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Figure II.2.8 The problem of inter- vs extrapolation using a spatially biased dataset.
Such frequency curves easily reveal the bias usually found in mountainous areas. In flat areas,
such as Denmark this problem seldom occurs. The variability of the terrain is low, and the
station density is high. The highest elevated station occurs at 166 m a.s.l. a few meters higher
than the highest grid cell. This is quite common also in Alpine countries, where observatories
are located on mountain peaks.
Another way to decide whether the sample is representative is to compare the actual
distribution with a spatially stationary random process following a multidimensional Poisson
law.
II.2.7.2 Data representativity and atmospheric processes.
So far we have discussed spatial representativity, second order spatial stationarity, the spatial
intrinsic hypothesis etc. Many spatialisation applications in climatology and meteorology are
however based on a pre-defined linear model. Applying such models with temporal data
creates other concerns. One example is if a model established using data from a period with a
certain climate would be valid for all events in the future, when the climate could be in a
different phase? Different atmospheric circulation patterns will for example have a significant
effect on the spatial structure functions in several regions in Europe. One specific problem is
the frequency of temperature inversions, which are strongly related to certain circulation
types. Also the occurrence of extreme climatic episodes will be strongly affected by certain
atmospheric conditions.
Systematic changes in the observation networks should also be a concern. If the “universe” is
different for the calibration and estimation period, the defined model will be different. This
problem can also be associated with spatial homogeneity, which is a relatively new issue that
appeared when spatialisation methods began to be used to derive gridded climatological
datasets.