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In these examples, again for simplicity reasons, the climatological and forecast values will only be in one of 2 categories, either 0-value or non 0-value. This way, the main impact of the 0/non-0 value issue can be demonstrated. In the tables below, the numbers and the related average ranks are given for the two groups of 0 and non-0 ensemble members, with the rank-mean, rank-std and expected anomaly and uncertainty categories determined from those cases.

There are 4 tables, with 10% / 30% / 70%  and 100% of 0 values in the climatology (i.e increasingly dry climate). For example, in the 7th row of the 1st table with 10% of 0 in the climatology, 11 ensemble members are 0-value and the remaining 40 are greater than 0. The average rank for the 0 members are 5.5 (as this is given by the method of handling the 0-value issue with equal representation, explained above), while the average rank for the non-zero members is given as 11. The related rank-mean is then 9.81, making this forecast into the 'Extreme low' expected category while the rank-std is 2.26, with low uncertainty category.

These tables demonstrate the complex interaction between the dryness of the climatology and ensemble forecasts, reflected in the forecast rank-mean and rank-std values and the subsequent expected anomaly and uncertainty categories. They also demonstrate, how less likely it becomes to have negative anomalies as the climatology becomes drier and drier.