SynWeather: Weather Observation Data Synthesis across Multiple Regions and Variables via a General Diffusion Transformer

• URL: http://arxiv.org/abs/2511.08291v3
• Date: Sat, 15 Nov 2025 01:42:18 GMT
• Title: SynWeather: Weather Observation Data Synthesis across Multiple Regions and Variables via a General Diffusion Transformer
• Authors: Kaiyi Xu, Junchao Gong, Zhiwang Zhou, Zhangrui Li, Yuandong Pu, Yihao Liu, Ben Fei, Fenghua Ling, Wenlong Zhang, Lei Bai,
• Abstract summary: We introduce SynWeather, the first dataset designed for Unified Multi-region and Multi-variable Weather Observation Data Synthesis.<n> SynWeather covers four representative regions: the Continental United States, Europe, East Asia, and Tropical Cyclone regions.<n>It provides high-resolution observations of key weather variables, including Composite Radar Reflectivity, Hourly Precipitation, Visible Light, and Microwave Brightness Temperature.<n>In addition, we introduce SynWeatherDiff, a general and probabilistic weather synthesis model built upon the Diffusion Transformer framework to address the over-smoothed problem.
• Score: 26.46147741268091
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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