Assimilating FY-4A Lightning and Radar Data for Improving Short-Term Forecasts of a High-Impact Convective Event with a Dual-Resolution Hybrid 3DEnVAR Method

Abstract

A dual-resolution, hybrid, three-dimensional ensemble-variational (3DEnVAR) data assimilation method combining static and ensemble background error covariances is used to assimilate radar data, and pseudo-water vapor observations to improve short-term severe weather forecasts with the Weather Research and Forecast (WRF) model. The higher-resolution deterministic forecast and the lower-resolution ensemble members have 3 and 9 km horizontal resolution, respectively. The water vapor pseudo-observations are derived from the combined use of total lightning data and cloud top height from the Fengyun-4A(FY-4A) geostationary satellite. First, a set of single-analysis experiments are conducted to provide a preliminary performance evaluation of the effectiveness of the hybrid method for assimilating multisource observations; second, a set of cycling analysis experiments are used to evaluate the forecast performance in convective-scale high-frequency analysis; finally, different hybrid coefficients are tested in both the single and cycling experiments. The single-analysis results show that the combined assimilation of radar data and water vapor pseudo-observations derived from the lightning data is able to generate reasonable vertical velocity, water vapor and hydrometeor adjustments, which help to trigger convection earlier in the forecast/analysis and reduce the spin-up time. The dual-resolution hybrid 3DEnVAR method is able to adjust the wind fields and hydrometeor variables with the assimilation of lightning data, which helps maintain the triggered convection longer and partially suppress spurious cells in the forecast compared with the three-dimensional variational (3DVAR) method. A cycling analysis that introduced a large number of observations with more frequent small adjustments is able to better resolve the observed convective events than a single-analysis approach. Different hybrid coefficients can affect the forecast results, either in the single deterministic or cycling analysis experiments. Overall, we found that a static coefficient of 0.4 and an ensemble coefficient of 0.6 yields the best forecast skill for this event.

A Scheme to Suppress Spurious Convection by Assimilating the "Zero" Column Maximum Vertical Velocity

“…The initial conditions in numerical weather prediction models (NWPs) play a significant role in short‐term weather forecasting. Currently, radar observations (Gan, Yang, Xie, et al., 2021; Gao et al., 2016; Liu, Yang, Lai, et al., 2021; Liu, Yang, Xin, & Wang, 2021; Pu et al., 2009; Yang et al., 2006, 2015), lightning data (Fierro et al., 2012; Gan, Yang, Qiu, et al., 2021; Liu et al., 2020; Qie et al., 2014; Wang et al., 2014), satellite data (Pu et al., 2002), and so on, have been assimilated through various methods to improve the initial field of convective‐scale NWPs, and these studies have achieved good results in improving the hit rate of severe convective systems. However, in actual forecasting, there are often cases of spurious forecasts.…”

Impact of Water Vapor on the Development of a Supercell Over Eastern China

“…In general, the assimilation of lightning data has a shorter impact time on the forecast because it lacks the adjustment for the water vapor environment. Our recent results suggest that lightning data can be transformed into pseudo water vapor observations (Liu et al, 2021a;Liu et al, 2021b) and has been proven to be a potential approach to improve the forecast performance of convection. Therefore, investigating the water vapor characteristics of convective system by assimilating lightning data with the correct vapor condition could be a potential approach to improve supercell forecasts.…”