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The reliance on distributed energy- and mass-balance snow models as runoff forecasting tools has been increasing. Compared to traditional, conceptual forecasting approaches, these physics-based tools are robust to conditions that deviate from historic norms and offer improved performance in potentially dangerous rain-on-snow events. The physics-based simulations, however, depend on a large suite of accurate forcing data. Current numerical weather prediction products are capable of supplying the full range of required data, but systematic biases are often present. Data assimilation presents a means of compensating for such errors as well as potential snow model errors, yet currently available data assimilation techniques have limited usefulness in these snow models. This study introduced an alternative technique that similarly uses observations to update and improve simulations. As such, it is the first method to incorporate point snow observations in a fully distributed, physics-based, multilayer snow model while conserving mass and maintaining physically consistent layer states that are in accord with observations. At the core of this technique is an ensemble of predetermined perturbations to the model forcings termed the bias-detecting ensemble. Ensemble members were evaluated using observed snow depths to ascertain potential biases at nearly 300 sites across Switzerland. The bias assessments were distributed to 38 independent sites and incorporated into the model. Tests were conducted over three winter seasons using two numerical weather prediction-based products with varying quality. Averaged across the 38 sites and 3 seasons, the Nash-Sutcliffe efficiency score for bias-detecting ensemble-corrected snow depth was 0.98 compared to 0.81 without the bias-detecting ensemble method.