In recent years, the Extended Kalman Filter (EKF) has been gaining more attention in the surface data assimilation (DA) community and has already replaced the older Optimal Interpolation (OI) scheme for the vertical component of the land surface DA system in a number of meteorological institutes. An EKF has been developed within the standalone land‐surface modelling platform SURFace Externalisée (SURFEX) for the initialisation of soil temperature and soil water content based on screen‐level temperature and relative humidity. In this article we present a new combination of the EKF with a basic (using conventional observations only) three‐dimensional variational (3D‐Var) upper‐air assimilation for the limited‐area model ALARO coupled to SURFEX. This new combination is compared to an Open Loop experiment where all initial conditions are interpolated from an analysis of the global numerical weather prediction model Action de Recherche Petite Echelle Grande Echelle (ARPEGE) and to an experiment where the surface is initialised using the EKF, while the upper‐air initial conditions are interpolated from the ARPEGE analysis. The aim of this article is to examine whether the EKF surface assimilation coupled or not with a basic 3D‐Var upper‐air assimilation has an added value compared to the Open Loop, in which the more advanced upper‐air data assimilation of ARPEGE with more observations used is interpolated onto the limited‐area model grid. All set‐ups are verified during a 1‐year period 2013 against soil measurements, screen‐level observations, radiosoundings and merged radar–rain‐gauge precipitation observations. Results indicate that the EKF surface assimilation has positive effects on humidity scores and is able to produce similar or improved scores compared to the Open Loop. While the upper‐air 3D‐Var DA system of ALARO still needs improvements, the potential benefits of the combination of upper‐air and surface assimilation are demonstrated through soil moisture and screen‐level relative humidity verifications.