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As Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it was about to grow into a major tropical system.

Serving as lead forecaster on duty, he predicted that in just 24 hours the weather system would intensify into a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had ever issued this confident forecast for quick intensification.

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

Growing Dependence on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his certainty: “Approximately 40/50 Google DeepMind ensemble members show Melissa reaching a most intense hurricane. While I am unprepared to predict that intensity at this time given track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening will occur as the storm drifts over very warm sea temperatures which is the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the first artificial intelligence system dedicated to tropical cyclones, and now the initial to beat traditional weather forecasters at their own game. Across all 13 Atlantic storms so far this year, the AI is top-performing – surpassing human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at maximum strength, among the most powerful coastal impacts recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction probably provided people in Jamaica extra time to get ready for the catastrophe, potentially preserving lives and property.

The Way The System Works

The AI system operates through spotting patterns that conventional lengthy scientific weather models may miss.

“They do it far faster than their traditional counterparts, and the computing power is more affordable and demanding,” stated Michael Lowry, a ex meteorologist.

“This season’s events has proven in quick time is that the newcomer AI weather models are on par with and, in certain instances, more accurate than the less rapid traditional weather models we’ve relied upon,” Lowry added.

Understanding AI Technology

It’s important to note, Google DeepMind is an instance of machine learning – a method that has been employed in research fields like meteorology for years – and is not generative AI like ChatGPT.

Machine learning 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 result, and can operate on a standard PC – in strong contrast to the flagship models that governments have used for years that can take hours to run and require the largest supercomputers in the world.

Expert Responses and Upcoming Developments

Nevertheless, the reality that the AI could exceed earlier top-tier legacy models so quickly is nothing short of amazing to weather scientists 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 pretty clear this is not just chance.”

Franklin said that while Google DeepMind is outperforming all other models on forecasting the trajectory of storms globally this year, like many AI models it sometimes errs on high-end intensity predictions inaccurate. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

During the next break, Franklin stated he intends to discuss with the company about how it can enhance the DeepMind output even more helpful for forecasters by providing extra internal information they can use to evaluate exactly why it is producing its answers.

“A key concern that troubles me is that while these forecasts appear highly accurate, the results of the system is essentially a opaque process,” remarked Franklin.

Broader Sector Developments

There has never been a commercial entity that has developed a high-performance weather model which allows researchers a view of its techniques – unlike most systems which are provided free to the public in their full form by the governments that created and operate them.

Google is not alone in adopting artificial intelligence to address difficult weather forecasting problems. The US and European governments are developing their own AI weather models in the works – which have also shown improved skill over previous non-AI versions.

Future developments in artificial intelligence predictions appear to involve new firms tackling previously difficult problems such as sub-seasonal outlooks and better early alerts of severe weather and flash flooding – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is also deploying its own weather balloons to fill the gaps in the national monitoring system.