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As Tropical Storm Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a major tropical system.

As the lead forecaster on duty, he predicted that in a single day the weather system would become a severe hurricane and begin a turn towards the coast of Jamaica. No forecaster had ever issued such a bold forecast for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s recently introduced DeepMind hurricane model – released for the initial occasion in June. True to the forecast, Melissa did become a system of astonishing strength that tore through Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his confidence: “Roughly 40/50 AI simulation runs show Melissa becoming a Category 5 hurricane. Although I am not ready to predict that strength yet due to path variability, that remains a possibility.

“It appears likely that a period of rapid intensification will occur as the storm drifts over exceptionally hot sea temperatures which is the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the pioneer artificial intelligence system focused on tropical cyclones, and now the initial to beat standard weather forecasters at their own game. Through all 13 Atlantic storms this season, the AI is the best – even beating human forecasters on track predictions.

Melissa ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts ever documented in nearly two centuries of record-keeping across the region. Papin’s bold forecast probably provided people in Jamaica extra time to get ready for the catastrophe, potentially preserving people and assets.

How Google’s Model Functions

The AI system operates through spotting patterns that traditional lengthy physics-based weather models may overlook.

“They do it far faster than their traditional counterparts, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in short order is that the recent AI weather models are competitive with and, in some cases, superior than the less rapid traditional forecasting tools we’ve traditionally leaned on,” Lowry added.

Understanding AI Technology

To be sure, Google DeepMind is an instance of machine learning – a technique that has been employed in data-heavy sciences like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes large datasets and extracts trends from them in a such a way that its model only requires minutes to generate an result, and can do so on a standard PC – in strong contrast to the flagship models that governments have utilized for decades that can take hours to run and require some of the biggest supercomputers in the world.

Professional Responses and Upcoming Developments

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

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

Franklin noted that although the AI is beating all other models on forecasting the future path of hurricanes worldwide this year, similar to other systems it occasionally gets extreme strength forecasts inaccurate. It had difficulty with Hurricane Erin previously, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, Franklin said he plans to talk with Google about how it can make the AI results even more helpful for forecasters by providing additional internal information they can use to evaluate the reasons it is producing its conclusions.

“A key concern that nags at me is that although these forecasts appear really, really good, the output of the system is kind of a black box,” said Franklin.

Wider Sector Developments

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

The company is not the only one in starting to use artificial intelligence to solve challenging weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the development phase – which have also shown better performance over previous non-AI versions.

The next steps in artificial intelligence predictions appear to involve new firms tackling formerly tough-to-solve problems such as long-range forecasts and better early alerts of tornado outbreaks and sudden deluges – and they have secured US government funding to do so. One company, WindBorne Systems, is even deploying its own weather balloons to address deficiencies in the national monitoring system.