This paper presents a new application of assim-ilating lidar signals to aerosol forecasting. It aims at in-vestigating the impact of a ground-based lidar network on the analysis and short-term forecasts of aerosols through a case study in the Mediterranean basin. To do so, we em-ploy a data assimilation (DA) algorithm based on the opti-mal interpolation method developed in the POLAIR3D chem-istry transport model (CTM) of the POLYPHEMUS air qual-ity modelling platform. We assimilate hourly averaged nor-malised range-corrected lidar signals (PR 2) retrieved from a 72 h period of intensive and continuous measurements performed in July 2012 by ground-based lidar systems of the European Aerosol Research Lidar Network (EAR-LINET) integrated into the Aerosols, Clouds, and Trace Published by Copernicus Publications on behalf of the European Geosciences Union. 12032 Y. Wang et al.: Assimilation of lidar signals gases Research InfraStructure (ACTRIS) network and an ad-ditional system in Corsica deployed in the framework of the pre-ChArMEx (Chemistry-Aerosol Mediterranean Ex-periment)/TRAQA (TRAnsport à longue distance et Qualité de l'Air) campaign. This lidar campaign was dedicated to demonstrating the potential operationality of a research net-work like EARLINET and the potential usefulness of assim-ilation of lidar signals to aerosol forecasts. Particles with an aerodynamic diameter lower than 2.5 µm (PM 2.5) and those with an aerodynamic diameter higher than 2.5 µm but lower than 10 µm (PM 10−2.5) are analysed separately using the li-dar observations at each DA step. First, we study the spatial and temporal influences of the assimilation of lidar signals on aerosol forecasting. We conduct sensitivity studies on al-gorithmic parameters, e.g. the horizontal correlation length (L h) used in the background error covariance matrix (50 km, 100 km or 200 km), the altitudes at which DA is performed (0.75–3.5 km, 1.0–3.5 km or 1.5–3.5 km a.g.l.) and the assim-ilation period length (12 h or 24 h). We find that DA with L h = 100 km and assimilation from 1.0 to 3.5 km a.g.l. dur-ing a 12 h assimilation period length leads to the best scores for PM 10 and PM 2.5 during the forecast period with refer-ence to available measurements from surface networks. Sec-ondly, the aerosol simulation results without and with lidar DA using the optimal parameters (L h = 100 km, an assim-ilation altitude range from 1.0 to 3.5 km a.g.l. and a 12 h DA period) are evaluated using the level 2.0 (cloud-screened and quality-assured) aerosol optical depth (AOD) data from AERONET, and mass concentration measurements (PM 10 or PM 2.5) from the French air quality (BDQA) network and the EMEP-Spain/Portugal network. The results show that the simulation with DA leads to better scores than the one with-out DA for PM 2.5 , PM 10 and AOD. Additionally, the com-parison of model results to evaluation data indicates that the temporal impact of assimilating lidar signals is longer than 36 h after the assimilation period.