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A constellation of microwave sounders named the EUMETSAT Polar System–Sterna (EPS–Sterna) is under study at the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), with the aim of complementing the backbone orbits of the global observing system in low Earth orbit. The satellites of this constellation would be similar to the Arctic Weather Satellite (AWS), which is being developed by the European Space Agency (ESA). The microwave sounder on board AWS is equipped with temperature sounding channels around the 50‐GHz oxygen absorption band, water‐vapour sounding channels around the 183‐ and 325‐GHz absorption bands, and also window channels at 89 and 165 GHz. An observing‐system simulation experiment (OSSE) has been conducted at the Centre National de Recherches Météorologiques (CNRM) to evaluate the impact of this constellation on numerical weather prediction (NWP) at the global scale with the Action de Recherche Petite Echelle Grande Echelle (ARPEGE) model. Two periods ranging from August–October 2021 and December 2021–February 2022 have been chosen to compute the nature run and to run 4D‐Var data assimilation experiments. As validation of the OSSE framework, the impact of a Metop‐B denial experiment in the OSSE is compared with the impact of a Metop‐B denial with real observations. This comparison shows that the Metop‐B denial impacts are very similar in the OSSE and with real observations, with the OSSE slightly overestimating the impact. Then, the impacts of various scenarios for the EPS–Sterna constellation are assessed by computing forecast errors, fractions skill scores, and moist global energy norms, and comparing these with the results of a baseline experiment without the EPS–Sterna constellation. Significant and positive improvements of the forecasts are found up to 96 h, for every variable tested, with an impact increasing with the number of satellites. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Lightning Imager (LI) onboard the Meteosat Third Generation (MTG) satellites will provide total lightning observations continuously over Europe with a spatial resolution of a few kilometers. The objective of this study is to prepare the assimilation of the flash extent accumulation (FEA) measured by LI in the French storm-scale regional AROME NWP system within a new EnVar assimilation scheme, by developing a lightning observation operator. This study relies on pseudo LI FEA observations as LI on MTG is still to be launched in the end of 2022, meaning actual observations will be available by 2023. Since neither flashes nor the electric field are predicted by the AROME NWP system, the observation operator relies on proxy variables to link the flash observations to the prognostic variables of the NWP system. A total of 8 storm parameters were selected from a literature review to be used as proxies. Two different proxy types emerged from this literature review: microphysical and dynamical proxies. The proxies are calculated from 1 h AROME forecasts from the assimilation cycle. Machine learning regression models are used to relate observed FEA and the simulated proxies. The training of the observation operator is performed on a dataset of 44 days and 3 additional days are used for the validation. The data are processed as a climatology over the whole domain (i.e. France) and time period. The performances of each proxy are evaluated by computing Fraction Skill Scores (FSS) between observed FEA and proxy-based FEA. The present study suggests that microphysical proxies seem to be more suited than the dynamical ones to model satellite lightning observations with the AROME NWP system. The performances of multivariate regression models are also evaluated by combining several proxies after a feature selection based on a principal component analysis and a proxy correlation study but no proxies combination yielded better results than microphysical proxies alone. Results are also compared to those obtained with another lightning calibration, i.e., a relationship between lightning and proxies, from the literature but the simulated FEA amplitudes were systematically lower than the observed ones. Finally, different accumulation periods of the FEA had little influence, i.e., similar FSS, on the observation operator's ability to reproduce the observed FEA. [ABSTRACT FROM AUTHOR]