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When Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a major tropical system.

As the lead forecaster on duty, he forecasted that in just 24 hours the storm would intensify into a severe hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had ever issued such a bold forecast for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the form of Google’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa did become a system of remarkable power that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his confidence: “Roughly 40/50 AI ensemble members indicate Melissa becoming a most intense storm. While I am not ready to forecast that intensity at this time given path variability, that remains a possibility.

“There is a high probability that a phase of rapid intensification is expected as the system moves slowly over very warm ocean waters which is the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Systems

The AI model is the pioneer AI model focused on hurricanes, and now the initial to beat standard meteorological experts at their specialty. Across all tropical systems this season, Google’s model is the best – even beating experts on path forecasts.

The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest coastal impacts recorded in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided residents additional preparation time to get ready for the disaster, possibly saving people and assets.

How Google’s System Functions

Google’s model works by spotting patterns that conventional lengthy scientific weather models may miss.

“The AI performs much more quickly than their traditional counterparts, and the computing power is more affordable 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 competitive with and, in some cases, superior than the slower traditional weather models we’ve relied upon,” he said.

Understanding Machine Learning

It’s important to note, Google DeepMind is an instance of machine learning – a technique that has been employed in data-heavy sciences like weather science for years – and is distinct from creative artificial intelligence like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a such a way that its system only requires minutes to come up with an result, and can operate on a desktop computer – in sharp difference to the flagship models that governments have utilized for years that can take hours to process and need the largest high-performance systems in the world.

Professional Responses and Upcoming Developments

Still, the fact that Google’s model could exceed earlier top-tier legacy models so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense weather systems.

“It’s astonishing,” commented James Franklin, a former expert. “The data is sufficient that it’s evident this is not a case of chance.”

Franklin noted that although the AI is beating all competing systems on predicting the future path of storms globally this year, like many AI models it occasionally gets high-end intensity predictions wrong. It had difficulty with another storm previously, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

In the coming offseason, he said he intends to talk with Google about how it can make the AI results even more helpful for experts by offering additional internal information they can utilize to evaluate exactly why it is producing its answers.

“The one thing that nags at me is that although these predictions seem to be really, really good, the results of the system is kind of a opaque process,” remarked Franklin.

Broader Industry Developments

Historically, no a private, for-profit company that has developed a top-level weather model which grants experts a view of its methods – in contrast to nearly all other models which are offered free to the public in their full form by the authorities that designed and maintain them.

Google is not the only one in starting to use AI to address challenging meteorological problems. The US and European governments are developing their respective AI weather models in the works – which have also shown better performance over previous traditional systems.

Future developments in AI weather forecasts appear to involve startup companies taking swings at previously difficult problems such as long-range forecasts and improved early alerts of severe weather and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also launching its own weather balloons to address deficiencies in the national monitoring system.