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II.2.7 Assessment of uncertainties in spatialisation.
In spatialisation, like in all other types of modelling, assessment of errors and uncertainties is
of major importance. There are several types of errors and uncertainties to consider, and also
several ways to address these.
First of all it is necessary to distinguish between what is uncertainty, and what is error. In this
section this will be discussed, and different procedures to address uncertainties and errors be
presented and discussed.
II.2.7.1 Data representativity, quality and reliability
Traditionally we differentiate between several types of errors. Meteorology and climatology
depend on observations done by some sort of measurements, direct or indirect. These
observations contain systematic and random measurement errors. Such errors will not be
deeply discussed in this report, as they are not a part of the spatialisation itself. But they
should be considered by the individual researchers in order to:
􀁸 exclude data with insufficient data quality
􀁸 address accuracy of measurements in order to understand the natural variability of the
data entered into the spatialisation algorithms.
Among the systematic errors are e.g. instruments which are not calibrated, and therefore give
wrong readings, observations not carried out according to the guidelines, computer
programmes used in data processing etc. Such errors should ideally be easy to detect in a
state-of-the-art data quality control system.
Random errors are more difficult to detect. Such errors are usually one-occasion type of errors
for which the reason is not clear, e.g. misreading by an observer is the classical example.
These are not that easy to discover if they are within the range of the natural variability of the
observed element or if it is an error.
Representativity of the observation network is probably the most serious problem within
meteorology and climatology. Such networks usually have an irregular spatial distribution
both in 2- and 3D. To complicate this issue further, station networks will change over time
both concerning the location of the individual stations as well as the spatial density of
measurement sites.
There are several ways of addressing the irregularity of station networks. The 2-dimensional
representativity of stations can e.g. be described by calculating Voronoï-diagrams (Thiessen
polygons) around each station. One example is given in Figure II.2.6 where the variation of
the surrounding area is shown. This example is taken from the NORDGRID project (Jansson
et al., 2007) and shows quite clearly the different problems that arise with station networks.
Here are the data networks in four different countries merged, showing e.g. a tremendously
dense network in the smallest country Denmark and a rather sparse network in Finland. In
addition is the variance of the terrain smallest in the areas with the densest station network
(Figure II.2.6).