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As Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a monster hurricane.

As the primary meteorologist on duty, he forecasted 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 forecast for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. True to the forecast, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Increasing Reliance on AI Forecasting

Meteorologists are increasingly leaning hard on 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 AI ensemble members show Melissa becoming a most intense storm. Although I am not ready to forecast that strength at this time given track uncertainty, that is still plausible.

“There is a high probability that a period of rapid intensification is expected as the storm moves slowly over very warm ocean waters which is the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the pioneer artificial intelligence system focused on tropical cyclones, and currently the first to outperform standard meteorological experts at their own game. Through all tropical systems this season, the AI is the best – even beating human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at maximum intensity, one of the strongest landfalls ever documented in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave residents extra time to get ready for the catastrophe, potentially preserving lives and property.

How The Model Works

Google’s model works by identifying trends that traditional time-intensive physics-based prediction systems may miss.

“They do it far faster than their physics-based cousins, and the computing power is less expensive and demanding,” said Michael Lowry, a ex forecaster.

“What this hurricane season has proven in quick time is that the recent AI weather models are on par with and, in certain instances, more accurate than the less rapid traditional forecasting tools we’ve traditionally leaned on,” he added.

Clarifying AI Technology

It’s important to note, Google DeepMind is an instance of machine learning – 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 such a way that its system only takes a few minutes to come up with an result, and can operate on a desktop computer – in strong contrast to the flagship models that authorities have utilized for decades that can take hours to process and need the largest supercomputers in the world.

Professional Reactions and Upcoming Advances

Nevertheless, the fact that the AI could outperform earlier gold-standard legacy models so rapidly 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 data is now large enough that it’s evident this is not just beginner’s luck.”

Franklin said that while the AI is beating all other models on predicting the future path of hurricanes worldwide this year, like many AI models it occasionally gets high-end intensity predictions wrong. It struggled with another storm previously, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

In the coming offseason, he said he intends to discuss with Google about how it can make the DeepMind output even more helpful for experts by providing additional internal information they can use to assess the reasons it is coming up with its answers.

“The one thing that troubles me is that although these forecasts seem to be highly accurate, the results of the model is kind of a opaque process,” remarked Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has produced a top-level forecasting system which grants experts a peek into its techniques – unlike nearly all systems which are provided free to the public in their entirety by the governments that created and operate them.

The company is not the only one in adopting AI to solve challenging weather forecasting problems. The authorities are developing their respective AI weather models in the development phase – which have also shown better performance over previous traditional systems.

Future developments in AI weather forecasts seem to be startup companies tackling formerly tough-to-solve problems such as long-range forecasts and better early alerts of severe weather and flash flooding – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is also deploying its proprietary weather balloons to fill the gaps in the national monitoring system.