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As Developing Cyclone Melissa swirled off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a major tropical system.

As the primary meteorologist on duty, he forecasted that in a single day the weather system would intensify into a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had ever issued this confident prediction for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa did become a storm of remarkable power that tore through Jamaica.

Growing Dependence on AI Forecasting

Meteorologists are heavily relying upon the AI system. 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 ensemble members show Melissa becoming a most intense storm. While I am unprepared to predict that strength at this time given track uncertainty, that is still plausible.

“It appears likely that a period of quick strengthening is expected as the storm moves slowly over very warm ocean waters which is the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Conventional Models

The AI model is the pioneer artificial intelligence system focused on tropical cyclones, and now the first to outperform traditional meteorological experts at their specialty. Across all 13 Atlantic storms this season, Google’s model is top-performing – even beating human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 strength, one of the strongest coastal impacts recorded in nearly two centuries of record-keeping across the region. The confident prediction probably provided people in Jamaica additional preparation time to get ready for the disaster, potentially preserving people and assets.

How Google’s Model Functions

The AI system works by spotting patterns that conventional lengthy scientific prediction systems may miss.

“They do it far faster than their physics-based cousins, and the processing requirements is less expensive and demanding,” stated Michael Lowry, a former forecaster.

“What this hurricane season has demonstrated in quick time is that the newcomer AI weather models are on par with and, in certain instances, more accurate than the slower traditional weather models we’ve relied upon,” Lowry added.

Clarifying Machine Learning

To be sure, the system is an instance of AI training – a technique that has been used in research fields like weather science for a long time – and is not generative AI like ChatGPT.

AI training processes 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 do so on a desktop computer – in strong contrast to the flagship models that governments have utilized for years that can require many hours to run and need some of the biggest supercomputers in the world.

Professional Reactions and Future Advances

Nevertheless, the fact that the AI could outperform earlier gold-standard traditional systems so rapidly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the most intense weather systems.

“It’s astonishing,” said James Franklin, a retired expert. “The sample is now large enough that it’s evident this is not just chance.”

Franklin noted that although the AI is beating all competing systems on forecasting the trajectory of storms globally this year, like many AI models it occasionally gets extreme strength forecasts inaccurate. It struggled with another storm earlier this year, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

During the next break, Franklin stated he plans to talk with Google about how it can make the DeepMind output more useful for experts by offering extra internal information they can utilize to assess the reasons it is coming up with its conclusions.

“A key concern that troubles me is that while these predictions seem to be really, really good, the output of the model is kind of a opaque process,” said Franklin.

Broader Industry Developments

Historically, no a commercial entity that has produced a high-performance forecasting system which grants experts a view of its techniques – in contrast to nearly all systems which are offered at no cost to the public in their entirety by the authorities that designed and maintain them.

Google is not alone in adopting AI to address challenging meteorological problems. The US and European governments are developing their own AI weather models in the development phase – which have demonstrated improved skill over previous non-AI versions.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also deploying its proprietary atmospheric sensors to fill the gaps in the national monitoring system.