Seven of the most common radiometer algorithms, used to compute the sea-ice concentration, were compared with ScanSAR data estimates of ice concentration. The focus was on the near-100% ice cover in winter. On a climatological timescale the differences between algorithms amounts to 14% and 22% of the down-going trend in winter Arctic sea-ice extent and area, respectively. The climatological changes in atmospheric and water surface emissivity primarily affect the extent trend while the changes in sea-ice surface emissivity affect the sea-ice area trend. In other words there is a climatic trend in the sea-ice time series related to changes in the snow cover and sea-ice surface properties and the Arctic atmosphere. Reliable estimates of atmospheric cloud liquid water and the ice brightness temperature variability are not readily available and it is therefore important to find ice concentration algorithms that are least sensitive to these atmospheric and surface properties. Other parameters, such as atmospheric water vapour and open ocean surface wind, are quantified rather well by numerical weather prediction models. It is therefore feasible to correct brightness temperatures for the influence of these effects using radiative transfer models before computing the ice concentration. The joint NSIDC and EUMETSAT reanalysis project is an extension of existing Ocean and Sea Ice SAF (OSI SAF) plans to reanalyse the SSM/I record (1987 to present). The entire level-1 dataset was purchased from Remote Sensing Systems by EUMETSAT for use in the SAF network. Cooperation with NSIDC includes extension with NIMBUS 7 SMMR back to November 1978 and the collaboration further defines a common dataset and standards. The processing includes atmospheric correction of brightness temperatures for open ocean surface wind roughness and atmospheric water vapour. A set of three different ice concentration algorithms has been selected due to their low sensitivity to cloud liquid water and ice surface emissivity. A new procedure has been developed where tie points for the ice concentration algorithms are selected dynamically in time and space as a novel method to mitigate intersensor differences, sensor drift and interannual variability. The dataset including error bars is compared with ice charts and other satellite sea-ice datasets. Initial results are shown at the conference.