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Fig. 2 Brightness temperature
difference as a function of snow
water equivalent (SWE) for the
same snow properties as in
Fig. 1 , except that grain diameter
is varied from 0.2 to 1.0 mm in
0.2 mm increments. The legend
(top left) indicates which line
style refers to each grain size
value. The shaded straight line is
the linear best fit to the first
50 mm SWE worth of the central
grain size value
Table 2 Chang sensitivity cal-
culated from the trend in bright-
ness temperature difference for
the first 100 mm of snow water
equivalent (SWE) for grain
diameters of 0.2-1.0 mm
Grain diameter
(mm)
Chang sensitivity
(mm SWE K -1 )
0.2
14.64
0.4
4.18
0.6
2.47
0.8
1.85
The snow and surface properties
used are those from Fig. 1
1.0
1.55
gradient, or by fitting to an assumed exponential autocorrelation function. There are also
functions for converting from correlation length to optical grain size (e.g., M¨tzler 2000 ;
Wiesmann et al. 2000 ;M ¨ tzler 2002 ).
3 Assimilation of Passive Microwave Observations to Improve Snow Mass Estimation
3.1 Assimilation of Passive Microwave Brightness Temperatures
Microwave-only algorithms retain large uncertainties due to issues with forest coverage
and changes in the scattering properties of snow, driven primarily by the snow's micro-
structure. However, if these effects could be quantified, then an assimilation scheme would
be able to extract information from the retrieved brightness temperatures to improve a
snow analysis.
Sun et al. ( 2004 ) suggested a scheme, which forecasts the snow cover using a LSM
before assimilating SWE estimated from PM. They performed an experiment using a
synthetic truth generated by the LSM versus two alternative model runs with strongly
perturbed initial conditions, one of which assimilated observations from the truth using a
Kalman Filter, and one which was left to run as an open loop. They demonstrated that the
assimilation scheme returned the analysis state close to the truth within 1 week, and then a
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