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Snow depth (SD) can be retrieved from spaceborne data through linear regression against the microwave brightness temperature difference between 19 and 37 GHz (or similar frequencies). Other methods use snow physical and/or snow electromagnetic (EM) models to estimate SD. Here, we introduce novel retrieval approaches that dynamically combine ancillary SD information (e.g., from snow physical models driven with surface meteorological data) with established algorithms based on regression or EM modeling. The basic idea is to recalibrate regression coefficients (or the effective grain size in the case of EM models) once per week in a simple data assimilation scheme. SD is retrieved from Special Sensor Microwave Imager brightness temperature data and evaluated against in situ observations from 37 stations throughout the Northern Hemisphere. As expected, the SD retrievals perform better with (weekly) ancillary SD inputs from in situ measurements (not used in validation) than with (weekly) ancillary SD inputs from snow physical modeling. The best results are obtained with the regression-based approach using dynamically recalibrated coefficients and ancillary SD inputs from in situ observations (rmse = 6 cm). The regression approach still performs better with the time average of the dynamic coefficients (rmse = 8 cm) than with standard literature values based on climatology ("REGR-CLIM"; rmse = 50 cm). For SD retrieval with an EM model, we obtain results comparable to REGR-CLIM (rmse = 44 cm). Driving the novel regression approaches with SD estimates from snow physical modeling still results in improvements over REGR-CLIM for all approaches (rmse = 15 cm). Comparable SD estimates are obtained from the snow physical model alone.