Evaluating the Performance of Lightning Data Assimilation from BLNET Observations in a 4DVAR-Based Weather Nowcasting Model for a High-Impact Weather over Beijing

Abstract

The Beijing Broadband Lightning Network (BLNET) was successfully set up in North China and had yielded a considerable detection capability of total lightning (intracloud and cloud to ground) over the regions with complex underlying (plains, mountains, and oceans). This study set up a basic framework for the operational application of assimilating total lightning activities from BLNET and assesses the potential benefits in cloud-scale, very short-term forecast (nowcasting) by modulating the vertical velocity using the 4DVar technique. Nowcast statistics aggregated over 11 cycles show that the nowcasting performances with the assimilation of BLNET lightning datasets outperform RAD and the assimilation of GLD360 (Global Lightning) datasets. The assimilation of BLNET data improves the model s dynamical states in the analysis by enhancing the convergence and updraft in and near the convective system. To better implement of assimilating real-time lightning data, this study also conducts sensitivity experiments to investigate the impact of the horizontal length scale of a distance-weighted interpolation, binning time intervals, and different vertical profile or distance weights prior to the DA. The results indicate that the best forecast performance for assimilating BLNET lightning datasets is obtained in a 4DVar cycle when the lightning accumulation interval is 3 min, the radius of horizontal interpolation is 5 × 5, and the statistically vertical velocity profile and the distance weights obtained from cumulus cloud.