A destructive storm in Spain in 2022 produced large hailstones up to 12 centimetres in diameter
Tomeu Rigo Ribas
A recent breakthrough in hailstone research has provided scientists with a new way to analyze these destructive pieces of ice. Thanks to a CT scanner at a dentist’s office, high-resolution 3D images of the internal structure of large hailstones have been captured for the first time. This development could significantly enhance the ability to predict which storms are likely to produce hailstones.
During a severe storm in Spain in 2022, hailstones up to 12 centimetres in diameter fell, causing widespread damage and casualties. Following the storm, researchers from the Meteorological Service of Catalonia in Spain, led by Carme Farnell Barqué, collected 14 hailstones that had been preserved by locals in their freezers.
Typically, studying the internal structure of hailstones involves cutting them open to examine cross-sections. However, the use of a CT scanner in this study provided a non-invasive way to visualize the complete internal composition of the hailstones. The scans revealed intricate details such as varying densities and layers within the ice.
One surprising finding from the scans was that the nuclei of the hailstones were not at the center, indicating that the thickest part of the stones formed as they were falling rather than during their ascent through different altitudes within the storm.
Julian Brimelow from the Northern Hail Project in Canada emphasized the significance of this research, especially considering the large size of the hailstones analyzed. Understanding the growth process of hailstones could lead to improved forecasting of hail size in future storms, enabling meteorologists to make more accurate predictions.
Looking ahead, researchers like John Allen from Central Michigan University are planning to conduct more extensive studies on hailstones to assess the viability of using CT scanning on a larger scale. The insights gained from this innovative approach could revolutionize our understanding of hail formation and enhance storm forecasting capabilities.
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