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Sea ice is a crucial component for short-, medium- and long-term numerical weather predictions. Most importantly, changes of sea ice coverage and areas covered by thin sea ice have a large impact on heat fluxes between the ocean and the atmosphere. L-band brightness temperatures from ESA's Earth Explorer SMOS (Soil Moisture and Ocean Salinity) have been proven to be a valuable tool to derive thin sea ice thickness. These retrieved estimates were already successfully assimilated in forecasting models to constrain the ice analysis, leading to more accurate initial conditions and subsequently more accurate forecasts. However, the brightness temperature measurements can potentially be assimilated directly in forecasting systems, reducing the data latency and providing a more consistent first guess. As a first step towards such a data assimilation system we studied the forward operator that translates geophysical parameters provided by a model into brightness temperatures. We use two different radiative transfer models to generate top of atmosphere brightness temperatures based on ORAP5 model output for the 2012/2013 winter season. The simulations are then compared against actual SMOS measurements. The results indicate that both models are able to capture the general variability of measured brightness temperatures over sea ice. The simulated brightness temperatures are dominated by sea ice coverage and thickness changes are most pronounced in the marginal ice zone where new sea ice is formed. There we observe the largest differences of more than 20 K over sea ice between simulated and observed brightness temperatures. We conclude that the assimilation of SMOS brightness temperatures yields high potential for forecasting models to correct for uncertainties in thin sea ice areas and suggest that information on sea ice fractional coverage from higher-frequency brightness temperatures should be used simultaneously.