Effect of GNSS radio occultation observations on the prediction of the 2021 Henan rainstorm

Accurately predicting heavy rainstorms remains challenging due to limited spatial and temporal measurements. Nowadays, space-borne Global Navigation Satellite System (GNSS) radio occultation (RO) data provides high spatial-resolution atmospheric parameters, which can improve the precision of heavy rainfall prediction. This study investigates the impact of GNSS radio occultation observations on forecasting the extremely heavy rainfall that occurred in Henan, China, on July 20, 2021. We assimilate GNSS radio occultation data from Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2), MetOp-A/B/C, and Fengyun (FY)-3C GNOS in Weather Research and Forecasting Model Data Assimilation (WRFDA) three-dimensional framework (3DVAR) system, using the local refractivity operator. Control experiment (CNTL) and RO are designed to assess the impact of GNSS radio occultation on this extreme rainfall prediction, and RO + GNOS is conducted to further evaluate the influence of GNSS RO data onboard FY-3C. The fractions skill score (FSS) is used to quantify the accuracy of predicted precipitation at given thresholds. The results demonstrate that assimilating GNSS radio occultation data improves precipitation forecasts in terms of the distribution and quantity, due to more precise initial conditions for the moisture field. The study also finds that RO and RO + GNOS produce similar increments and outperform the CNTL, indicating a more accurate humidity field near Henan and more explicit water vapor channels. Moreover, the study reveals that assimilating additional data from GNOS onboard FY-3C significantly enhances the prediction of this record-breaking rainfall.

Automated summary

This study shows that assimilating GNSS radio occultation (RO) data into weather models can improve the accuracy of predicting extreme heavy rainfall. The impact of using GNOS data from FY-3C satellite is also investigated, and it is found to significantly enhance the prediction of the record-breaking rainfall.