Spatial and temporal averaging of observational data is widely used
in many problems of meteorology, hydrology and related sciences.
Several averaging methods have been proposed by various scientists
and are used on a regular basis to obtain data averaged in space
orfand in time, to yield data which are less influenced by random
factors than initial point data are. The significance of averaging
meteorological fields has substantially increased nowadays in
connection with the problem of global climate change. However
important as such change may be, it is very small as compared with
the natural variability of atmospheric fields. An accurate
averaging is absolutely necessary in order to discover a small
climate change signal on the background of strong natural
variability that acts as a random noise in this context. It is
highly desirable not only to determine the averaged values
themselves, but also to estimate the accuracy with which these
values are known. To evaluate the accuracy of averaging is not a
simple task mainly because the initial point values are not
independent from each other, and interconnections between them
substantially influence the averaging accuracy.
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