...
The ensemble forecasts have 51 members, which will be assigned an extremity rank each. Using these 51 ranks, the forecasts will be put in one of the 7 anomaly categories (as described in Table 1). This is done based on the arithmetic mean of the 51 ensemble member rank values, which all can be from 1 to 100 (rank-mean) (see Figure 4):
. This rank-mean will also be a number between 1 and 100, but this time a real (not integer) number. If the anomaly is 50.5, that is exactly the normal (median) condition, i.e. no anomaly whatsoever. If the anomaly is below 50.5, then drier than normal conditions are forecast, if above 50.5, then wetter than normal. The lower/higher the anomaly value is below/above 50.5, the drier/wetter the conditions are predicted to be. The lowest/highest possible value is 1/100, if all ensemble members are 1/100 (the most extremely dry/wet). Then, based on this rank-mean, we define the expected forecast anomaly category (one of the 7 categories in Table 1) for the whole ensemble forecast, by placing the rank-mean into the right categories, as defined in Table 1 above. For example, all rank-mean values from 40.0 to 60.0, interpreted as 40.0<= <60.0, will be assigned to 'Normal', or category-4.
...
In addition to the expected forecast anomaly computation for the whole ensemble, as one of 7 predefined categories, the forecast uncertainty is also represented in some of the sub-seasonal and seasonal products, namely on the new 'Seasonal outlook - River network' and 'Seasonal outlook - Basin summary' products. The forecast uncertainty is defined by the standard deviation (std) of the ensemble member ranks, which all can be from 1 to 100 (rank-std). :
If the ensemble member ranks cluster well, and the spread of the ranks is low, then the forecast uncertainty will be low and conversely the confidence will be high. The standard deviation of One specific example is the even distribution of ranks, with 51 values ranging spread from 1 to 100 evenly, as ranks of 1, 3, 5,..., 47, 49, 50 (or 51), 52, 54,..., 96, 98 and 100. This distribution has a mean of very close to 50.5 and a standard deviation of very close to 29.0. Then another example can be the most uneven distribution of rank values of 1, 1, 1, ..., 1, 100, 100, 100,..., 100, with either 25 values of 1 and 26 values of 100 or vice versa. In this case the rank-mean is either 49.5 or 51.5 (depending on either 1 or 100 has 26 and not 25 values) and the standard deviation is in both case the same 49.5. Another specific example is when all values are the same, so there is no variability amongst the 51 ranks at all, in which case the rank-mean is the same value and the rank-std is 0.
Based on these specific examples and the rank-std ranging from 0 to 49.5, with the climatology-matching distribution's ranks-std of 29, three uncertainty categories were defined, which is the perfect match to the climatologically expected spread, is going to be (100-1)/sqrt(12) = 28.86. On the other hand, the most extreme rank-std value is when half of the members are with rank 1 and the other half with rank 99 (perfectly split ensemble), in which case the rank-std is 49.5, while the lowest rank-std value is 0, when all ranks are the exact same, so there is no variability in the ranks at all.For forecast uncertainty, three uncertainty categories are defined based on the rank-std value of the ensemble forecasts, using the easy to remember generic category split values of 10 and 20. (Table 2). These work well enough for all lead times and geographical areas and will give an indication of generally how uncertain the expected forecast anomaly is for that pixel or area and that lead time.
| Uncertainty categories | Name | Rank STD |
|---|---|---|
| Cat-1 | Low uncertainty | 0-10 |
| Cat-2 | Medium uncertainty | 10-20 |
| Cat-3 | High uncertainty | 20< |
...