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As Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a major tropical system.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the storm would intensify into a category 4 hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had ever issued this confident prediction for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the form of Google’s recently introduced DeepMind hurricane model – launched for the initial occasion in June. True to the forecast, Melissa did become a storm of remarkable power that ravaged Jamaica.

Increasing Dependence on AI Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 Google DeepMind simulation runs show Melissa becoming a Category 5 storm. Although I am not ready to forecast that strength yet due to track uncertainty, that remains a possibility.

“It appears likely that a phase of rapid intensification is expected as the storm moves slowly over exceptionally hot sea temperatures which is the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Systems

The AI model is the first artificial intelligence system focused on hurricanes, and now the initial to beat standard weather forecasters at their specialty. Through all 13 Atlantic storms so far this year, Google’s model is top-performing – surpassing experts on track predictions.

Melissa eventually made landfall in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica additional preparation time to get ready for the catastrophe, potentially preserving lives and property.

The Way Google’s System Functions

Google’s model works by spotting patterns that traditional lengthy physics-based prediction systems may overlook.

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

“This season’s events has demonstrated in short order is that the newcomer AI weather models are on par with and, in certain instances, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” Lowry added.

Clarifying AI Technology

To be sure, the system is an instance of AI training – a technique that has been employed in data-heavy sciences like weather science for a long time – and is distinct from generative AI like ChatGPT.

AI training processes mounds of data and extracts trends from them in a such a way that its model only takes a few minutes to generate an answer, and can do so on a standard PC – in sharp difference to the primary systems that governments have used for decades that can take hours to process and require the largest supercomputers in the world.

Professional Responses and Future Advances

Still, the fact that the AI could outperform earlier top-tier legacy models so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to predict the most intense storms.

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

Franklin said that while Google DeepMind is outperforming all competing systems on forecasting the future path of hurricanes globally this year, like many AI models it sometimes errs on extreme strength forecasts inaccurate. It had difficulty with another storm earlier this year, as it was also undergoing quick strengthening to category 5 above the Caribbean.

In the coming offseason, Franklin said he plans to discuss with the company about how it can enhance the AI results even more helpful for forecasters by providing additional under-the-hood data they can use to assess the reasons it is coming up with its answers.

“A key concern that nags at me is that although these forecasts seem to be really, really good, the output of the model is kind of a opaque process,” remarked Franklin.

Broader Sector Trends

There has never been a commercial entity that has developed a high-performance forecasting system which grants experts a view of its methods – unlike most other models which are offered free to the general audience in their entirety by the governments that created and operate them.

The company is not alone in adopting artificial intelligence to address difficult meteorological problems. The authorities are developing their respective AI weather models in the development phase – which have also shown improved skill over previous non-AI versions.

Future developments in AI weather forecasts appear to involve startup companies tackling previously tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and sudden deluges – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is even launching its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.