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.

Rádio Ocultação GNSS: Revisão Teórica, Missões e Produtos

“…Existe uma vasta gama de investigações que ainda se faz necessária nessa área de pesquisa, não apenas em relação às novas missões, mas também quanto às configurações de processamento, metodologias envolvidas na obtenção e validação dos perfis atmosféricos e, ainda, as metodologias de assimilação dos dados de RO-GNSS em modelos atmosféricos. Diversos estudos recentes apresentam importantes contribuições em relação à atmosfera neutra (ANTHES et al, 2022;FU et al, 2022;YANG, 2022) e à ionosfera (BERGSSON;SYNDERGAARD, 2022;SUN et al, 2022;PROL et al, 2022).…”

Impact of Assimilating FORMOSAT-7/COSMIC-2 Radio Occultation Data on Typhoon Prediction Using a Regional Model

“…The RO data with a nonlocal operator contributes to a remarkable improvement in typhoon track prediction through Typhoon Hagupit's (2020) and Haishen's (2020) case studies. Chang and Yang (2022) [38] also assimilated FS7 refractivity but used the WRF local ensemble transform Kalman filter (WRF-LETKF) hybrid DA system. As expected, assimilating with FS7 RO data would improve low-level moisture analysis; however, they found that excluding the RO data below 3 km facilitated better rainfall prediction.…”

“…After FS7 was launched, the data quality was verified by comparing it with other independent data, such as radiosondes, model forecasts, and reanalyses [27][28][29][30][31][32][33][34][35][36][37][38]. As the first summary of the initial FS7 quality assessment, Schreiner et al (2020) [27] confirmed that FS7 RO data meet expectations of high accuracy and precision.…”

Understanding the impact of assimilating FORMOSAT‐7/COSMIC‐2 radio occultation refractivity on tropical cyclone genesis: Observing system simulation experiments using Hurricane Gordon (2006) as a case study

“…Schreiner et al (2020) identified that the low-level negative biases still exist in the FS7-C2 RO data. Chang and Yang (2022) showed that a heavy rainfall event caused by the long-range moisture transport from southern China and South China Sea can be better predicted when the FS7-C2 RO data above 3 km are assimilated.…”