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When Tropical Storm Melissa was churning south 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 intensify into a severe hurricane and start shifting towards the coast of Jamaica. Not a single expert had previously made such a bold prediction for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s recently introduced DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his certainty: “Approximately 40/50 Google DeepMind ensemble members show Melissa reaching a most intense storm. While I am unprepared to predict that strength at this time given track uncertainty, that remains a possibility.

“There is a high probability that a phase of quick strengthening is expected as the storm moves slowly over exceptionally hot ocean waters which is the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Systems

The AI model is the first artificial intelligence system focused on tropical cyclones, and now the initial to outperform standard weather forecasters at their specialty. Across all 13 Atlantic storms this season, Google’s model is the best – even beating experts on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 intensity, among the most powerful landfalls recorded in nearly two centuries of data collection across the Atlantic basin. The confident prediction probably provided residents extra time to prepare for the catastrophe, possibly saving people and assets.

The Way Google’s Model Works

Google’s model operates through spotting patterns that traditional time-intensive physics-based weather models may miss.

“They do it much more quickly than their traditional counterparts, and the computing power is more affordable and demanding,” stated Michael Lowry, a ex meteorologist.

“This season’s events has proven in short order is that the newcomer AI weather models are on par with and, in certain instances, more accurate than the less rapid traditional weather models we’ve traditionally leaned on,” he added.

Understanding Machine Learning

It’s important to note, the system is an instance of AI training – a technique that has been used in research fields like meteorology for a long time – and is distinct from generative AI like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a manner that its system only takes a few minutes to come up with an result, and can do so on a desktop computer – in strong contrast to the primary systems that governments have used for years that can require many hours to process and require the largest high-performance systems in the world.

Professional Responses and Future Advances

Nevertheless, the fact that Google’s model could outperform previous top-tier legacy models so quickly is truly remarkable to weather scientists who have spent their careers trying to predict the world’s strongest weather systems.

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

He said that while the AI is outperforming all other models on predicting the trajectory of hurricanes worldwide this year, like many AI models it sometimes errs on extreme strength forecasts inaccurate. It struggled with Hurricane Erin previously, as it was also undergoing quick strengthening to category 5 above the Caribbean.

During the next break, Franklin said he intends to discuss with the company about how it can make the DeepMind output more useful for experts by providing additional internal information they can use to evaluate exactly why it is coming up with its answers.

“A key concern that troubles me is that although these forecasts appear really, really good, the results of the model is essentially a black box,” remarked Franklin.

Broader Sector Trends

Historically, no a commercial entity that has produced a top-level weather model which allows researchers a peek into its methods – unlike nearly all systems which are offered free to the public in their full form by the authorities that designed and maintain them.

The company is not the only one in starting to use AI to solve challenging meteorological problems. The US and European governments are developing their respective artificial intelligence systems in the development phase – which have demonstrated better performance over earlier traditional systems.

Future developments in artificial intelligence predictions appear to involve startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and better advance warnings of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even deploying its proprietary weather balloons to address deficiencies in the US weather-observing network.