Resolution Wind Field

Resolution wind field research focuses on enhancing the spatial and temporal resolution of wind data for improved accuracy in weather prediction and related applications. Current efforts leverage machine learning, particularly deep learning architectures like generative adversarial networks (GANs) and enhanced super-resolution networks, to upscale lower-resolution wind fields, often incorporating physics-informed constraints to improve realism. This improved resolution is crucial for various applications, including more accurate atmospheric modeling, better wind energy forecasting (reducing reliance on fossil fuel backup), and enhanced trajectory calculations for pollutant dispersion modeling.