Wind Field

Wind field research focuses on accurately measuring, predicting, and modeling wind patterns at various scales, from urban microclimates to global atmospheric circulation, primarily to improve weather forecasting and support applications like aviation and renewable energy. Current research emphasizes developing advanced algorithms, including neural networks (e.g., DeepKriging, Fourier Neural Operators) and physics-informed machine learning models, to address challenges in data acquisition, processing (e.g., data cleaning and super-resolution), and real-time prediction, particularly in complex terrains. These improvements are crucial for enhancing the accuracy and resolution of wind field data, leading to better weather prediction, more efficient transportation systems (e.g., eVTOL aircraft path planning), and optimized infrastructure design.