Research on Three-Dimensional Wind Field Retrieval with Height-Dependent Filtering

Abstract A primary challenge in multiradar three-dimensional (3D) wind field retrieval is the significant error in the vertical wind component. To address this issue, this study derived theoretical models and simulated wind fields to analyze the causes of errors during the retrieval process, revealing the primary sources of retrieval errors and their correlation with retrieval height. Consequently, a height-dependent filtering strategy is proposed to optimize radar observational data for error suppression. Validation experiments utilizing a network of six phased-array radars in Guangzhou, China, indicate that the proposed method reduces the root-mean-square error (RMSE) of direct retrieval by 52.84%–69.11% within the effective coverage area (0–10 km). Furthermore, accuracy improvements of 15.41%–30.94% are observed in the boundary layer, even when applied to physically constrained variational assimilation methods. These findings demonstrate that the proposed strategy is a critical preprocessing step, independent of the specific retrieval method, for obtaining accurate 3D wind fields, thereby improving meteorological analysis and forecasting capabilities. Significance Statement Accurate three-dimensional wind fields are crucial for weather forecasting and hazard warning, yet obtaining them—especially the vertical velocity component at low heights—has been a long-standing challenge. We therefore developed a theoretical model of the retrieval error to uncover its root cause and, based on this, designed a dynamically adjusted filter bank to optimize the results. Validated with both simulated data and real-world observations from a six-radar network, our method is proven to be a powerful tool for generating reliable, dynamically consistent three-dimensional (3D) wind fields, significantly enhancing weather analysis and forecasting capabilities.