A reanalysis can be considered the most complete representation of the atmospheric state given all available observations as well as the model background. Via data assimilation, a reanalysis allows assessing the non-local influence of observations on the time evolution of the modeled state, while aiming at conserving the physicality of the model. Here, we present the pioneering, 3-months convective-scale campaign reanalysis of the 'Swabian MOSES 2023' campaign, consisting of a control (CTRL) dataset without and a campaign (CMPG) dataset with additional field campaign observations from the mobile atmospheric measurement platform KITcube during June, July and August 2023. Both are computed at hourly resolution on a 2.2 km grid centered over Germany with the quasi-operational data assimilation framework by Deutscher Wetterdienst which is based on a Local Ensemble Transform Kalman Filter. CMPG incorporates additional ground-based remote-sensing and in situ observations from the southwest German mountain ranges of the Black Forest and Swabian Jura, a region with moderately complex terrain. The unprecedentedly dense network of 12 Doppler wind lidars, one X-band precipitation radar, two additional radiosounding sites, Global Navigation Satellite Systems receivers, and meteorological masts increases the number of observations in the approximately 10⁵ km² campaign area by 45 % compared to the operational measurement network. As expected, the assimilation reduces the difference between observations and model fields. Specifically, the standard deviation of the observation-to-model difference across all additional observations is reduced by 10 % to 35 % after assimilation. Updates of the model background are predominantly introduced in the lower troposphere and in the campaign region itself, but differences between CMPG and CTRL extend all over Germany, eastern France, and northern Italy. Mean absolute differences in the campaign region reach up to the order of 1.5 m s^−1 in windspeed, 0.4 K in temperature, and 0.7 g kg^−1 in humidity. This pioneering campaign reanalysis can be employed to quantify the influence of individual observations and different observation types and to diagnose potential model deficiencies in complex terrain. It further serves as a high-resolution reference for detailed case studies targeting high-impact weather and provides a basis for re-forecast experiments to quantify observation impact on forecasts.