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Abstract: To interpret the snowpack evolution, and in particular to estimate snow water equivalent (SWE), passive microwave remote sensing has proved to be a useful tool given its sensitivity to snow properties. However, the main uncertainties using existing SWE algorithms arise from snow metamorphism which evolves during the winter season, and changes the snow emissivity. To consider the evolution in snow emissivity a coupled snow evolution–emission model can be used to simulate the brightness temperature (TB) of the snowpack. During a dedicated campaign in the winter season, from November to April, of 2007–2008 two surface-based radiometers operating at 19GHz and 37GHz continuously measured the passive microwave radiation emitted through a seasonal snowpack in southern Quebec (Canada). This paper aims at modeling and interpreting this time series of TB over the whole season, with an hourly step, using a coupled multi-layer snow evolution–emission model. The thermodynamic snow evolution model, referred as to Crocus, was driven by local meteorological measurements. Results from this model provided, in turn, the input variables to run the Microwave Emission Model of Layered Snowpacks (MEMLS) in order to predict TB at 19GHz and 37GHz for both vertical (V) and horizontal (H) polarizations. The accuracy of TB predicted by the Crocus–MEMLS coupled model was evaluated using continuous measurements from the surface-based radiometers. The weather conditions observed during the winter season were diverse, including several warm periods with melting snow and rain-on-snow events, producing very complex variations in the time series of TB. To aid our analysis, we identified days with melting snow versus days with dry snow. The Crocus–MEMLS coupled model was able to accurately predict melt events with a success rate of 86%. The residual error was due to an overestimation of the duration of several melt events simulated by Crocus. This problem was explained by 1) a limitation of percolation, and 2) a very long-acting melt of lower layers due to geothermal flux. When the snowpack was completely dry, the global trend of TB during the season was characterized by a decrease of TB due to growth in the snow grain size. During most of the season, Crocus–MEMLS correctly predicted the evolution of TB resulting from temperature gradient metamorphism; the root mean square errors ranged between 2.8K for the 19GHz vertical polarization (19V) and 6.9K for the 37GHz horizontal polarization (37H). However, during dry periods near the end of the season, the values of TB were strongly overestimated. This overestimation was mainly due to a limitation of the growth of large snow grains in the wet snowpack simulated by Crocus. This effect was confirmed by estimating snow grain sizes from the observed TB and the coupled model. The estimated snow grain sizes were larger and more realistic than those initially predicted by the Crocus model. [Copyright &y& Elsevier]