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This paper examines the ability of optical reflectance data assimilation toimprove snow depth and snow water equivalent simulations from a chain ofmodels with the SAFRAN meteorological model driving the detailed multilayersnowpack model Crocus now including a two-stream radiative transfer model forsnow, TARTES. The direct use of reflectance data, allowed by TARTES, insteadof higher level snow products, mitigates uncertainties due to commonly usedretrieval algorithms.</br></br>Data assimilation is performed with an ensemble-based method, the SequentialImportance Resampling Particle filter, to represent simulation uncertainties.In snowpack modeling, uncertainties of simulations are primarily assigned tometeorological forcings. Here, a method of stochastic perturbation based onan autoregressive model is implemented to explicitly simulate theconsequences of these uncertainties on the snowpack estimates.</br></br>Through twin experiments, the assimilation of synthetic spectral reflectancesmatching the MODerate resolution Imaging Spectroradiometer (MODIS) spectralbands is examined over five seasons at the Col du Lautaret, located in theFrench Alps. Overall, the assimilation of MODIS-like data reduces by 45 % theroot mean square errors (RMSE) on snow depth and snow water equivalent. Atthis study site, the lack of MODIS data on cloudy days does not affect theassimilation performance significantly. The combined assimilation ofMODIS-like reflectances and a few snow depth measurements throughout the2010/2011 season further reduces RMSEs by roughly 70 %. This work suggeststhat the assimilation of optical reflectances has the potential to become anessential component of spatialized snowpack simulation and forecast systems.The assimilation of real MODIS data will be investigated in future works

On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model