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

As the primary meteorologist on duty, he predicted that in just 24 hours the storm would intensify into a severe hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made such a bold prediction for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the form of Google’s new DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa did become a system of remarkable power that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 AI simulation runs show Melissa reaching a Category 5 hurricane. While I am unprepared to predict that strength yet given track uncertainty, that remains a possibility.

“There is a high probability that a phase of rapid intensification will occur as the storm moves slowly over exceptionally hot sea temperatures which represent the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the pioneer artificial intelligence system focused on hurricanes, and now the first to beat standard meteorological experts at their own game. Through all tropical systems this season, Google’s model is the best – surpassing human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 strength, among the most powerful landfalls ever documented in nearly two centuries of record-keeping across the region. Papin’s bold forecast probably provided people in Jamaica additional preparation time to prepare for the catastrophe, possibly saving lives and property.

How The System Works

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

“They do it far faster than their physics-based cousins, and the computing power is less expensive and time consuming,” said Michael Lowry, a ex meteorologist.

“This season’s events has proven in quick time is that the newcomer artificial intelligence systems are competitive with and, in some cases, more accurate than the less rapid traditional weather models we’ve traditionally leaned on,” Lowry said.

Clarifying AI Technology

It’s important to note, the system is an instance of AI training – a technique that has been used in data-heavy sciences like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a such a way that its model only takes a few minutes to generate an answer, and can operate on a standard PC – in sharp difference to the flagship models that governments have utilized for decades that can require many hours to process and need the largest high-performance systems in the world.

Expert Responses and Upcoming Developments

Nevertheless, the reality that Google’s model could exceed previous gold-standard traditional systems so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the most intense weather systems.

“It’s astonishing,” said James Franklin, a former expert. “The sample is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

Franklin noted that although the AI is beating all other models on predicting the trajectory of storms globally this year, like many AI models it sometimes errs on extreme strength forecasts wrong. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification 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 AI results more useful for forecasters by providing additional under-the-hood data they can utilize to assess the reasons it is producing its conclusions.

“A key concern that troubles me is that although these forecasts appear highly accurate, the results of the model is essentially a black box,” remarked Franklin.

Broader Sector Developments

Historically, no a private, for-profit company that has produced a top-level forecasting system which allows researchers a peek into its techniques – unlike most systems which are provided free to the public in their full form by the authorities that created and operate them.

Google is not the only one in starting to use artificial intelligence to solve difficult meteorological problems. The authorities are developing their respective artificial intelligence systems in the works – which have also shown improved skill over previous traditional systems.

Future developments in AI weather forecasts appear to involve new firms tackling previously tough-to-solve problems such as long-range forecasts and improved early alerts of severe weather and sudden deluges – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is also launching its own atmospheric sensors to fill the gaps in the US weather-observing network.