28 Jul 2022
28 Jul 2022

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

Felix Erdmann1,2, Olivier Caumont1,3,, and Eric Defer4, Felix Erdmann et al.
  • 1CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
  • 2Royal Meteorological Institute, 3 av Circulaire, 1180 Brussels, Belgium
  • 3Météo-France, Direction des opérations pour la prévision, Toulouse, France
  • 4Laboratoire d’Aérologie, 14 avenue Edouard Belin, 31400 Toulouse, France
  • These authors contributed equally to this work.

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.

Felix Erdmann et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-637', Anonymous Referee #1, 19 Aug 2022
  • RC2: 'Comment on egusphere-2022-637', Anonymous Referee #2, 27 Sep 2022

Felix Erdmann et al.

Felix Erdmann et al.


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Short summary
This work develops a novel lightning data assimilation (LDA) technique to make use of Meteosat Third Generation (MTG) Lightning Imager (LI) data in a regional, convection-permitting numerical weather prediction model. The approach combines statistical Bayesian and 3-dimensional variational methods. Our LDA can promote missing convection and suppress spurious convection in the initial state of the model, and has similar skill to the operational radar data assimilation for rainfall forecasts.