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Table 3 Summary of snow input data for each intensive observation period (IOP) split by mesoscale study
area (MSA)
Dates
MSA
Snowpits
Mean
depth (cm)
Mean
SWE (mm)
Snow
cover (%)
IOP3
20-25th February 2003
North Park
115
14.0
23.1
100
Rabbit Ears
18
225.6
580
100
Fraser
48
77.3
189
100
IOP4
26-30th March 2003
North Park
68
5.3
9.6
57
Rabbit Ears
44
229.0
758
100
Fraser
48
143.2
381
92
Fig. 5
Count of snowpits by depth for each intensive observation period (IOP)
This is consistent with the results of Davenport et al. ( 2012 ) for IOP4 and suggests that the
average snowpack was deeper than that sampled by the snowpits. The majority of North
Park snowpits occurred near roads and might therefore be biased towards thinner snow,
and excluding IOP4 North Park results, the HUT RMSE drops from 8 to 6 K, while Chang
increases from 17 to 18 K. The increase in Chang error is due to its North Park error being
an underestimate and therefore limited in size to below the 14-K observation.
A number of assumptions contribute to the uncertainty in the DT B ; V estimates: static
snow properties, dry snowpits, a simplified vegetation model and unbiased sampling of the
real snow by the snowpits. Furthermore, other work (Picard et al. 2009 ) has found that
effective microwave grain size does not necessarily match that estimated by field obser-
vations. In the light of these assumptions, the HUT simulations are largely consistent with
the satellite observations and therefore justify continued assessment using this model.
The small differences in scene simulated DT B ; V values when using the N-layer or 1-
layer HUT realisations does not necessarily indicate that they are equally valid, as the
averaging over a large number of simulations will suppress the variance. In the Globsnow
assimilation scheme, a single snow profile is taken to represent the snow at each grid point,
rather than the average of 59 profiles used here, and for a single simulation the output
DT B ; V depends on the layering structure provided to the profile.
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