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Initial and boundary conditions of dust are still a missing component in atmospheric modeling. In this context, dust models are usually initialized based on their own previous forecasting cycle. As it is obvious, even at the idealized hypothesis of a perfect model run, this approach implies the propagation of numerical diffusion errors. However, recent improvements in remote sensing retrievals of dust optical depth allow the timely generation of dust fields that can be used for assimilation in forecasting atmospheric modeling systems. In this work we present the methodology and preliminary results for the application of MSG/SEVIRI dust retrievals in the atmospheric model NMME-DREAM. First results of the assimilation method are compared with ground photometers (AERONET) and LIDAR (PollyXT) systems during Charadmexp campaign (15 June-15 July 2014). Significant improvement is found mainly over dust sources in Africa and Arabia deserts. The introduction of satellite assimilation methods in dust models provides an additional tool for the improvement of our understanding on the dust-atmosphere interactions and on their possible implications for climate change.