🔗 Share this article

As Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a major tropical system.

As the lead forecaster on duty, he forecasted that in just 24 hours the weather system would become a severe hurricane and start shifting towards the Jamaican shoreline. No forecaster had previously made this confident prediction for quick intensification.

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. True to the forecast, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Increasing Reliance on AI Predictions

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his certainty: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa reaching a most intense storm. Although I am unprepared to forecast that intensity yet due to path variability, that remains a possibility.

“It appears likely that a phase of quick strengthening is expected as the storm moves slowly over exceptionally hot sea temperatures which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the first AI model focused on tropical cyclones, and now the initial to beat standard weather forecasters at their specialty. Across all tropical systems this season, the AI is the best – even beating experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 strength, among the most powerful coastal impacts recorded in almost 200 years of record-keeping across the region. Papin’s bold forecast probably provided residents extra time to get ready for the catastrophe, potentially preserving lives and property.

How Google’s Model Works

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

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

“This season’s events has demonstrated in quick time is that the newcomer artificial intelligence systems are on par with and, in certain instances, more accurate than the slower traditional forecasting tools we’ve traditionally leaned on,” Lowry added.

Understanding AI Technology

To be sure, the system is an example of machine learning – a method that has been used in research fields like weather science for a long time – and is not generative AI like ChatGPT.

AI training takes mounds of data and extracts trends from them in a such a way that its model only requires minutes to generate an answer, and can operate on a desktop computer – in strong contrast to the flagship models that authorities have utilized for years that can require many hours to run and need the largest high-performance systems in the world.

Professional Responses and Future Developments

Nevertheless, the fact that Google’s model could exceed earlier gold-standard legacy models so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense storms.

“I’m impressed,” said James Franklin, a former forecaster. “The sample is sufficient that it’s pretty clear this is not just chance.”

He noted that while Google DeepMind is beating all competing systems on predicting the future path of hurricanes globally this year, like many AI models it sometimes errs on extreme strength forecasts wrong. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

During the next break, Franklin said he plans to talk with Google about how it can enhance the AI results even more helpful for forecasters by offering additional internal information they can utilize to assess exactly why it is producing its conclusions.

“The one thing that nags at me is that although these forecasts appear highly accurate, the results of the model is essentially a black box,” said Franklin.

Broader Sector Trends

Historically, no a private, for-profit company that has produced a top-level forecasting system which grants experts a view of its methods – in contrast to nearly all other models which are offered free to the public in their entirety by the authorities that designed and maintain them.

The company is not the only one in adopting artificial intelligence to address difficult meteorological problems. The authorities also have their respective artificial intelligence systems in the development phase – which have also shown better performance over previous non-AI versions.

Future developments in AI weather forecasts appear to involve new firms tackling formerly difficult problems such as long-range forecasts and better early alerts of severe weather and flash flooding – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is even deploying its proprietary atmospheric sensors to fill the gaps in the national monitoring system.