Application of radar data assimilation on convective precipitation forecasts based on water vapor retrieval

Abstract

Based on a short-time heavy rainfall in Anhui and the weather research and forecasting (WRF) model, the water vapor in the initial field of the model is retrieved using the statistical relationships of the reflectivity factor from the Doppler weather radar with the relative humidity and hydrometeor. Three-dimensional variational (3DVAR) assimilation method is used to assimilate the radar reflectivity factor and radial velocity, and then the impact of assimilating retrieved water vapor on the analysis and forecast of the torrential rain is assessed. The results show that, after assimilating the retrieved water vapor, the water vapor field in the model is significantly improved. The water vapor content in the middle layer of the model in the analyzed field is increased, corresponding well with the convective region. Meanwhile, the precipitation distribution during this weather process is successfully simulated. The mesoscale characteristics are better presented by the imageries of radar reflectivity factor, and false echoes are partially reduced. Besides, the prediction of short-time heavy rainfall regions is closer to the actual observations. After assimilating the retrieved water vapor, the simulated one-hour accumulated rainfall is closer to the actual observation, and the fraction skill score (FSS) is higher.

Impacts of Radar Data Assimilation on the Forecast of “12.8” Extreme Rainstorm in Central China (2021)

“…Previous studies have focused on radar data assimilation, and there are three main assimilation methods: variational assimilation, ensemble Kalman filter (EnKF) assimilation, and cloud analysis. Sun and Crook [20] (1997), Gao et al [21] (1999), Hu et al [22] (2006), Li et al [23] (2009), Wan et al [24] (2006), Shu et al [25] (2022), and He et al [26] (2021) have carried out variational assimilation experiments of radar reflectivity or radial velocity data. Their results indicated that heavy precipitation forecast skills were significantly improved after assimilation.…”

Influence of the indirect assimilation of radar reflectivity data using the ensemble Kalman filter on the simulation of a warm-sector squall line

“…It was concluded that on average the assimilation of reflectivity significantly improves the short-term precipitation forecast skill up to 7 h. Fan et al (2013) used three-dimensional variational method in the Beijing rapid updated cycling analysis and forecast system to assimilate the rain and water vapor data derived from the radar reflectivity data, which greatly improved the short-term precipitation forecast skill and extended the lead time to 6 h with positive forecast skills. He et al (2021) adjusted the humidity profile of the model initial field based on the statistical relationship between the radar reflectivity and the humidity profile and found that this method can significantly improve the short-term forecast and nowcast skills of heavy rainfall. In addition, Weygandt et al (2008) used the diabatic digital filter initialization method in the high-resolution rapid refresh system.…”