AtmosGen: Condition-Aware Synthetic Atmospheric Data and Image Generation for Aviation Applications

Accurate atmospheric data is essential for weather forecasting, aviation safety, and climate research. However, real-world data collection methods such as ra-diosonde launches are limited by high operational costs, sparse temporal availability, and restricted geographical coverage. To address these challenges, this paper proposes AtmosGen (Atmospheric Synthetic Data and Image Generator), a condition-aware syn-thetic atmospheric data and image generation framework that combines numerical data synthesis with atmospheric image generation. The system utilizes historical radiosonde data to generate realistic synthetic atmospheric parameters, including temperature, pres-sure, humidity, wind speed, and altitude, using machine learning-based generative mod-els. In addition, a conditional image generation model is employed to generate atmo-spheric and weather-condition images corresponding to different environmental states such as clear sky, cloudy, foggy, and stormy conditions. To ensure the reliability of the generated data, a compatibility evaluation model is introduced, which verifies the consistency between input atmospheric conditions and the generated images using sta-tistical similarity metrics and regression-based validation. Furthermore, a compara-tive analysis between original radiosonde datasets and model-generated datasets is per-formed using distribution analysis, correlation metrics, and downstream task perfor-mance evaluation. The proposed approach reduces dependency on continuous real-time data acquisition while providing scalable, diverse, and scientifically consistent datasets. This framework is particularly useful for aviation simulations, machine learning model training, and atmospheric research where large labeled datasets are required.