Improving Afternoon Thunderstorm Prediction in Taiwan: Insights from Dense Ground-based GNSS Assimilation

A convective-scale ensemble data assimilation (EDA) system has been developed in Taiwan to improve very short-term heavy rainfall prediction. The ground-based GNSS Zenith Total Delay (ZTD) data provides fast moisture information, which captures the precursor of convection initialization over complex terrain. Focusing on thunderstorm prediction in the Taipei Basin, previous studies have shown that assimilating ZTD data from the Central Weather Administration (CWA) operated stations provides effective moisture adjustment. Incorporating the surface 10-meter wind further exploits the benefit of ZTD assimilation in very short-term precipitation prediction. Including non-CWA-operated stations, there are more than 400 GNSS stations in Taiwan, forming a uniquely dense GNSS observation network. In addition to ZTD observation, the tropospheric gradient (TG) measurement provides spatial moisture variations in the low troposphere. Based on a severe afternoon thunderstorm on 24 June 2022 in the Taipei Basin, we conducted rapid-update data assimilation experiments to investigate the impact of the ground-based GNSS data. Data assimilation was performed over a three-hour period at 30-minute interval to predict a heavy rainfall event lasting two hours.Compared to standard ZTD assimilation using CWA-operated stations, the assimilation of dense ZTD observations improves the moisture representation near the Taipei Basin, which is critical for the timing of convection initialization. For this case, TG observation reveals a strong moisture gradient into an inland river valley upstream of the Basin. Additional TG assimilation enhances moisture, facilitating the rapid convection development and the merging of the convection cells. Consequently, assimilating both dense ZTD and TG leads to significant improvements in the forecasted intensity and location of heavy rain, as well as the forecast performance at a longer lead time. Notably, the impact of TG assimilation is more pronounced when combined with dense ZTD data.