{"ID":2843432,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.08291","arxiv_id":"2511.08291","title":"SynWeather: Weather Observation Data Synthesis across Multiple Regions and Variables via a General Diffusion Transformer","abstract":"With the advancement of meteorological instruments, abundant data has become available. Current approaches are typically focus on single-variable, single-region tasks and primarily rely on deterministic modeling. This limits unified synthesis across variables and regions, overlooks cross-variable complementarity and often leads to over-smoothed results. To address above challenges, we introduce SynWeather, the first dataset designed for Unified Multi-region and Multi-variable Weather Observation Data Synthesis. SynWeather covers four representative regions: the Continental United States, Europe, East Asia, and Tropical Cyclone regions, as well as provides high-resolution observations of key weather variables, including Composite Radar Reflectivity, Hourly Precipitation, Visible Light, and Microwave Brightness Temperature. In addition, we introduce SynWeatherDiff, a general and probabilistic weather synthesis model built upon the Diffusion Transformer framework to address the over-smoothed problem. Experiments on the SynWeather dataset demonstrate the effectiveness of our network compared with both task-specific and general models.","short_abstract":"With the advancement of meteorological instruments, abundant data has become available. Current approaches are typically focus on single-variable, single-region tasks and primarily rely on deterministic modeling. This limits unified synthesis across variables and regions, overlooks cross-variable complementarity and of...","url_abs":"https://arxiv.org/abs/2511.08291","url_pdf":"https://arxiv.org/pdf/2511.08291v3","authors":"[\"Kaiyi Xu\",\"Junchao Gong\",\"Zhiwang Zhou\",\"Zhangrui Li\",\"Yuandong Pu\",\"Yihao Liu\",\"Ben Fei\",\"Fenghua Ling\",\"Wenlong Zhang\",\"Lei Bai\"]","published":"2025-11-11T14:24:49Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\",\"Transformer\"]","has_code":false}