🔗 Share this article

As Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it was about to grow into a monster hurricane.

As the primary meteorologist on duty, he predicted that in a single day the weather system would become a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued such a bold prediction for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the guise of Google’s recently introduced DeepMind hurricane model – released for the initial occasion in June. True to the forecast, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his confidence: “Approximately 40/50 Google DeepMind ensemble members show Melissa becoming a most intense hurricane. Although I am unprepared to predict that strength at this time due to track uncertainty, that is still plausible.

“There is a high probability that a phase of quick strengthening will occur as the system moves slowly over very warm ocean waters which is the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first artificial intelligence system dedicated to hurricanes, and currently the initial to beat standard meteorological experts at their own game. Through all 13 Atlantic storms so far this year, Google’s model is top-performing – surpassing experts on track predictions.

The hurricane ultimately struck in Jamaica at maximum strength, among the most powerful coastal impacts recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction probably provided people in Jamaica extra time to prepare for the catastrophe, potentially preserving lives and property.

How Google’s Model Works

The AI system operates through identifying trends that traditional time-intensive physics-based weather models may miss.

“The AI performs far faster than their traditional counterparts, and the processing requirements is more affordable and demanding,” said Michael Lowry, a former forecaster.

“This season’s events has proven in short order is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the less rapid traditional forecasting tools we’ve relied upon,” Lowry said.

Understanding AI Technology

It’s important to note, the system is an instance of machine learning – a method that has been employed in data-heavy sciences like weather science for years – and is distinct from generative AI like ChatGPT.

Machine learning takes large datasets 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 desktop computer – in sharp difference to the flagship models that authorities have utilized for years that can require many hours to run and need some of the biggest high-performance systems in the world.

Professional Reactions and Upcoming Advances

Nevertheless, the reality that Google’s model could outperform earlier top-tier legacy models so rapidly is truly remarkable to weather scientists who have spent their careers trying to predict the most intense weather systems.

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

He noted that while the AI is beating all competing systems on predicting the trajectory of storms worldwide this year, like many AI models it sometimes errs on extreme strength predictions wrong. It struggled with another storm earlier this year, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

During the next break, he said he plans to talk with the company about how it can enhance the AI results more useful for forecasters by offering extra internal information they can utilize to assess exactly why it is producing its conclusions.

“The one thing that nags at me is that although these predictions appear really, really good, the results of the system is kind of a opaque process,” remarked Franklin.

Broader Industry Developments

There has never been a commercial entity that has produced a top-level forecasting system which allows researchers a peek into its methods – unlike most systems which are offered at no cost to the general audience in their full form by the governments that designed and maintain them.

The company is not the only one in starting to use AI to address challenging weather forecasting problems. The US and European governments also have their respective AI weather models 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 tackling formerly tough-to-solve problems such as long-range forecasts and better advance warnings of tornado outbreaks and sudden deluges – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is even deploying its own weather balloons to address deficiencies in the US weather-observing network.