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When Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a monster hurricane.

As the primary meteorologist on duty, he forecasted that in a single day the weather system would become a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had previously made this confident prediction for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the guise of Google’s new DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa evolved into a system of remarkable power that tore through Jamaica.

Increasing Dependence on AI Predictions

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his certainty: “Roughly 40/50 Google DeepMind ensemble members show Melissa reaching 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 period of quick strengthening will occur as the system moves slowly over very warm sea temperatures which is the highest oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Models

The AI model is the pioneer AI model focused on tropical cyclones, and currently the first to outperform standard meteorological experts at their specialty. Across all 13 Atlantic storms this season, Google’s model is the best – even beating human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at category 5 strength, one of the strongest landfalls recorded in almost 200 years of data collection across the region. The confident prediction likely gave residents additional preparation time to prepare for the disaster, potentially preserving lives and property.

How Google’s Model Functions

Google’s model operates through spotting patterns that conventional lengthy scientific prediction systems may miss.

“The AI performs far faster than their traditional counterparts, and the processing requirements is less expensive and demanding,” said Michael Lowry, a former meteorologist.

“This season’s events has proven in short order is that the recent AI weather models are on par with and, in some cases, superior than the slower physics-based forecasting tools we’ve relied upon,” he added.

Understanding AI Technology

To be sure, Google DeepMind is an instance of AI training – a method that has been used in data-heavy sciences like weather science for years – and is distinct from generative AI like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a manner that its system only requires minutes to generate an answer, and can operate on a desktop computer – in sharp difference to the flagship models that governments have used for years that can require many hours to process and need some of the biggest supercomputers in the world.

Professional Reactions and Future Developments

Nevertheless, the fact that the AI could exceed earlier gold-standard legacy models so quickly is truly remarkable to weather scientists who have spent their careers trying to forecast the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a retired forecaster. “The sample is sufficient that it’s evident this is not just chance.”

Franklin said that while Google DeepMind is outperforming all competing systems on predicting the future path of hurricanes worldwide this year, like many AI models it sometimes errs on extreme strength predictions inaccurate. It had difficulty with Hurricane Erin previously, as it was also undergoing quick strengthening to maximum intensity north of 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 under-the-hood data they can utilize to assess exactly why it is coming up with its conclusions.

“A key concern that troubles me is that while these predictions seem to be really, really good, the output of the model is kind of a opaque process,” said Franklin.

Wider Industry Developments

Historically, no a private, for-profit company that has produced a high-performance forecasting system which allows researchers a peek into its techniques – in contrast to nearly all other models which are provided at no cost to the general audience in their entirety by the authorities that created and operate them.

Google is not alone in adopting AI to solve difficult weather forecasting problems. The authorities also have their respective artificial intelligence systems in the works – which have demonstrated improved skill over previous traditional systems.

Future developments in artificial intelligence predictions seem to be new firms taking swings at formerly difficult problems such as sub-seasonal outlooks and better advance warnings of severe weather and flash flooding – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is even deploying its own atmospheric sensors to fill the gaps in the national monitoring system.