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When Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a major tropical system.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the weather system would intensify into a severe hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made this confident forecast for quick intensification.

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

Increasing Dependence on AI Predictions

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his certainty: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa becoming a most intense storm. Although I am not ready to forecast that strength at this time due to path variability, that remains a possibility.

“It appears likely that a phase of quick strengthening will occur as the storm moves slowly over very warm ocean waters which represent the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Models

The AI model is the pioneer AI model focused on hurricanes, and currently the first to beat standard meteorological experts at their specialty. Through all 13 Atlantic storms this season, the AI is the best – even beating human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 strength, one of the strongest coastal impacts recorded in almost 200 years of data collection across the region. Papin’s bold forecast likely gave people in Jamaica additional preparation time to prepare for the catastrophe, potentially preserving people and assets.

The Way The System Works

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

“They do it far faster than their traditional counterparts, and the computing power is less expensive and demanding,” stated Michael Lowry, a ex meteorologist.

“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 less rapid physics-based forecasting tools we’ve traditionally leaned on,” he added.

Clarifying Machine Learning

To be sure, Google DeepMind is an instance of machine learning – a technique that has been used in research fields like meteorology for a long time – and is distinct from generative AI like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a such a way that its system only takes a few minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the flagship models that authorities have used for years that can take hours to process and need some of the biggest high-performance systems in the world.

Expert Reactions and Future Developments

Still, the fact that the AI could exceed earlier gold-standard legacy models so rapidly is truly remarkable to meteorologists who have spent their careers trying to predict the most intense weather systems.

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

Franklin noted that although the AI is beating all competing systems on predicting the future path of hurricanes worldwide this year, like many AI models it sometimes errs on high-end intensity predictions wrong. It struggled with Hurricane Erin previously, as it was also undergoing rapid intensification to category 5 above the Caribbean.

In the coming offseason, he said he intends to talk with Google about how it can make the DeepMind output even more helpful for forecasters by offering extra internal information they can utilize to assess exactly why it is producing its answers.

“A key concern that troubles me is that although these forecasts appear highly accurate, the output of the system is essentially a black box,” remarked Franklin.

Broader Sector Trends

Historically, no a private, for-profit company that has produced a top-level weather model which allows researchers a peek into its methods – in contrast to nearly all other models which are provided free to the public in their entirety by the governments that designed and maintain them.

The company is not the only one in adopting AI to address challenging weather forecasting problems. The US and European governments also have their own AI weather models in the development phase – which have demonstrated improved skill over earlier traditional systems.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and better early alerts of severe weather and flash flooding – and they are receiving federal support to do so. One company, WindBorne Systems, is also launching its proprietary atmospheric sensors to fill the gaps in the national monitoring system.