Improving Afternoon Thunderstorm Prediction over Complex Terrain with the Assimilation of Dense Ground-Based Observations: Four Cases in the Taipei Basin

Abstract

Abstract In this study, we investigate the impact of assimilating densely distributed Global Navigation Satellite System (GNSS) zenith total delay (ZTD) and surface station (SFC) data on the prediction of very short-term heavy rainfall associated with afternoon thunderstorm (AT) events in the Taipei basin. Under weak synoptic-scale conditions, four cases characterized by different rainfall features are chosen for investigation. Experiments are conducted with a 3-h assimilation period, followed by 3-h forecasts. Also, various experiments are performed to explore the sensitivity of AT initialization. Data assimilation experiments are conducted with a convective-scale Weather Research and Forecasting–local ensemble transform Kalman filter (WRF-LETKF) system. The results show that ZTD assimilation can provide effective moisture corrections. Assimilating SFC wind and temperature data could additionally improve the near-surface convergence and cold bias, further increasing the impact of ZTD assimilation. Frequently assimilating SFC data every 10 min provides the best forecast performance especially for rainfall intensity predictions. Such a benefit could still be identified in the earlier forecast initialized 2 h before the start of the event. Detailed analysis of a case on 22 July 2019 reveals that frequent assimilation provides initial conditions that can lead to fast vertical expansion of the convection and trigger an intense AT. This study proposes a new metric using the fraction skill score to construct an informative diagram to evaluate the location and intensity of heavy rainfall forecast and display a clear characteristic of different cases. Issues of how assimilation strategies affect the impact of ground-based observations in a convective ensemble data assimilation system and AT development are also discussed. Significance Statement In this study, we investigate the impact of frequently assimilating densely distributed ground-based observations on predicting four afternoon thunderstorm events in the Taipei basin. While assimilating GNSS-ZTD data can improve the moisture fields for initializing convection, assimilating surface station data improves the prediction of rainfall location and intensity, particularly when surface data are assimilated at a very high frequency of 10 min.