Hurricanes, or tropical cyclones, rank among the most devastating natural disasters. They can level cities and claim countless lives. A major challenge in preparing for these storms lies in their unpredictability. Their strength, trajectory, and impact remain difficult to predict due to the complexity of the atmospheric systems from which they arise.
Now, in a new study published in Physics of Fluids, researchers Qiusheng Li and Feng Hu from the City University of Hong Kong unveiled a new machine-learning approach that could transform the accuracy of hurricane forecasting.
The Challenges with Hurricanes
Hurricanes are most prevalent in specific regions, notably the Atlantic Ocean, the Caribbean Sea, and the central and northeastern Pacific Ocean. The Atlantic hurricane season typically spans from June 1 to November 30, with peak activity occurring between mid-August and late October. During this period, hurricanes frequently develop in the Caribbean Sea, Gulf of Mexico, and the Atlantic Ocean, impacting areas such as Bermuda, eastern Canada, the eastern United States, and parts of Central America.
The destructive potential of hurricanes is immense, with the capacity to devastate entire cities and result in significant loss of life. For instance, Hurricane Katrina in 2005 caused catastrophic damage in New Orleans, leading to over 1,800 fatalities and approximately $125 billion in damages. Similarly, Hurricane Harvey in 2017 brought unprecedented flooding to southeastern Texas, resulting in 68 direct deaths and economic losses estimated at $125 billion. More recently, Hurricane Helene largely took residents of western North Carolina off guard as residual effects of the hurricane caused widespread damage much further inland. These events underscore the severe impact hurricanes can have on human populations and infrastructure.
In recent years, there has been growing concern about the influence of climate change on hurricane behavior. While studies indicate that the overall frequency of hurricanes has not shown a clear trend over the past 150 years, evidence suggests that hurricanes are becoming more intense. Research indicates an increase in the proportion of Category 3 and higher hurricanes and a rise in the number of storms undergoing rapid intensification. This trend is attributed to warmer ocean temperatures and increased atmospheric moisture, conditions that fuel stronger storms.
The Challenges of Boundary Layer Modeling
As experts struggle to improve their models for hurricane prediction, the City University of Hong Kong researchers focused specifically on the prediction of the boundary layer wind field—the region of the atmosphere closest to Earth’s surface, where human activity and storm impact converge.
“We human beings are living in this boundary layer, so understanding and accurately modeling it is essential for storm forecasting and hazard preparedness,” Li said in a recent statement.
Modeling the boundary layer is particularly difficult because it involves interactions between air, land, ocean, and surface-level structures. Traditional forecasting methods rely on massive numerical simulations performed on supercomputers, incorporating vast observational data. Despite these efforts, predictions often fall short of the precision needed for effective disaster response.
A Physics-Informed Machine Learning Solution
Li and Hu’s machine learning algorithm presents a faster, more accurate alternative. Unlike conventional methods, their model uses an advanced physics-informed framework that integrates atmospheric physics equations. This allows it to generate precise wind field predictions using only a small amount of real-world data.
“Only a small amount of real data is required by our model to capture the complex behavior of the wind field of tropical cyclones,” said Hu in the recent press release. “The model’s flexibility and ability to integrate sparse observational data result in more accurate and realistic reconstructions.”
By reconstructing the wind field of a tropical cyclone, their algorithm provides critical data on the storm’s intensity, structure, and potential impact. This detailed information is invaluable for disaster authorities aiming to prepare communities before a storm strikes.
“With more frequent and intense hurricanes due to climate change, our model could significantly improve the accuracy of wind field predictions,” Hu emphasized. “This advancement can help refine weather forecasts and risk assessments, providing timely warnings and enhancing the resilience of coastal communities and infrastructure.”
Toward Real-Time Forecasting
The researchers plan to continue refining their model to incorporate additional observational data and handle the evolving nature of storm winds over time. They also aim to expand its application to various storm events worldwide and integrate it into real-time forecasting systems.
“Our next steps include improving the model’s capability to handle the time evolution of winds and integrating it into real-time systems for weather prediction and risk management,” said Hu.
Kenna Hughes-Castleberry is the Science Communicator at JILA (a world-leading physics research institute) and a science writer at The Debrief. Follow and connect with her on BlueSky or contact her via email at kenna@thedebrief.org