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Abstract:
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 = 6cm). 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.