SynWeather: Weather Observation Data Synthesis across Multiple Regions and Variables via a General Diffusion Transformer
This work addresses the need for unified weather data synthesis for meteorology and climate research, but it appears incremental as it builds on existing diffusion transformer frameworks.
The authors tackled the problem of synthesizing weather observation data across multiple regions and variables, which was limited by existing single-task deterministic models, and introduced SynWeather, a dataset and SynWeatherDiff model that improved synthesis results, as demonstrated in experiments.
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.