The Way Alphabet’s AI Research System is Revolutionizing Hurricane Prediction with Speed

As Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon grow into a major tropical system.

As the primary meteorologist on duty, he predicted that in just 24 hours the weather system would become a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had ever issued such a bold prediction for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s recently introduced DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Growing Reliance on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin explained in his official briefing that Google’s model was a key factor for his confidence: “Roughly 40/50 Google DeepMind ensemble members show Melissa becoming a most intense storm. While I am unprepared to predict that intensity at this time due to track uncertainty, that remains a possibility.

“It appears likely that a period of quick strengthening is expected as the system moves slowly over very warm sea temperatures which is the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Models

The AI model is the first artificial intelligence system dedicated to hurricanes, and now the initial to beat standard weather forecasters at their own game. Through all 13 Atlantic storms this season, the AI is top-performing – even beating human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at maximum intensity, among the most powerful coastal impacts ever documented in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided residents additional preparation time to get ready for the catastrophe, potentially preserving people and assets.

How The System Functions

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

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

“What this hurricane season has proven in quick time is that the newcomer artificial intelligence systems are on par with and, in certain instances, more accurate than the slower physics-based forecasting tools we’ve traditionally leaned on,” Lowry added.

Clarifying Machine Learning

To be sure, the system is an instance of AI training – a method that has been used in data-heavy sciences like meteorology for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a manner that its model only takes a few minutes to come up with an result, and can operate on a standard PC – in strong contrast to the flagship models that governments have utilized for years that can take hours to run and need the largest supercomputers in the world.

Expert Reactions and Future Advances

Nevertheless, the reality that Google’s model could outperform previous gold-standard traditional systems so quickly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the most intense weather systems.

“It’s astonishing,” said James Franklin, a retired expert. “The data is now large enough that it’s pretty clear this is not a case of chance.”

Franklin noted that while the AI is outperforming all other models on predicting the trajectory of storms globally this year, similar to other systems it sometimes errs on high-end intensity predictions wrong. It struggled with Hurricane Erin previously, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

During the next break, he stated he intends to talk with the company about how it can make the DeepMind output even more helpful for experts by offering extra under-the-hood data they can utilize to evaluate the reasons it is producing its answers.

“The one thing that troubles me is that although these predictions seem to be really, really good, the results of the system is kind of a opaque process,” remarked Franklin.

Broader Industry Trends

There has never been a private, for-profit company that has developed a high-performance forecasting system which allows researchers a view of its techniques – in contrast to most systems which are offered free to the public in their entirety by the governments that created and operate them.

The company is not alone in adopting artificial intelligence to solve challenging weather forecasting problems. The US and European governments are developing their own artificial intelligence systems in the development phase – which have demonstrated better performance over earlier non-AI versions.

Future developments in AI weather forecasts seem to be startup companies tackling previously tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of severe weather and flash flooding – and they are receiving US government funding to do so. One company, WindBorne Systems, is even deploying its proprietary atmospheric sensors to fill the gaps in the national monitoring system.