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Figure II.2.6 The left panel shows the (area of) Thiessen polygons of the meteorological
network used for the NORDGRID project. The right panel shows the standard deviation of the
terrain model (red colour high standard deviation, yellow small) indicating the variability of
the terrain. Ideally areas with high variability should have higher station density.
That means that many of the assumptions underlying the spatialisation methods described in
the previous chapters are not fully fulfilled.
􀁸 Station networks are usually biased towards lower altitudes and populated areas
􀁸 Station density varies 􀃆 uncertainty is a function of station density.
􀁸 In a sparse network changes in the network will have consequences for the
homogeneity of gridded time series.
Therefore will the uncertainty no be a fixed property in spatial analysis, but vary in space and
time.
In meteorology and climatology terrain characteristics is one of the main explanatory
variables, and the input data should represent the same “universe” as the one that shall be
estimated. In an ideal world that means that the frequency distribution of the input data and
the explanatory variables should coincide. Whether this is true can be investigated by
comparing the distribution functions directly, e.g. the altitude distribution of the terrain model
with the distribution of station elevation (Figure II.2.7). In mountainous areas stations are
usually observation biased towards the lower elevations.
The consequence of such biased input data is a high risk of performing extrapolation instead
of interpolation. Especially when using a model parameterized on a biased network this
problem could occur. This effect might even be increased by use of external predictors.
Defining the trend by e.g. linear regression analysis will easily lead to estimates in the
extrapolation domain - outside the valid area of the model’ (Figure II.2.8.).