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When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a major tropical system.

As the lead forecaster on duty, he forecasted that in a single day the weather system would intensify into a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued this confident forecast for quick intensification.

However, Papin possessed a secret advantage: AI technology in the form of Google’s new DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa did become a system of astonishing strength that tore through Jamaica.

Increasing Dependence on AI Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his certainty: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa becoming a Category 5 storm. While I am not ready to forecast that strength at this time given path variability, that remains a possibility.

“It appears likely that a phase of rapid intensification is expected as the system drifts over exceptionally hot ocean waters which is the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Systems

The AI model is the pioneer artificial intelligence system focused on hurricanes, and currently the first to outperform standard weather forecasters at their own game. Across all 13 Atlantic storms this season, Google’s model is top-performing – even beating human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at maximum intensity, among the most powerful landfalls recorded in nearly two centuries of record-keeping across the region. The confident prediction probably provided residents additional preparation time to get ready for the catastrophe, potentially preserving people and assets.

How The System Functions

Google’s model operates through spotting patterns that conventional lengthy scientific prediction systems may overlook.

“The AI performs far faster than their traditional counterparts, and the processing requirements is more affordable and demanding,” said Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in quick time is that the newcomer AI weather models are on par with and, in some cases, more accurate than the less rapid physics-based forecasting tools we’ve relied upon,” he said.

Understanding AI Technology

To be sure, Google DeepMind is an instance of machine learning – a method that has been employed in data-heavy sciences like weather science for a long time – and is distinct from generative AI like ChatGPT.

AI training processes mounds of data and extracts trends from them in a manner that its model only requires minutes to come up with an answer, and can operate on a desktop computer – in sharp difference to the flagship models that authorities have utilized for decades that can take hours to process and require the largest high-performance systems in the world.

Professional Reactions and Future Developments

Nevertheless, the reality that the AI could outperform earlier top-tier legacy models so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a former expert. “The data is now large enough that it’s evident this is not just chance.”

Franklin said that while the AI is outperforming all other models on forecasting the future path of hurricanes worldwide this year, similar to other systems it occasionally gets high-end intensity predictions inaccurate. It had difficulty with Hurricane Erin previously, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

During the next break, Franklin said he intends to talk with the company about how it can enhance the AI results more useful for experts by providing extra under-the-hood data they can use to assess exactly why it is producing 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 black box,” said Franklin.

Broader Sector Developments

Historically, no a commercial entity that has developed a top-level weather model which allows researchers a view of its techniques – unlike nearly all systems which are provided free 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 weather forecasting problems. The US and European governments also have their own AI weather models in the works – which have also shown better performance over previous traditional systems.

Future developments in artificial intelligence predictions appear to involve startup companies tackling formerly tough-to-solve problems such as long-range forecasts and improved advance warnings of tornado outbreaks and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also deploying its own atmospheric sensors to address deficiencies in the national monitoring system.