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As Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it was about to grow into a monster hurricane.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the storm would intensify into a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had ever issued this confident forecast for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the guise of Google’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa did become a storm of remarkable power that tore through Jamaica.

Increasing Reliance on AI Forecasting

Forecasters are heavily relying upon Google DeepMind. During 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 reaching a most intense storm. While I am not ready to forecast that intensity yet given path variability, that is still plausible.

“It appears likely that a period of rapid intensification will occur as the system moves slowly over very warm sea temperatures which represent the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the pioneer artificial intelligence system focused on tropical cyclones, and currently the initial to outperform standard weather forecasters at their own game. Across all 13 Atlantic storms so far this year, Google’s model is the best – even beating human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at maximum strength, one of the strongest coastal impacts ever documented in nearly two centuries of data collection across the Atlantic basin. The confident prediction probably provided residents extra time to get ready for the catastrophe, possibly saving people and assets.

How The System Works

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

“They do it far faster than their physics-based cousins, and the processing requirements is less expensive and time consuming,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in short order is that the newcomer artificial intelligence systems are on par with and, in some cases, more accurate than the less rapid traditional weather models we’ve relied upon,” he added.

Clarifying AI Technology

It’s important to note, the system is an example of AI training – a technique that has been used in research fields like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

AI training takes large datasets and extracts trends from them in a such a way that its system only takes a few minutes to come up with an answer, and can operate on a desktop computer – in sharp difference to the primary systems that governments have utilized for decades that can require many hours to process and require the largest supercomputers in the world.

Expert Reactions and Future Advances

Nevertheless, the reality that Google’s model could outperform earlier gold-standard traditional systems so rapidly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the world’s strongest weather systems.

“It’s astonishing,” commented James Franklin, a retired expert. “The sample is now large enough that it’s evident this is not a case of beginner’s luck.”

He said that although the AI is beating all other models on predicting the future path of storms globally this year, similar to other systems it sometimes errs on extreme strength predictions wrong. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to category 5 above the Caribbean.

In the coming offseason, he said he plans to discuss with the company about how it can enhance the DeepMind output even more helpful for forecasters by offering additional under-the-hood data they can utilize to evaluate exactly why it is producing its conclusions.

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

Broader Sector Developments

Historically, no a private, for-profit company that has produced a top-level weather model which allows researchers a view of its methods – in contrast to nearly all systems which are offered free to the general audience in their full form by the governments that created and operate them.

The company is not alone in starting to use artificial intelligence to solve difficult weather forecasting problems. The US and European governments also have their own AI weather models in the development phase – which have also shown improved skill over previous non-AI versions.

Future developments in artificial intelligence predictions appear to involve new firms taking swings at previously tough-to-solve problems such as long-range forecasts and improved advance warnings of severe weather and flash flooding – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even deploying its proprietary weather balloons to fill the gaps in the US weather-observing network.