Assimilation of the Maximum Vertical Velocity Converted From Total Lightning Data Through the EnSRF Method

Abstract

A total lightning data assimilation (LDA) scheme is developed at the cloud‐resolving scale in this study. The LDA scheme assimilates total lightning data through the ensemble square root filter (EnSRF) method based on the relationship between the maximum vertical velocity and the instantaneous flash rate. To verify the effect of LDA, three assimilation experiments based on a convective activity on July 6, 2019 are performed. The results show that all LDA experiments can improve the forecast. LDA improves the water vapor field and provides a warm and moist environment in which convection can develop. LDA can also increase the convergence at low levels and the divergence at upper levels, which leads to intense convection development. However, the LDA experiment that does not consider vertical localization (LDA_no) shows excessive adjustments of the model variables, which leads to overestimation of precipitation. The LDA experiment which adjusts the state variables in the charge zone (LDA_CZ) underestimates the forecast. The LDA experiment using a global group filter (GGF) localization function (LDA_GGF) is optimal in theory and produces a better forecast. The results of LDA_GGF show that the interaction between the cold pool and the strong low‐level vertical wind shear in front of the cold pool is beneficial for triggering new convection in front of the convection, which leads to a rapid shifting in convective activity. In addition, the LDA_GGF scheme is evaluated further for a rainfall process on August 1, 2020 and obtains similar forecast improvements for composite reflectivity and accumulated precipitation.

Impact of Radar Reflectivity and Lightning Data Assimilation on the Rainfall Forecast and Predictability of a Summer Convective Thunderstorm in Southern Italy

“…For cloud-resolving models, different methods have also been implemented. Wang et al [38] and Gan et al [39] assimilated LDA, adjusting the vertical velocity simulated by NWP based on the relationship between the frequency of lightning and cloud top height and on the relationship between cloud top height and maximum updraft [40,41]. Marchand and Fuelberg [42] used LDA to adjust the thermal field at the lower atmospheric levels to force convection.…”

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.…”

“…EnSRF has greater flexibility and can be used to assimilate nonlocal observations, such as column maximum vertical velocity (w max ). Gan, Yang, Qiu, et al (2021) proposed a scheme to assimilate the two-dimensional w max converted from lightning data through the EnSRF method. The results showed that assimilating two-dimensional w max could effectively improve the thermal fields, leading to a moist and warm conditions for convection to develop.…”

Impact of Lightning Data Assimilation on Forecasts of a Leeward Slope Precipitation Event in the Western Margin of the Junggar Basin

“…However, there is no universal relationship between lightning and rainfall, and the relationship between lightning and rainfall is sensitive to storm location and season [17,[29][30][31]. By establishing an empirical relationship between the lightning frequency and vertical velocity, the vertical velocity proxy variable created from lightning data is assimilated into the model with the dynamic nudging and ensemble square root filter (EnSRF) methods [32,33]. The empirical relationships between vertical velocity or temperature and lightning are limited by local climate characteristics and differences in convective processes.…”