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As Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a major tropical system.

Serving as lead forecaster on duty, he forecasted that in a single day the storm would become 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 quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa did become a system of astonishing strength that tore through Jamaica.

Increasing Dependence on AI Predictions

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his certainty: “Approximately 40/50 AI ensemble members show Melissa reaching a most intense storm. While I am unprepared to forecast that strength at this time given path variability, that remains a possibility.

“It appears likely that a phase of rapid intensification will occur as the storm moves slowly over very warm sea temperatures which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Systems

The AI model is the first AI model focused on hurricanes, and currently the initial to outperform traditional meteorological experts at their own game. Across all tropical systems so far this year, the AI is top-performing – even beating human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 intensity, among the most powerful coastal impacts recorded in almost 200 years of record-keeping across the Atlantic basin. The confident prediction probably provided people in Jamaica additional preparation time to prepare for the catastrophe, potentially preserving people and assets.

How The System Works

The AI system operates through spotting patterns that traditional time-intensive physics-based prediction systems may overlook.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is less expensive and demanding,” stated Michael Lowry, a former forecaster.

“This season’s events has proven in quick time is that the newcomer artificial intelligence systems are competitive with and, in certain instances, superior than the less rapid traditional weather models we’ve relied upon,” he said.

Understanding AI Technology

To be sure, the system is an instance of machine learning – a technique that has been used in research fields like weather science for years – and is not creative artificial intelligence like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a manner that its model only requires minutes to come up with an result, and can do so on a desktop computer – in strong contrast to the primary systems that governments have used for years that can require many hours to process and need some of the biggest supercomputers in the world.

Expert Responses and Upcoming Developments

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

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

Franklin said that while the AI is beating all competing systems on forecasting the future path of hurricanes worldwide this year, like many AI models it sometimes errs on extreme strength forecasts wrong. It struggled with Hurricane Erin previously, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

During the next break, he stated he intends to discuss with the company about how it can enhance the DeepMind output more useful for experts by offering extra internal information they can utilize to evaluate exactly why it is coming up with its answers.

“The one thing that troubles me is that while these forecasts appear really, really good, the output of the model is kind of a opaque process,” said Franklin.

Broader Sector Trends

Historically, no a private, for-profit company that has developed a top-level forecasting system which grants experts a peek into its methods – unlike nearly all systems which are offered free to the public in their full form by the authorities that created and operate them.

The company is not alone in starting to use artificial intelligence to solve difficult weather forecasting problems. The authorities are developing their respective AI weather models in the development phase – which have also shown improved skill over earlier traditional systems.

Future developments in artificial intelligence predictions seem to be 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 have secured federal support to do so. A particular firm, WindBorne Systems, is even launching its proprietary weather balloons to address deficiencies in the US weather-observing network.