Assimilation of Meteosat Third Generation (MTG) Lightning Imager (LI) observations in AROME-France – Proof of Concept

Abstract

Abstract. This study develops a Lightning Data Assimilation (LDA) scheme for the regional, convection-permitting NWP model AROME-France. The LDA scheme intends to assimilate total lightning, i.e., cloud-to-ground (CG) and inter- and intra-cloud (IC), of the future Meteosat Third Generation (MTG) Lightning Imager (LI). MTG-LI proxy data are created and Flash Extent Density (FED) fields are derived. An FED forward observation operator (FFO) is trained based on modeled, column integrated graupel mass from 24 storm days in 2018. The FFO is successfully verified for 2 independent storm days. With the FFO, the LDA adapts a 1-dimensional Bayesian (1DBay) retrieval followed by a 3-dimensional variational (3DVar) assimilation approach that is currently run operationally in AROME-France for radar reflectivity data. The 1DBay retrieval derives relative humidity profiles from the background by comparing the FED observations to the FED inferred from the background. Retrieved relative humidity profiles are assimilated as sounding data. The evaluation of the LDA comprises different LDA experiments and four case studies. It is found that all LDA experiments can increase the background integrated water vapor (IWV) in regions where the observed FED exceeds the FED inferred from AROME-France outputs. In addition, IWV can be reduced where spurious FED is modeled. A qualitative analysis of 6-hour accumulated rainfall fields reveals that the LDA is capable of locating and initiating some local precipitation fields better than a radar data assimilation (RDA) experiment. However, the LDA also leads to too high rainfall accumulations at some locations. Fractions Skill Scores (FSSs) of 6-hour accumulated rainfall are overall similar for the developed LDA and RDA experiments. An approach aiming at mitigating effects due to differences in the optical extents of lightning flashes and the area of the corresponding cloud was developed and included in the LDA, however, it does not always improve the FSS.

Thunderstorm characteristics with lightning jumps and dives in satellite-based nowcasting

“…(2019) suggest 10-15% higher DE at night than during daytime over the CONUS. (Bateman et al, 2021;Erdmann, 2020) found small differences in GLM day-and nightime DE due to the use of coarse criteria and a limited region, respectively.…”

Study of the Intense Meteorological Event Occurred in September 2022 over the Marche Region with WRF Model: Impact of Lightning Data Assimilation on Rainfall and Lightning Prediction

“…Different techniques have been used for LDA as follows: (a) The forcing of water vapour and other hydrometeors, with both nudging [22][23][24][25][26][27][28] and 3D-Var [21,29,30], (b) retrieving the convective precipitation and latent heat profile by lightning and adjusting the initial three-dimensional thermo-dynamic field of NWP models [31,32], (c) retrieving the radar reflectivity by lightning and assimilating it in NWP models [33][34][35][36], and (d) forcing convection through the adjustment of thermo-dynamical fields [37]. Attempts to suppress spurious convection through lightning data assimilation have also been reported in the literature [38]. All these studies showed different methods to assimilate lightning in NWP, and most of them improved the forecast of intense and/or abundant rainfall typical of convective and severe weather events.…”