'It Was Nuts': The Extreme Tests that Show Why Hail Is a Multibillion-Dollar Problem The costs of a hail damage have ballooned over the past two decades, prompting researchers to resort to extreme measures to understand how these storms destroy buildings. The scars left on houses look like shotgun blasts, sometimes. In the aftermath of major storms, Andrew Shick, owner and chief executive of Illinois-based firm Roofing USA, has driven through suburbs blasted by hail and been left stunned by the damage. Earlier this year, he visited a farm complex in western Illinois where roofs, even sturdy metal ones, were left pockmarked and perforated after 3-inch balls of ice fell from the sky. "It was nuts," he recalls.

This study examines the use of social media and news images to detect and measure hailstones, utilizing pre-trained multimodal large language models. The dataset for this study comprises 474 crowdsourced images of hailstones from documented hail events in Austria, which occurred between January 2022 and September 2024. These hailstones have maximum diameters ranging from 2 to 11cm. We estimate the hail diameters and compare four different models utilizing one-stage and two-stage prompting strategies. The latter utilizes additional size cues from reference objects, such as human hands, within the image. Our results show that pretrained models already have the potential to measure hailstone diameters from images with an average mean absolute error of 1.12cm for the best model. In comparison to a single-stage prompt, two-stage prompting improves the reliability of most models. Our study suggests that these off-the-shelf models, even without fine-tuning, can complement traditional hail sensors by extracting meaningful and spatially dense information from social media imagery, enabling faster and more detailed assessments of severe weather events. The automated real-time image harvesting from social media and other sources remains an open task, but it will make our approach directly applicable to future hail events.

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.

The shape of a severe storm, such as this one, is an important factor in whether the storm produces hail and how large the hailstones are, but current hail-prediction techniques are typically not able to take the storm's entire structure into account. NCAR scientists are experimenting with a new machine-learning technique that can process images to weigh the impact of storm shape and potentially improve hail forecasts. This image is freely available for media and nonprofit use.) The same artificial intelligence technique typically used in facial recognition systems could help improve prediction of hailstorms and their severity, according to a new study from the National Center for Atmospheric Research (NCAR). Instead of zeroing in on the features of an individual face, scientists trained a deep learning model called a convolutional neural network to recognize features of individual storms that affect the formation of hail and how large the hailstones will be, both of which are notoriously difficult to predict.

Facial recognition software could be used to detect hail storms - and their severity. That's according to scientists at the US National Center for Atmospheric Research, who've tested the software's effectiveness on meteorological data. Specifically, they found that a deep learning model called a convolutional neural network can spot the early signs as they happen - better than current methods. The promising results, published in the American Meteorological Society's Monthly Weather Review, could be a game-changer for providing accurate weather warnings. AI: The promising results, published in the American Meteorological Society's Monthly Weather Review, could be a game-changer for providing accurate weather warning Whether or not a storm produces hail hinges on myriad meteorological factors.

The benefits of a well-designed and well-managed digital workplace should be obvious, but it's not enough to simply invest in the latest, greatest technology. The cultural shift required for embedding new ways of working demands a clear business change strategy, and most importantly, a deep understanding of employees' needs. A decent digital workplace enables real-time communication and synchronous, or asynchronous, collaboration that cuts down admin and improves productivity. Social communication tech enables the previously unheard majority to be directly involved with business and so improves agility. And emerging technologies, such as flow technology, pragmatic analytics, AI and chatbots, can analyse masses of data, free people from repetitive tasks, and offer a different interface to systems.