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

Serving as lead forecaster on duty, he predicted that in just 24 hours the storm would intensify into a category 4 hurricane and begin a turn towards the Jamaican shoreline. No forecaster had ever issued this confident forecast for quick intensification.

However, Papin possessed a secret advantage: AI technology in the guise of Google’s recently introduced DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa did become a system of remarkable power that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon the AI system. During 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his certainty: “Approximately 40/50 AI simulation runs indicate Melissa reaching a Category 5 storm. Although I am not ready to predict that strength at this time due to path variability, that is still plausible.

“There is a high probability that a phase of rapid intensification will occur as the system drifts over very warm ocean waters which is the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Models

The AI model is the pioneer artificial intelligence system focused on hurricanes, and currently the first to outperform traditional meteorological experts at their own game. Through all tropical systems this season, the AI is top-performing – even beating human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at maximum intensity, among the most powerful landfalls ever documented in almost 200 years of data collection across the region. The confident prediction probably provided people in Jamaica extra time to prepare for the catastrophe, possibly saving people and assets.

How Google’s System Functions

Google’s model works by identifying trends that conventional lengthy physics-based prediction systems may overlook.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is more affordable and demanding,” said Michael Lowry, a former meteorologist.

“What this hurricane season has proven in short order is that the newcomer artificial intelligence systems are competitive with and, in some cases, superior than the less rapid physics-based weather models we’ve relied upon,” Lowry added.

Understanding Machine Learning

To be sure, Google DeepMind is an example of AI training – a method that has been employed in research fields like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a such a way that its system only requires minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the flagship models that authorities have utilized for decades that can require many hours to process and require the largest high-performance systems in the world.

Professional Reactions and Future Advances

Nevertheless, the reality that Google’s model could outperform earlier gold-standard legacy models so quickly is truly remarkable to meteorologists who have dedicated their lives trying to predict the most intense weather systems.

“I’m impressed,” said James Franklin, a former forecaster. “The sample is now large enough that it’s pretty clear this is not just beginner’s luck.”

He noted that although Google DeepMind is outperforming all competing systems on predicting the trajectory of hurricanes globally this year, similar to other systems it sometimes errs on high-end intensity predictions wrong. It had difficulty with another storm earlier this year, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

In the coming offseason, Franklin stated he intends to talk with the company about how it can enhance the AI results even more helpful for experts by offering additional under-the-hood data they can utilize to assess exactly why 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 results of the model is kind of a black box,” remarked Franklin.

Broader Sector Trends

Historically, no a private, for-profit company that has produced a top-level forecasting system which allows researchers a view of its techniques – in contrast to most other models which are provided at no cost to the public in their full form by the governments that created and operate them.

The company is not the only one in starting to use AI to solve challenging weather forecasting problems. The US and European governments are developing their own 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 formerly tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of severe weather and flash flooding – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also deploying its own atmospheric sensors to fill the gaps in the US weather-observing network.