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NEW ALGORITHMS FOR SNOW COVER, TEMPERATURE AND
WETNESS
New algorithms created by the Norwegian Computing Institute enable
more accurate prediction of snow characteristics from satellite data.
A large part of Europe depends on snow melt as a source of drinking
water. In addition to its role in the hydrological cycle, snow is also an impor-
tant component of the Earth’s climate system. As such, significant social val-
ue can be lined by improving our knowledge of snow.
The Envisnow project set out to develop the necessary infrastructure to
improve monitoring f snow parameters using earth observation data from sat-
ellites. Specifically, the Norwegian Computing Institute defined new algo-
rithms to produce estimates of the fractional snow cover area (FSCA), the
surface tempera-ire of snow (STS) and snow wetness.
FSCA calculations are made difficult by the fact that snow's spectral re-
flectance can vary according to several different factors. These include the
age of the snowpack, its impurity content, the sun's elevation and the viewing
angle of the satellite instrumentation, for example. The institute's solution
was to employ both a metamorphosis model and an impurity model to pro-
duce a valid snow spectrum and a local bare ground spectrum. In the final
step, a linear spectral mixing algorithm is used to estimate the FSCA.
With respect to the STS, atmospheric attenuation alters the snow's origi-
nal blackbody radiation signature. To account for the effects of atmospheric
composition and path length, the institute tested a number of different algo-
rithms. The team identified a pre-existing algorithm as the optimal solution,
particularly for polar regions. The institute adapted this algorithm to the En-
visnow integrated snow information system and verified its performance with
real earth observation and surface data.
Information about snow wetness provides valuable insight into the
snowmelt process. The institute was able to enhance snow wetness prediction
capabilities by combining snow grain size (SGS) measurements with STS
measurements. A snow wetness class is determined based on the STS and the
temporal evolution of SGS. As with the other new algorithms, the results
were validated at a number of different locations.
The Norwegian Computing Institute's contribution to Envisnow repre-
sents a quantum leap forward as it is now possible to accurately estimate es-
sential snow parameters throughout the entire snow season. The institute is
consequently looking to license the new algorithms.
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