Assimilation of Satellite Lightning Data in a Storm-Scale Numerical Weather Prediction System Using a 3D-EnVar Data Assimilation Scheme

Abstract

Abstract The objective of this study is to prepare the assimilation of the Meteosat Third Generation Lightning Imager (MTG-LI) observations in the regional numerical weather prediction model AROME-France using a 3D-EnVar data assimilation algorithm. As opposed to the current operational configuration of AROME-France data assimilation system, the hydrometeor specific contents are added in the control variable and are thus updated during the assimilation process. Consequently, a lightning observation operator based on the specific contents of snow and graupel is used. As the first MTG satellite was launched in December 2022, the real MTG-LI observations are not available yet, and proxy spaceborne lightning observations generated from ground-based lightning detection network are used. We assess the forecast skills of experiments assimilating lightning in a close to operational configuration by comparing the results to a reference experiment that does not assimilate lightning. In the studied cases, the frequency bias and fraction skill score are improved for brightness temperatures forecasts lower than 280 K up to 4 h after the assimilation, implying a better description of the general cloud cover. The convective cores, identified by brightness temperatures lower than 220 K, are better captured in the first forecast hour but this improvement is not maintained through time and quickly fades.