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As Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it was about to grow into a monster hurricane.

As the lead forecaster on duty, he predicted that in just 24 hours the weather system would become a category 4 hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had ever issued this confident prediction for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the form of the tech giant’s recently introduced DeepMind hurricane model – launched for the initial occasion in June. True to the forecast, Melissa did become a system of remarkable power that ravaged Jamaica.

Increasing Reliance on AI Forecasting

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his certainty: “Roughly 40/50 AI ensemble members show Melissa becoming a most intense storm. Although I am not ready to predict that strength at this time due to path variability, that is still plausible.

“There is a high probability that a period of quick strengthening is expected as the system moves slowly over very warm sea temperatures which is the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Models

Google DeepMind is the pioneer artificial intelligence system dedicated to hurricanes, and currently the initial to beat standard meteorological experts at their specialty. Across all 13 Atlantic storms so far this year, the AI is the best – even beating human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum strength, one of the strongest landfalls ever documented in almost 200 years of record-keeping across the region. Papin’s bold forecast likely gave residents extra time to prepare for the disaster, possibly saving people and assets.

The Way Google’s Model Works

Google’s model operates through identifying trends that traditional lengthy physics-based prediction systems may miss.

“They do it much more quickly than their physics-based cousins, and the computing power is more affordable and demanding,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in quick time is that the recent artificial intelligence systems are competitive with and, in certain instances, more accurate than the less rapid traditional forecasting tools we’ve relied upon,” Lowry said.

Clarifying Machine Learning

To be sure, Google DeepMind is an instance of AI training – a method that has been used in data-heavy sciences like weather science for years – and is not generative AI like ChatGPT.

AI training takes mounds of data 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 do so on a standard PC – in strong contrast to the primary systems that authorities have used for decades that can take hours to process and need the largest high-performance systems in the world.

Expert Responses and Upcoming Advances

Still, the fact that Google’s model could outperform earlier top-tier legacy models so quickly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

“I’m impressed,” commented James Franklin, a former forecaster. “The data is sufficient that it’s evident this is not just chance.”

Franklin noted that while the AI is outperforming all competing systems on forecasting the future path of hurricanes globally this year, like many AI models it sometimes errs on extreme strength predictions wrong. It struggled with another storm previously, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, Franklin said he intends to discuss with Google about how it can enhance the DeepMind output more useful for forecasters by providing additional under-the-hood data they can use to assess exactly why it is producing its answers.

“A key concern that troubles me is that while these predictions appear highly accurate, the output of the model is kind of a black box,” said Franklin.

Broader Sector Developments

Historically, no a commercial entity that has produced a high-performance weather model which allows researchers a peek into its techniques – unlike nearly all systems which are offered free to the public in their entirety by the governments that designed and maintain them.

Google is not alone in starting to use AI to solve difficult meteorological problems. The US and European governments are developing their own artificial intelligence systems in the works – which have demonstrated better performance over earlier traditional systems.

The next steps in artificial intelligence predictions appear to involve startup companies tackling previously tough-to-solve problems such as long-range forecasts and improved advance warnings of tornado outbreaks and sudden deluges – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is also launching its own weather balloons to address deficiencies in the US weather-observing network.