive with and, in certain instances, more accurate than the slower traditional weather models we’ve traditionally leaned on,” he added.

Clarifying AI Technology

To be sure, the system is an example of machine learning – a technique that has been used in research fields like meteorology for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a such a way that its model only takes a few minutes to come up with an result, and can operate on a desktop computer – in sharp difference to the flagship models that governments have utilized for years that can require many hours to run and require the largest high-performance systems in the world.

Professional Responses and Upcoming Advances

Still, the reality that Google’s model could exceed previous top-tier traditional systems so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the most intense storms.

“It’s astonishing,” said James Franklin, a retired expert. “The sample is now large enough that it’s evident this is not just beginner’s luck.”

He noted that although the AI is outperforming all competing systems on forecasting the trajectory of hurricanes worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty with another storm previously, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he stated he intends to discuss with the company about how it can enhance the DeepMind output more useful for experts by providing additional internal information they can use to evaluate exactly why it is coming up with its conclusions.

“The one thing that troubles me is that while these forecasts seem to be really, really good, the results of the system is essentially a opaque process,” remarked Franklin.

Broader Industry Trends

Historically, no a private, for-profit company that has developed a high-performance forecasting system which allows researchers a view of its techniques – in contrast to most other models which are offered free to the public in their full form by the governments that designed and maintain them.

Google is not alone in adopting artificial intelligence to address difficult meteorological problems. The authorities are developing their own artificial intelligence systems in the works – which have also shown improved skill over previous non-AI versions.

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 better advance warnings of tornado outbreaks and flash flooding – and they have secured US government funding to pursue this. One company, WindBorne Systems, is even deploying its own atmospheric sensors to fill the gaps in the US weather-observing network.