Beyond the Blur: Variational Techniques for Radar Gridding and 3D Wind Retrieval

Observations made by weather radars play a central role in many aspects of meteorological research and forecasting. These applications often require radar data to be provided on a Cartesian grid, necessitating a coordinate transformation and interpolation from the radar’s native spherical geometry using a process known as gridding. In this study, we introduce a variational gridding method and, through a series of theoretical and real data experiments, show that it outperforms existing methods in terms of data resolution, noise filtering, spatial continuity, and more. Additionally, we outline extensions to the framework, including radar mosaic generation and 3D wind retrieval using Doppler velocities. We underscore the limitations of existing gridding methods, such as Cressman weighted average and nearest neighbor/linear interpolation, and illustrate the potential for significant improvements in radar-based severe weather products, ranging from nowcasting to climatological scales.