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

As the lead forecaster on duty, he forecasted that in a single day the weather system would become a category 4 hurricane and start shifting towards the coast of Jamaica. Not a single expert had ever issued this confident forecast for rapid strengthening.

However, Papin possessed a secret advantage: AI technology 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 did become a system of astonishing strength that ravaged Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Forecasters are heavily relying upon 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 certainty: “Roughly 40/50 AI simulation runs show Melissa becoming a most intense hurricane. While I am not ready to predict that intensity at this time due to path variability, that is still plausible.

“It appears likely that a phase of quick strengthening is expected as the system moves slowly over very warm sea temperatures which is the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the pioneer AI model dedicated to hurricanes, and currently the first to beat traditional meteorological experts at their own game. Across all 13 Atlantic storms this season, the AI is top-performing – even beating human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 intensity, among the most powerful coastal impacts ever documented in nearly two centuries of record-keeping across the region. The confident prediction probably provided people in Jamaica extra time to get ready for the disaster, possibly saving people and assets.

The Way The System Works

Google’s model works by identifying trends that conventional time-intensive scientific prediction systems may overlook.

“They do it much more quickly than their physics-based cousins, and the computing power is more affordable and demanding,” stated 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 certain instances, more accurate than the less rapid physics-based weather models we’ve traditionally leaned on,” he added.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of machine learning – a method that has been employed in research fields like meteorology for years – and is distinct from generative AI like ChatGPT.

Machine learning takes large datasets and pulls out patterns from them in a manner that its system only takes a few minutes to come up with an result, and can operate on a desktop computer – in strong contrast to the flagship models that authorities have utilized for decades that can take hours to process and need the largest high-performance systems in the world.

Expert Reactions and Upcoming Developments

Nevertheless, the reality that Google’s model could outperform previous gold-standard traditional systems so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the most intense weather systems.

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

Franklin noted that although the AI is outperforming all competing systems on forecasting the future path of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts wrong. It struggled with Hurricane Erin previously, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, he stated he plans to talk with the company about how it can enhance the AI results even more helpful for forecasters by providing extra under-the-hood data they can use to assess the reasons it is producing its answers.

“A key concern that troubles me is that while these forecasts seem to be highly accurate, the output of the model is essentially a black box,” remarked Franklin.

Broader Sector Developments

There has never been a commercial entity that has developed a high-performance forecasting system which grants experts a view of its techniques – unlike nearly all systems which are offered free to the public in their full form by the governments that created and operate them.

The company is not the only one in adopting artificial intelligence to solve challenging meteorological problems. The US and European governments also have their respective AI weather models in the works – which have also shown better performance over previous traditional systems.

The next steps in artificial intelligence predictions appear to involve startup companies tackling formerly difficult problems such as sub-seasonal outlooks and better early alerts of severe weather and flash flooding – and they have secured 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.