Hurricanes are powerful storms that can cause widespread destruction. Predicting their behavior, like how strong they’ll be or where they’ll hit land, is tough because these storms are influenced by many factors.

Researchers from City University of Hong Kong have used machine learning to better understand and predict hurricanes.

In their study, published in Physics of Fluids, the researchers focused on the boundary layer of the atmosphere. This is the part of the air closest to the Earth’s surface.

This layer is tricky to model because it interacts with the land, sea, and everything on the ground, making weather predictions challenging.

Traditional weather models use huge supercomputers and lots of data, but they often miss the mark.

“Our model employs an advanced physics-informed machine learning framework,” says researcher Feng Hu in a press release issued by American Institute of Physics.

He adds that the model requires only a small amount of real data to capture the complex behavior of the wind field of tropical cyclones. The model’s flexibility and ability to integrate sparse observational data result in more accurate and realistic reconstructions, he says.

Faster information for civil protection

The new machine learning method developed by the researchers uses equations from atmospheric physics to predict the wind patterns inside hurricanes. This method needs less data and can run faster than traditional models.

Understanding the wind field of a hurricane is crucial. It tells us about the storm’s power, its shape, and what kind of trouble it might bring to the shorelines. This model can provide a clearer view of what the hurricane’s wind will do, allowing to prepare better for its arrival.

The researchers believe that with climate change causing more frequent and intense hurricanes, their model could play an important role. This would mean better warnings for people in coastal areas, reducing damage and saving lives. They plan to expand this model to study other types of storms and integrate it into systems that provide real-time weather updates.

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