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When Tropical Storm Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a monster hurricane.

As the primary meteorologist on duty, he predicted that in just 24 hours the storm would become a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued this confident forecast for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s recently introduced DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa did become a system of astonishing strength that tore through Jamaica.

Increasing Reliance on AI Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his certainty: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa reaching a Category 5 hurricane. While I am unprepared to predict that strength at this time given path variability, that remains a possibility.

“It appears likely that a phase of quick strengthening is expected as the system drifts over very warm ocean waters which represent the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first artificial intelligence system focused on tropical cyclones, and now the first to outperform standard meteorological experts at their specialty. Across all tropical systems this season, the AI is the best – surpassing experts on path forecasts.

Melissa eventually made landfall in Jamaica at maximum strength, among the most powerful coastal impacts ever documented in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast likely gave residents extra time to prepare for the catastrophe, possibly saving people and assets.

The Way Google’s System Works

The AI system operates through spotting patterns that traditional lengthy scientific prediction systems may overlook.

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

“This season’s events has proven in quick time is that the newcomer artificial intelligence systems are on par with and, in some cases, more accurate than the less rapid physics-based forecasting tools we’ve relied upon,” he added.

Understanding Machine Learning

It’s important to note, Google DeepMind is an instance of AI training – a method that has been used in research fields like weather science for years – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes mounds of data and extracts trends from them in a such a way that its system only requires minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the flagship models that authorities have used for years that can take hours to run and require the largest supercomputers in the world.

Expert Reactions and Upcoming Developments

Nevertheless, the fact that Google’s model could outperform previous gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have spent their careers trying to predict the most intense weather systems.

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

He noted that although the AI is outperforming all other models on forecasting the trajectory of hurricanes worldwide this year, like many AI models it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty with another storm previously, as it was also undergoing quick strengthening to category 5 above the Caribbean.

During the next break, Franklin stated he plans to talk with Google about how it can enhance the AI results more useful for experts by providing additional internal information they can use to evaluate exactly why it is coming up with its conclusions.

“The one thing that nags at me is that while these predictions appear really, really good, the results of the model is kind of a black box,” remarked Franklin.

Broader Industry Developments

Historically, no a commercial entity that has developed a high-performance weather model which grants experts a peek into 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 artificial intelligence to address challenging weather forecasting problems. The US and European governments also have their respective AI weather models in the development phase – which have also shown improved skill over previous traditional systems.

The next steps in AI weather forecasts seem to be new firms tackling previously tough-to-solve problems such as long-range forecasts and better early alerts of tornado outbreaks and flash flooding – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also deploying its own weather balloons to fill the gaps in the US weather-observing network.