🔗 Share this article

When Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a monster hurricane.

As the lead forecaster on duty, he predicted that in a single day the weather system would become a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued this confident prediction for quick intensification.

But, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s new DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Growing Reliance on AI Forecasting

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his confidence: “Approximately 40/50 AI ensemble members show Melissa becoming a Category 5 storm. While I am unprepared to forecast that intensity at this time due to path variability, that remains a possibility.

“It appears likely that a period of quick strengthening is expected as the storm drifts over very warm sea temperatures which is the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Models

The AI model is the first artificial intelligence system focused on hurricanes, and currently the initial to outperform traditional meteorological experts at their specialty. Across all 13 Atlantic storms so far this year, the AI is the best – surpassing experts on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 intensity, one of the strongest coastal impacts ever documented in nearly two centuries of data collection across the region. The confident prediction likely gave people in Jamaica extra time to get ready for the disaster, potentially preserving people and assets.

How Google’s Model Functions

Google’s model operates through identifying trends that conventional lengthy physics-based weather models may overlook.

“The AI performs far faster than their physics-based cousins, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” he said.

Clarifying Machine Learning

To be sure, Google DeepMind is an instance of AI training – a method that has been used in data-heavy sciences like meteorology for years – and is not creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a such a way that its model only takes a few minutes to generate an answer, and can do so on a desktop computer – in sharp difference to the primary systems that governments have utilized for years that can take hours to process and need the largest supercomputers in the world.

Professional Responses and Future Advances

Nevertheless, the reality that the AI could exceed previous gold-standard traditional systems so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the most intense storms.

“It’s astonishing,” commented James Franklin, a retired forecaster. “The sample is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

Franklin said that while Google DeepMind is outperforming all other models on forecasting the future path of storms worldwide this year, like many AI models it occasionally gets extreme strength predictions wrong. It had difficulty with another storm previously, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, he said he intends to talk with Google about how it can enhance the AI results more useful for experts by providing additional under-the-hood data they can utilize to evaluate exactly why it is producing its answers.

“The one thing that nags at me is that although these forecasts appear really, really good, the output of the model is kind of a black box,” remarked Franklin.

Wider Industry Developments

Historically, no a commercial entity that has produced a high-performance weather model which grants experts a view of its techniques – unlike nearly all other models which are offered free to the public in their entirety by the governments that created and operate them.

Google is not the only one in starting to use artificial intelligence to solve difficult weather forecasting problems. The authorities also have their own artificial intelligence systems in the works – which have demonstrated improved skill over earlier traditional systems.

Future developments in artificial intelligence predictions appear to involve new firms tackling previously tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and flash flooding – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even deploying its proprietary weather balloons to address deficiencies in the national monitoring system.