Environmental Decision-Making - New Ecoinformatics Tools in Environmental Science: Applications and Decision-making - page 247

which can be used in the framework of other procedures. Table 3.3 shows the
advantage of such an approach. Figure 3.15 gives the results of reconstructions of the
empirical distribution for precipitation in three regions of the Russian Federation and
Kustanay in northern Kazakhstan. The parameters for these distributions are given in
Table 3.4 .

Table 3.3 Results of the data processing concerning the temperature anomalies for the arctic
atmosphere (during the last 10 years)
Season M 1 M 2 M 5 M 6 NW ʔ , % Distribution

Winter 0.02 0.29 0.16 0.41 44 56 K

Spring 0.08 0.87 − 0.52 0.05 108 0 St

Summer 0.00 0.39 − 0.01 − 0.65 51 49 Si

Autumn 0.09 0.64 0.11 4.74 73 27 G

0.75 59 41 G
Notation: M 1 is the first order moment (mean), M 2 is the central second order moment (dispersion),
M 5 is the asymmetry coefficient, and M 6 is the excess coefficient, NW is the number of the
measurements in the framework of the sequential procedure, ʔ is the advantage of the sequential
procedure in comparison with the fixed level of 100 measurements in the classical procedure, G is
the Gaussian distribution, K is the Cauchy distribution, Si is the Simpson distribution and St is the
Student distribution

Year 0.05 0.55 0.26 −

Fig. 3.15 Empirical distributions of the precipitation in four regions of Russia and Kazakhstan
estimated by means of the sequential procedure. The distribution parameters are given in Table 3.4

Table 3.4 Estimations of the distribution parameters for precipitation (mm month - 1 ) in three
regions of the Russian Federation and in Kustanay, Kazakhstan
Region Variation coefficient Distribution Mean Dispersion

Uliyanovsk 16 Pearson 310 51

Middle-ural mountains 15 Pearson 435 67

Guriev 31 Pearson 109 33

Kustanay 31 Pearson 211 61