🔗 Share this article

When Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it was about to grow into a monster hurricane.

Serving as lead forecaster on duty, he predicted that in a single day the weather system would intensify into 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.

However, Papin possessed a secret advantage: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa did become a system of remarkable power that tore through Jamaica.

Growing Reliance on Artificial Intelligence 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 primary reason for his confidence: “Approximately 40/50 Google DeepMind simulation runs show Melissa reaching a Category 5 hurricane. While I am unprepared to forecast that strength yet given path variability, that remains a possibility.

“There is a high probability that a phase of rapid intensification will occur as the system drifts over very warm ocean waters which represent the highest oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the first AI model focused on hurricanes, and currently the initial to outperform traditional weather forecasters at their specialty. Through all 13 Atlantic storms this season, Google’s model is top-performing – surpassing experts on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 intensity, among the most powerful coastal impacts ever documented in almost 200 years of data collection across the region. Papin’s bold forecast probably provided people in Jamaica additional preparation time to prepare for the disaster, possibly saving people and assets.

The Way Google’s Model Works

Google’s model operates through spotting patterns that traditional time-intensive physics-based prediction systems may overlook.

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

“This season’s events has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in certain instances, superior 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 weather science for years – and is not creative artificial intelligence like ChatGPT.

AI training takes mounds of data 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 desktop computer – in sharp difference to the flagship models that governments have utilized for decades that can take hours to process and require the largest high-performance systems in the world.

Professional Responses and Upcoming Developments

Nevertheless, the reality that the AI could outperform earlier gold-standard legacy models so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the world’s strongest storms.

“It’s astonishing,” commented James Franklin, a former forecaster. “The sample is now large enough that it’s pretty clear this is not a case of chance.”

He noted that while the AI is beating all other models on predicting the future path of hurricanes worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts wrong. It struggled with another storm previously, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

During the next break, he stated he plans to talk with Google about how it can make the DeepMind output even more helpful for forecasters by offering extra internal information they can use to assess the reasons it is producing its answers.

“A key concern that troubles me is that although these forecasts seem to be really, really good, the results of the model is kind of a black box,” remarked Franklin.

Wider Sector Developments

Historically, no a private, for-profit company that has developed a high-performance weather model which grants experts a peek into its methods – unlike nearly all other models which are provided at no cost to the general audience in their full form by the authorities that designed and maintain them.

Google is not the only one in starting to use AI to solve challenging meteorological problems. The authorities are developing their respective artificial intelligence systems in the development phase – which have also shown improved skill over earlier non-AI versions.

The next steps in artificial intelligence predictions seem to be new firms taking swings at formerly difficult problems such as long-range forecasts and better advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also deploying its own atmospheric sensors to address deficiencies in the national monitoring system.