Impact of assimilating Formosat-7/COSMIC-II GNSS radio occultation data on heavy rainfall prediction in Taiwan

Abstract

This study investigates the impact of assimilating Formosat-7/COSMIC-II (FS7/C2) radio occultation (RO) refractivity data on predicting the heavy rainfall event that occurred in Taiwan on August 13, 2019. This event was characterized by heavy rainfall over the coastal region of central and southwestern Taiwan. Our investigation is performed using the Weather Research and Forecasting-Local Ensemble Transform Kalman Filter. Generally, assimilating the RO data increases the amount of moisture over the northern South China Sea (SCS) and the Pearl River area in southern China. It was expected that assimilating the RO data would improve low-level moisture analysis, given that more RO data are available for the lower atmosphere compared to those from Formosat-3/COSMIC-I. However, our results show that the experiment that does not include the RO data below 3 km facilitates better rainfall prediction over Taiwan in terms of the intensity and location of heavy rainfall. This heavy rainfall event can be attributed to moisture transport from the Pearl River area, where the RO data at the altitude of 3–5 km provide effective moisture enhancement to deepen the high-moisture layer. The experiment using the local spectral width (LSW) to conduct the quality control (QC) also helps improve rainfall prediction. However, such an LSW-based QC procedure tends to reject significant amounts of RO data 3 km above the land. Based on this case study, our results show that the QC procedure brings a larger impact to rainfall prediction than counterparts that adjust the observation error variance. A sophisticated QC procedure should be developed to optimize the impact of low-level RO data.