{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/srvit-vision-transformers-for-estimating","title":"SRViT: Vision Transformers for Estimating Radar Reflectivity from Satellite Observations at Scale","arxiv_id":"2406.16955","date":"2024-06-20","proceeding":null,"authors":["Jason Stock","Kyle Hilburn","Imme Ebert-Uphoff","Charles Anderson"],"abstract":"We introduce a transformer-based neural network to generate high-resolution (3km) synthetic radar reflectivity fields at scale from geostationary satellite imagery. 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