Finding Faces in Hailstorms - Eos

Hail can be among the most damaging of severe weather phenomena, but predicting whether a passing thunderstorm might start spitting pea-sized (or golf ball–sized) hailstones is notoriously difficult. A new approach using machine learning techniques related to facial recognition technology is giving meteorologists a new tool for mapping how various components of a storm might add up to dangerous hail conditions. Some types of thunderstorms, such as supercells, are more likely to produce hail than others. But the sheer scale of thunderstorms, which can stretch for kilometers and contain multitudes of intrastorm interactions, makes it difficult for computers to accurately model and predict storm behavior, said David John Gagne, a machine learning scientist at the National Center for Atmospheric Research (NCAR) in Boulder, Colo., and lead author of the new study, published in Monthly Weather Review. Drawing upon machine learning technology sometimes used to identify features of individual faces, Gagne and colleagues at NCAR trained a deep learning model called a convolutional neural network to recognize various storm features known to produce hail.