We describe a new method to infer the presence or absence of snow cover when it is obscured to remote sensing by clouds. The inference is based on the spatial distribution of snow water equivalent (SWE) estimated by a physical snow model. The snow model accounts for surface and internal snowpack energy exchanges and mass exchanges including snow accumulation, sublimation, rainfall, and meltwater outflux. We examine two approaches to using the modeled SWE information to infer snow cover: (1) we directly categorize areas with modeled SWE greater than zero as snow covered and areas with zero SWE as no snow; (2) we classify the modeled SWE field into a binary map of snow covered/no snow, using maximum likelihood logistic estimation (LR). Here the modeled SWE serves as a semicontinuous independent variable, and the binary dependent variable consists of observed snow cover derived from all available ground observations of snow depth and water equivalent, and from samples of snow cover randomly selected from cloud-free areas of a satellite snow cover product. We demonstrate these methods for a 504,000 km2 region in the north central United States, over a 4 week period during March and April 1997. The snow model was run hourly on a 4 km grid using data normally available in near real time, including numerical weather analysis products, satellite-derived insolation products, and ground observations of precipitation. We tested two dependent variable data configurations for the LR approach to simulate (1) typical conditions when remotely sensed snow cover observations are available to help develop the logistic model and (2) worst-case conditions where only ground-based data are available. Averaged over the study period, all three methods, the direct SWE, the worst-case LR, and the typical LR, yielded comparable results in the range of 78--80% accuracy when compared to satellite-observed snow cover maps. The results of this study demonstrate that a relatively simple, spatially distributed, physically based snow model is capable of providing useful snow cover information in an operational environment. ¿ 1999 American Geophysical Union