A high-resolution divergence and vorticity dataset in Beijing derived from radar wind profiler mesonet measurements

Abstract

Low-level convergence and cyclonic circulation are one of the most important dynamic variables in governing the initiation and development of convective storms. Our ability to obtain high-resolution horizontal divergence and vertical vorticity profiles, nevertheless, remains limited largely due to the lack of vertical wind observations. To fill this data gap, a high-density mesonet consisting of six radar wind profiler (RWP) sites has been operated in Beijing, which has allowed for continuous observations of three-dimensional winds with high vertical resolution. This paper aims to produce a temporally continuous horizontal divergence and vertical vorticity dataset at the vertical resolution of 120 m, which is derived from horizontal winds measured by the RWP mesonet in Beijing using the triangle method. This dataset is generated at intervals of 6 min for the whole year of 2023, covering the altitude range of 0.51–4.11 km. The dynamic variables from RWP mesonet are found to scatter sharply as opposed to those from ERA5 that are concentrated around zero, especially at high altitudes. In particular, the negative divergence and positive vorticity are detected in the low-level troposphere up to 1 h in advance of the occurrence of rainfall events, and their magnitudes increasingly become greater when the time comes closer to the rainfall onset, exhibiting the key role that the dataset plays in rainfall nowcasting. This is indicative of, to some extent, the effectiveness of the high-resolution divergence and vorticity dataset in Beijing. The dataset is publicly available at https://doi.org/10.5281/zenodo.15297246 (Guo and Guo, 2024), which is of significance for a multitude of scientific research and applications, including convection initiation and air quality forecasting. Therefore, the findings highlight the urgent need for exploiting the dynamic variables from RWP mesonet measurements to better characterize the pre-storm environment.