Improving forecast of “21.7” Henan extreme heavy rain by assimilating high spatial resolution GNSS ZTDs

Abstract

Short-term forecasting of extreme weather is crucial for disaster warning and prevention. Many extreme weather events are often accompanied by significant water vapor changes, therefore, assimilating high-precision, high-resolution water vapor observations into numerical models is essential. This study explores the impact of GNSS ZTD assimilation on short-term forecasting of extreme weather using the WRF model on the case of “21.7” Henan extreme heavy rain. The impacts of GNSS ZTD assimilation on model fields and forecast results are analyzed, compared with scenarios where no data or only conventional observational data are assimilated. The results indicate that GNSS products outperform radiosonde data in temporal and spatial resolution, significantly affecting humidity fields in assimilation and providing more detailed water vapor distribution. In terms of precipitation forecasting, the analysis of POD, FAR, and ETS scores shows that GNSS data assimilation primarily impacts moderate to heavy rainfall for this case. During most simulation periods, the scores are higher when GNSS products are assimilated, with the most notable improvements observed at the threshold of 30 mm for 3-h accumulated precipitation, where ETS scores increase by an average of 21 %. However, despite the general improvement in precipitation forecast accuracy, limitations remain in forecasting peak rainfall periods.