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When Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it was about to grow into a major tropical system.

Serving as lead forecaster on duty, he forecasted that in a single day the weather system would intensify into a severe hurricane and start shifting towards the coast of Jamaica. No forecaster had previously made such a bold forecast for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the guise of Google’s new DeepMind cyclone prediction system – launched 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 Forecasting

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his certainty: “Roughly 40/50 AI simulation runs indicate Melissa reaching a Category 5 hurricane. While I am not ready to predict that intensity yet due to path variability, that remains a possibility.

“It appears likely that a period of quick strengthening will occur as the storm drifts over exceptionally hot sea temperatures which represent the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the pioneer artificial intelligence system dedicated to hurricanes, and currently the initial to outperform traditional weather forecasters at their own game. Through all 13 Atlantic storms so far this year, the AI is the best – even beating human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls ever documented in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction probably provided residents extra time to prepare for the disaster, potentially preserving lives and property.

How The Model Works

The AI system operates through spotting patterns that traditional lengthy physics-based prediction systems may miss.

“The AI performs much more quickly than their physics-based cousins, and the computing power is more affordable and demanding,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has proven in quick time is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the less rapid traditional weather models we’ve traditionally leaned on,” he added.

Clarifying AI Technology

It’s important to note, the system is an instance of AI training – a technique that has been used in data-heavy sciences like weather science for years – and is not creative artificial intelligence like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a such a way that its model only takes a few minutes to come up with an result, and can do so on a desktop computer – in sharp difference to the flagship models that authorities have used for years that can require many hours to run and need the largest supercomputers in the world.

Professional Reactions and Upcoming Developments

Nevertheless, the fact that Google’s model could outperform previous top-tier legacy models so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the most intense storms.

“I’m impressed,” commented James Franklin, a former forecaster. “The data is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

Franklin noted that while Google DeepMind is beating all competing systems on forecasting the future path of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity predictions inaccurate. It struggled with another storm earlier this year, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, he said he plans to discuss with the company about how it can enhance the AI results even more helpful for experts by providing extra under-the-hood data they can utilize to evaluate exactly why it is producing its conclusions.

“A key concern that nags at me is that while these predictions seem to be highly accurate, the results of the model is essentially a opaque process,” said Franklin.

Broader Industry Trends

Historically, no a commercial entity that has produced a top-level forecasting system which grants experts a view of its methods – unlike most other models which are offered at no cost to the public in their entirety by the governments that created and operate them.

The company is not alone in starting to use AI to address difficult meteorological problems. The US and European governments also have their own AI weather models in the works – which have also shown improved skill over previous traditional systems.

The next steps in AI weather forecasts seem to be new firms tackling formerly difficult problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and flash flooding – and they are receiving federal support to do so. One company, WindBorne Systems, is even launching its own weather balloons to address deficiencies in the US weather-observing network.