How Alphabet’s AI Research Tool is Transforming Hurricane Forecasting with Rapid Pace

When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a monster hurricane.

As the primary meteorologist on duty, he forecasted that in just 24 hours the weather system would intensify into a severe hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had ever issued such a bold forecast for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa did become a system of astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a key factor for his confidence: “Approximately 40/50 AI ensemble members show Melissa reaching a Category 5 hurricane. While I am not ready to forecast that intensity at this time due to track uncertainty, that is still plausible.

“It appears likely that a period of quick strengthening is expected as the system moves slowly over exceptionally hot ocean waters 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 tropical cyclones, and currently the first to outperform standard meteorological experts at their own game. Across all 13 Atlantic storms this season, Google’s model is top-performing – surpassing experts on track predictions.

The hurricane ultimately struck in Jamaica at category 5 intensity, one of the strongest coastal impacts recorded in almost 200 years of record-keeping across the region. Papin’s bold forecast probably provided people in Jamaica additional preparation time to get ready for the catastrophe, possibly saving people and assets.

How The System Functions

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

“They do it far faster than their physics-based cousins, and the computing power is more affordable and time consuming,” said Michael Lowry, a former forecaster.

“This season’s events has demonstrated in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, more accurate than the slower traditional weather models we’ve relied upon,” Lowry added.

Clarifying AI Technology

It’s important to note, Google DeepMind is an example of AI training – a technique that has been employed in research fields like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a manner that its system only requires minutes to generate an answer, and can operate on a standard PC – in sharp difference to the primary systems that authorities have utilized for decades that can require many hours to run and need the largest high-performance systems in the world.

Expert Reactions and Upcoming Advances

Nevertheless, the reality that the AI could outperform earlier gold-standard legacy models so quickly is truly remarkable to meteorologists who have dedicated their lives trying to predict the most intense weather systems.

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

Franklin noted that while the AI is outperforming all competing systems on forecasting the trajectory of hurricanes worldwide this year, like many AI models it occasionally gets extreme strength forecasts inaccurate. It struggled with another storm previously, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, Franklin stated he intends to talk with Google about how it can make the DeepMind output more useful for experts by providing extra internal information they can use to assess the reasons it is coming up with its conclusions.

“A key concern that nags at me is that while these predictions seem to be highly accurate, the results of the system is kind of a opaque process,” remarked Franklin.

Wider Sector Developments

There has never been a commercial entity that has developed a top-level forecasting system which allows researchers a view of its techniques – unlike most other models which are provided at no cost to the general audience in their entirety by the governments that created and operate them.

Google is not the only one in starting to use AI to solve challenging meteorological problems. The US and European governments are developing their respective AI weather models in the works – which have demonstrated better performance over earlier traditional systems.

Future developments in AI weather forecasts seem to be startup companies tackling previously tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of severe weather and sudden deluges – and they have secured US government funding to pursue this. One company, WindBorne Systems, is even launching its proprietary weather balloons to fill the gaps in the national monitoring system.