WMOD00504

• Title: Papers Presented at the 17th Annual Canadian Meteorological and Oceanography Congress
• Date: 5/1/1983
• Weather Modification - Doc Type: Report

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3.3 Snow Spatial Variation

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An analysis of the' climate station data was conducted
to examine the gross spatial characteristics of the
regional snowfall. The sparse measurement network
and complex terrain makes any data interpolation be-
tween points questionable but some patterns may be
apparent. Figure 11 shows the computer-contoured
average annual snowfall in the project region. This
contouring includes prairie stations and is based on a
one-pass weighted-average data smoothing.

The map shows that maximum snowfall amounts
occur over the mountain regions, as expected. A fairly
steep gradient over the drier Columbia river valley is
indicated. Snowfall amounts gradually decrease across
the Alberta foothills and into the lower prairie regions.
The effect of the limited and irregular network is sug-
gested by the apparent lower values over the divide
southwest of Banff. This feature may be due more to
network sparseness rather than any real precipitation
patterns.

The variability of snowfall frequency is indicated by
the annual number snowfall days (Fig. 12). Two data-
smoothings were performed to minimize the significant
variability of this data. A maximum frequency over the
higher mountain regions is indicated. The complex pat-
tern suggests a general tendency for the number of
snowfall days to decrease eastwards across the
prairies.

The high correlation of snowfall amount with site
elevation may mask other factors influencing snow
distribution. The previously noted snow-elevation
regression model approach was used to "subtract" the
elevation influence from the annual station values.
Elevation-predicted annual snowfall amounts were
determined for each station and the ratio of the actual
to predicted values calculated. The contour plotting of
these actual/predicted ratios is illustrated in Figure 13
(two smoothings). The plotted ratio can be used to sug-
gest whether other factors may be responsible for
snowfall variation. Values in excess of one indicate
larger than expected snowfall values. Conversely,
values less than one suggest less than expected
snowfall values.

A general trend of underestimation towards the

southwest and overestimation in the northeast is sug-
gested. The excessive values for the southern part of
the region may reflect the area's closer proximity to
maritime moisture sources. The lower-than-expected
values for the north may indicate dryelr continental
climate patterns.

4.0 CONCLUSION

This brief preliminary analysis has outlined some of
the general characteristics of the snow climate of the
southern Canadian Rockies. The seasonal strong
season character of snowfall with a mid-winter peak has
been indicated. The maximum snowpack depth in late
spring, and subsequent rapid melt, has also been
delineated. A strong correlation with site elevation pro-
vided equations for predicting snowfall and average
snowpack water equivalent. The spatial distribution of
snowfall has been shown to be quite varia.ble with max-
imum values being indicated along the higher
elevations.

These results give a preliminary oVElrview of the
snowfall climate of the snowpack stud~' region. The
temporal trend of snowfall and snowpack water
equivalent suggests that a substantial contribution is
made during the late winter and spring. The spring
"seeding window" (Renick et aI, 1979) may then oc-
cur during this period of substantial natural precipita-
tion. This suggests that a potential for snowpack
augmentation within the study area may occur during
the spring months.

The study has also indicated the difficulty of assess-
ing the snow climate due to the sparse network and
limited snow survey record length. The importance of
site elevation is also indicated. Based on these
preliminary results, a measurement network for the
region cannot be confidently specified.

A more detailed analysis of the precipitation data
would provide a more confident climate assessment.
Examination of daily records from the climate network
would assist in this by identifying individual precipite-
tion events. A time series analysis of the data may
delineate any trends in the snow data. Finally, a cloud
climatology of the region would provide relevant infor-
mation on. the snow-producing clouds of the region.

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