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When Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a major tropical system.

As the primary meteorologist on duty, he predicted that in just 24 hours the storm would intensify into a severe hurricane and begin a turn towards the Jamaican shoreline. No forecaster had previously made this confident prediction for quick intensification.

But, Papin possessed a secret advantage: AI technology in the form of Google’s new DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa did become a storm of remarkable power that ravaged Jamaica.

Growing Dependence on AI Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that Google’s model was a key factor for his confidence: “Roughly 40/50 Google DeepMind simulation runs show Melissa reaching a Category 5 hurricane. While I am unprepared to predict that strength at this time due to track uncertainty, that is still plausible.

“It appears likely that a period of quick strengthening will occur as the system moves slowly over exceptionally hot sea temperatures which is the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the pioneer artificial intelligence system focused on tropical cyclones, and currently the initial to beat standard weather forecasters at their specialty. Across all 13 Atlantic storms this season, the AI is top-performing – even beating human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in almost 200 years of data collection across the region. Papin’s bold forecast likely gave residents extra time to get ready for the catastrophe, possibly saving lives and property.

The Way Google’s Model Works

The AI system works by identifying trends that conventional time-intensive physics-based weather models may overlook.

“They do it much more quickly than their traditional counterparts, and the processing requirements is more affordable and demanding,” said Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in quick time is that the newcomer artificial intelligence systems are on par with and, in certain instances, superior than the less rapid traditional weather models we’ve traditionally leaned on,” he said.

Understanding AI Technology

It’s important to note, the system is an instance of AI training – a method that has been used in research fields like meteorology for years – and is not creative artificial intelligence like ChatGPT.

AI training processes mounds of data and pulls out patterns from them in a such a way that its system only takes a few minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the primary systems that authorities have used for years that can require many hours to process and require some of the biggest supercomputers in the world.

Expert Reactions and Upcoming Advances

Nevertheless, the reality that the AI could exceed previous gold-standard traditional systems so rapidly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

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

He said that although Google DeepMind is outperforming all other models on predicting the trajectory of storms globally this year, like many AI models it sometimes errs on high-end intensity forecasts inaccurate. It struggled with another storm earlier this year, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

During the next break, he stated he intends to talk with Google about how it can make the DeepMind output more useful for forecasters by offering extra under-the-hood data they can use to evaluate exactly why it is producing its conclusions.

“A key concern that troubles me is that while these forecasts appear really, really good, the output of the system is essentially a opaque process,” said Franklin.

Wider Sector Trends

There has never been a commercial entity that has produced a high-performance forecasting system which allows researchers a peek into its techniques – unlike most systems which are offered free to the general audience in their full form by the governments that created and operate them.

Google is not the only one in starting to use artificial intelligence to solve challenging weather forecasting problems. The US and European governments also have their respective artificial intelligence systems in the development phase – which have also shown improved skill over previous traditional systems.

Future developments in artificial intelligence predictions seem to be new firms taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and better early alerts of severe weather and flash flooding – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the US weather-observing network.