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When Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a major tropical system.

As the primary meteorologist on duty, he predicted that in just 24 hours the weather system would become a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued this confident forecast for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the guise of Google’s recently introduced DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa did become a system of astonishing strength that tore through Jamaica.

Increasing Reliance on AI Predictions

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his certainty: “Approximately 40/50 AI simulation runs indicate Melissa becoming a most intense storm. While I am unprepared to forecast that intensity at this time due to path variability, that is still plausible.

“It appears likely that a phase of rapid intensification is expected as the system drifts over very warm sea temperatures which is the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Systems

The AI model is the first AI model dedicated to hurricanes, and currently the first to outperform traditional meteorological experts at their specialty. Through all tropical systems so far this year, the AI is the best – surpassing experts on track predictions.

Melissa eventually made landfall in Jamaica at category 5 strength, among the most powerful coastal impacts ever documented in almost 200 years of record-keeping across the region. Papin’s bold forecast likely gave residents additional preparation time to get ready for the catastrophe, potentially preserving lives and property.

The Way The Model Functions

The AI system works by spotting patterns that traditional lengthy physics-based 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 former meteorologist.

“What this hurricane season has demonstrated in short order is that the newcomer AI weather models are competitive with and, in certain instances, more accurate than the less rapid traditional weather models we’ve traditionally leaned on,” he said.

Understanding Machine Learning

To be sure, the system is an example of AI training – a method that has been used in data-heavy sciences like meteorology for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training processes mounds of data and pulls out patterns from them in a manner that its model only requires minutes to generate an answer, and can operate on a standard PC – in strong contrast to the flagship models that governments have utilized for years that can take hours to run and require the largest high-performance systems in the world.

Expert Reactions and Future Advances

Nevertheless, the reality that the AI could exceed earlier top-tier legacy models so quickly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest weather systems.

“It’s astonishing,” said James Franklin, a retired forecaster. “The sample is now large enough that it’s pretty clear this is not a case of chance.”

He noted that while the AI is beating all other models on predicting the trajectory of hurricanes worldwide this year, similar to other systems it sometimes errs on extreme strength forecasts wrong. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

In the coming offseason, he said he plans to talk with Google about how it can make the DeepMind output more useful for experts by offering additional internal information they can utilize to assess exactly why it is producing its answers.

“A key concern that troubles me is that while these forecasts appear really, really good, the output of the model is essentially a black box,” remarked Franklin.

Broader Industry Trends

Historically, no a commercial entity that has developed a top-level weather model which grants experts a peek into its techniques – in contrast to most other models which are provided at no cost to the public in their entirety by the authorities that created and operate them.

Google is not the only one in starting to use AI to solve challenging weather forecasting problems. The authorities are developing their own AI weather models in the development phase – which have demonstrated better performance over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve new firms taking swings at formerly difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and flash flooding – and they have secured federal support to do so. One company, WindBorne Systems, is also deploying its proprietary atmospheric sensors to address deficiencies in the national monitoring system.