🔗 Share this article

As Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a major tropical system.

As the primary meteorologist on duty, he forecasted that in just 24 hours the storm would intensify into a severe hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had previously made this confident forecast for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s new DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa did become a storm of astonishing strength that tore through Jamaica.

Increasing Dependence on AI Forecasting

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his certainty: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa becoming a most intense hurricane. While I am not ready to forecast that intensity yet given track uncertainty, that remains a possibility.

“It appears likely that a phase of quick strengthening will occur as the storm drifts over exceptionally hot ocean waters which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the pioneer AI model dedicated to hurricanes, and currently the initial to outperform standard weather forecasters at their specialty. Through all 13 Atlantic storms so far this year, the AI is top-performing – surpassing human forecasters on track predictions.

Melissa ultimately struck in Jamaica at category 5 intensity, one of the strongest landfalls recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction probably provided people in Jamaica extra time to get ready for the disaster, potentially preserving people and assets.

The Way Google’s System Functions

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

“They do it much more quickly than their traditional counterparts, and the processing requirements is less expensive and time consuming,” stated Michael Lowry, a former forecaster.

“What this hurricane season has demonstrated in short order is that the newcomer artificial intelligence systems are competitive with and, in some cases, more accurate than the slower traditional forecasting tools we’ve traditionally leaned on,” he added.

Understanding Machine Learning

To be sure, the system is an example of machine learning – a technique that has been used in data-heavy sciences like meteorology for a long time – and is distinct from generative AI like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a manner that its system only requires minutes to come up with an result, and can operate on a standard PC – in sharp difference to the primary systems that governments have used for decades that can take hours to process and require the largest high-performance systems in the world.

Expert Responses and Future Developments

Nevertheless, the fact that the AI could exceed previous gold-standard traditional systems so quickly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the most intense weather systems.

“I’m impressed,” commented James Franklin, a former expert. “The data is sufficient that it’s pretty clear this is not just beginner’s luck.”

He said that while the AI is outperforming all other models on forecasting the trajectory of hurricanes globally this year, similar to other systems it sometimes errs on extreme strength forecasts wrong. It had difficulty with another storm earlier this year, as it was also undergoing rapid intensification to category 5 above the Caribbean.

During the next break, Franklin said he intends to discuss with the company about how it can enhance the AI results even more helpful for forecasters by offering additional internal information they can use to evaluate the reasons it is producing its answers.

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

Wider Industry Trends

There has never been a commercial entity that has produced a top-level weather model which grants experts a peek into its techniques – unlike most systems which are provided free to the general audience in their entirety by the authorities that created and operate them.

The company is not the only one in starting to use AI to solve challenging weather forecasting problems. The authorities are developing their respective artificial intelligence systems in the development phase – which have demonstrated better performance over previous non-AI versions.

The next steps in AI weather forecasts seem to be startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and flash flooding – and they are receiving 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.