首页|Research from Australian Bureau of Meteorology Has Provided New Data on Machine Learning (Radar and environment-based hail damage estimates using machine learning)
Research from Australian Bureau of Meteorology Has Provided New Data on Machine Learning (Radar and environment-based hail damage estimates using machine learning)
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NETL
NSTL
Copernicus Gesellschaft Mbh
Current study results on artificial intelligence have been published. According to news originating from Victoria, Australia, by NewsRx correspondents, research stated, “Large hail events are typically infrequent, with significant time gaps between occurrences at specific locations. However, when these events do happen, they can cause rapid and substantial economic losses within a matter of minutes.” Our news journalists obtained a quote from the research from Australian Bureau of Meteorology: “Therefore, it is crucial to have the ability to accurately observe and understand hail phenomena to improve the mitigation of this impact. While in situ observations are accurate, they are limited in number for an individual storm. Weather radars, on the other hand, provide a larger observation footprint, but current radar-derived hail size estimates exhibit low accuracy due to horizontal advection of hailstones as they fall, the variability of hail size distributions (HSDs), complex scattering and attenuation, and mixed hydrometeor types. In this paper, we propose a new radar-derived hail product developed using a large dataset of hail damage insurance claims and radar observations. We use these datasets coupled with environmental information to calculate a hail damage estimate (HDE) using a deep neural network approach aiming to quantify hail impact, with a critical success index of 0.88 and a coefficient of determination against observed damage of 0.79. Furthermore, we compared HDE to a popular hail size product (MESH), allowing us to identify meteorological conditions that are associated with biases on MESH.”
Australian Bureau of MeteorologyVictoriaAustraliaAustralia and New ZealandCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Copernicus Gesellschaft Mbh
