^

summary, eq 4 yields good results for small values of α and β but leads to signifi-

^

cant errors if either one of them is 0.5 or more.

We tried this algorithm for predicting total cloud amount using the standard

meteorological data obtained on drifting station North Pole 25, which was above

85N from October 1982 to October 1983. Table 5 lists our results for the winter of

19821983, November through March. In the table, all the correlation coefficients

have a significance level below 0.13. In other words, based on the calculations sum-

winter is better than 87%.

Figures 8 and 9 show other tests of our algorithm for estimating total cloud

amount using data from NP-25. Figure 8 shows histograms of the observed and

modeled total cloud amounts based on data collected on NP-25 in November 1982.

Figure 9 compiles 240 consecutive observations of total cloud amount and our

simultaneous estimates of total cloud amount based on eq 4. The figures show that,

^

using the α and β coefficients averaged from all data between 1955 and 1991 and

^

having observations of surface-

layer temperature on NP-25, we

have managed to capture with eq

4 not only the U-shaped fre-

quency distribution in total cloud

amount but also, to an extent, its

0.54

0.31

0.57

0.42

0.57

temporal variability.

50

Observed

40

30

20

10

0

50

Modeled

40

30

20

10

0

02

34

56

78

910

Cloud Amount (tenths)

12