Assimilation of Satellite Snow Data
The problem of how to predict snow water equivalent (SWE) using numerical models still
requires considerable attention. EOS satellite-based remotely sensed snow data provide
more realistic information about the snow distribution and quantity than the model
predictions. In studies involving the SAST model coupled with the NCAR GCM (CCM3),
we replaced the modeled snow cover over the Rocky Mountains with satellite SMMR monthly
snow coverage and SWE data. Significant consequences in terms of both the land surface
and atmospheric processes were observed: (1) previously overestimated soil moisture
became much closer to the long-term reanalysis values, (2) runoff in the Missouri
River basin decreased, (3) regional surface temperature from March to September
increased 1 ° -1.5 ° C and became closer to the observations, and (4)
summer (JJA) precipitation in the SWUS and Great Plains improved from original
substantial underestimation ( Jin, 2002 ). We intend to follow up on
these promising results by investigating the physical processes giving rise to
the observed changes and implementing appropriate assimilation techniques using
the EOS snow data.
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When assimilated into CCM3, the community climate model, MODIS snow information resulted in significant changes of model's prediction of precipitation for long-term simulation.
MODIS snow products are available
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Glossary
SWE
Links
National Snow and Ice Data Center (NSIDC)
MODIS Snow and Ice Mapping Project at Goddard
SNOTEL Network
Snow maps from NOHRSC
Snow analysis from NOHRSC
