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When Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in a single day the storm would become a severe hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had ever issued this confident prediction for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the form of Google’s recently introduced DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a system of remarkable power that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that Google’s model was a key factor for his certainty: “Approximately 40/50 AI ensemble members show Melissa reaching a most intense hurricane. While I am unprepared to forecast that strength at this time due to track uncertainty, that is still plausible.

“There is a high probability that a period of quick strengthening will occur as the system drifts over very warm sea temperatures which is the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Models

The AI model is the first artificial intelligence system dedicated to hurricanes, and currently the initial to beat standard weather forecasters at their own game. Across all tropical systems this season, the AI is top-performing – even beating experts on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 strength, one of the strongest coastal impacts ever documented in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica additional preparation time to get ready for the disaster, possibly saving lives and property.

How Google’s System Works

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

“The AI performs far faster than their traditional counterparts, and the computing power is more affordable and time consuming,” said Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in quick time is that the recent artificial intelligence systems are competitive with and, in certain instances, superior than the less rapid traditional weather models we’ve traditionally leaned on,” he said.

Clarifying Machine Learning

To be sure, Google DeepMind is an instance of AI training – a technique that has been used in research fields like meteorology for a long time – and is distinct from generative AI like ChatGPT.

AI training 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 answer, and can do so on a standard PC – in sharp difference to the primary systems that governments have utilized for years that can take hours to run and require the largest supercomputers in the world.

Expert Reactions and Upcoming Advances

Nevertheless, the fact that the AI could outperform previous gold-standard legacy models so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to predict the most intense storms.

“I’m impressed,” commented James Franklin, a retired expert. “The sample is now large enough that it’s evident this is not a case of beginner’s luck.”

Franklin noted that while the AI is outperforming all other models on predicting the future path of storms globally this year, similar to other systems it occasionally gets extreme strength forecasts inaccurate. It had difficulty with another storm previously, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, Franklin stated he plans to talk with the company about how it can make the AI results more useful for forecasters by offering extra internal information they can use to assess exactly why it is producing its conclusions.

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

Wider Industry Trends

Historically, no a commercial entity that has developed a high-performance forecasting system which allows researchers a view of its techniques – unlike nearly all systems which are provided free to the general audience in their full form by the authorities that created and operate them.

Google is not the only one in starting to use AI to solve challenging weather forecasting problems. The authorities also have their own AI weather models in the works – which have demonstrated improved skill over earlier traditional systems.

The next steps in artificial intelligence predictions appear to involve startup companies tackling formerly tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of severe weather and flash flooding – and they have secured US government funding to do so. One company, WindBorne Systems, is also deploying its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.