Satellite-observed microwave radiances provide information on both surface and atmosphere. For operational weather forecasting, information on atmospheric temperature, humidity, cloud and precipitation is directly inferred using all-sky radiance data assimilation. In contrast, information on the surface state, such as sea surface temperature (SST) and sea ice fraction, is typically provided through third-party retrieval products. Scientifically, this is a sub-optimal use of the observations, and practically it has disadvantages such as time delays of more than 48 hours. A better solution is to jointly estimate the surface and atmospheric state from the radiance observations. This has not been possible until now due to incomplete knowledge of the surface state and the radiative transfer that links this to the observed radiances. A new approach based on an empirical state and empirical sea ice surface emissivity model is used here to add sea ice state estimation, including sea ice concentration (SIC), to the European Centre for Medium-range Weather Forecasts atmospheric data assimilation system. The sea ice state is estimated using augmented control variables at the observation locations. The resulting SIC estimates are of good quality and they highlight apparent defects in the existing OCEAN5 sea ice analysis. The SIC estimates can also be used to track giant icebergs, which may provide a novel maritime application for passive microwave radiances. Further, the SIC estimates should be suitable for onward use in coupled ocean-atmosphere data assimilation. There is also increased coverage of microwave observations in the proximity of sea ice, leading to improved atmospheric forecasts out to day 4 in the Southern Ocean.