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The left half of the plot, left of the horizontal dotted line indicating the forecast start, is the antecedent condition section. This part includes black dots (connected by black line), which show the average weekly (sub-seasonal) or monthly (seasonal) discharge from the so-called water balance, the proxi observations, which are produced as a LISFLOOD simulation forced with either gridded meteorological observations in EFAS, or ERA5 meteorological reanalysis fields in GloFAS. This represents the simulated reality, as close as it can go to reality in EFAS and GloFAS. It always includes 6 lead time periods of the past on the hydrographs, either the 6 calendar weeks before the first week of the forecast in the sub-seasonal, or 6 calendar months before the first month of the forecast in the seasonal (as in Figure 4a for the seasonal and 4d for the subseasonal). The The black dots are added to the hydrographs retrospectively, after each week (in sub-seasonal) or month (in seasonal) passes and the weekly or monthly mean proxi-observed river discharge values become available. For the seasonal forecast hydrograph, the water balance is known for all 6 past months at the time of the forecast hydrographs are produced, while for the sub-seasonal the last week of the antecedent section will not have a water balance black dot, as that is not available yet at the time of the production. The users are encouraged to go back and check previous forecasts to see how well the earlier forecasts predicted the anomalies.
The right half of the plot covers the forecast part, in the displayed example in Figure 4 4a-c this means 7 lead times of 7 calendar months from August to February (next year) (see Figure 4a), while for the sub-seasonal example in 4d it will be either 5 weeks or 6 weeks, depending on the forecast run date. The forecast distribution is indicated by box-and-whiskers, displaying the minimum and maximum values in the ensemble forecasts of all the 51 members and the lower and upper quartiles (25th and 75th percentiles) and the median (the 50th percentile).
The colour-shaded background in the hydrograph is the model climatology (see Figure 4b for explanation). The climatology is generated using reforecasts over a 20-year period. From the climatology, the 5 anomaly categories are coloured, the below 10th percentile zone ('Extreme low' with red), the 10th to 25th percentile zone ('Low' with orange), the 25th to 75th percentile zone (extended 'Near normal' with grey), the 75th to 90th percentile zone ('High' with cyan) and the above 90th percentile ('Extreme high' with blue). Further information on the climatologies and their generation is given on: Placeholder CEMS-flood sub-seasonal and seasonal forecast signal generation methodology.
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In the antecedent half the hydrograph, the climatology with the shortest possible lead time will always be shown. For the seasonal, this will be month-1 for each of the 6 months. For the sub-seasonal, on the other hand, it will be the days1-7 climate lead time. If one of the available climate dates coincides with the Monday of the water balance week, then that will be directly shown. While, if the Monday falls in between two climate dates, then we show the weighted average (by the distance in days from Monday) of those two climatologies. In the above example of the sub-seasonal run on the 14th of December 2024, which was a Saturday run, the 6 water balance weekly periods will be 4-10 Nov, 11-17 Nov, 18-24 Nov, 25 Nov - 1 Dec, 2-8 Dec and 9-15 Dec, all Monday-Sunday calendar weeks. The last of these 9-15 Dec, the one before the week-1 forecast, will not be known by the time the forecast signal is generated and will only be added later retrospectively. For this 9-15 Dec week, the Monday of 15 Dec falls in between two climate dates of 13 and 17 Dec, so the plotted cliamate will be the average of the two with 50%-50% weights (as both 13 and 17 Dec are 2 days away from 15 Dec Monday).
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Figure 4. Example snapshot of the reporting point pop-up window product's hydrograph, with different interpretation schemes (a-b-c).
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The last part of the reporting point popup window is the probability evolution table. It shows all the 7 anomaly categories (from 'Extreme low' to 'Extreme high' as left to right) and the related probabilities for all the forecast lead time periods and from all the most recent forecast runs that verify during the last forecast horizon. For the sub-seasonal, this means 5 or 6 calendar weekly forecast lead time periods, depending on which day of the week the forecast run, and thus how many calendar weeks the 45-day lead time in the forecast can cover; and 7 calendar monthly periods for the seasonal. For the seasonal forecasts, there is always 7 rows with the most recent 7 seasonal forecast probabilities (as Figure 5 shows). While for the sub-seasonal, with including all the daily (00 UTC) forecast runs verifying in the forecast horizon, there can be 41 to 46 rows. Always as many, as many forecasts can verify in the forecast horizon of the actual forecast, and it again depends on which day of the week the forecast run. The bottom right corner of the probability evolution table is blank, as those lead times are not available from the earlier forecast runs.
Figure 5. Example snapshot of the reporting point pop-up window product's Probability evolution table from the seasonal and the sub-seasonal forecasts.
The ‘expected to happen’ anomaly category (one of the seven categories) is indicated by cell colouring in the table below, having one coloured cell for each lead time and run date (all other cells are left blank). The colour is dependent on the anomaly, but also on the related forecast uncertainty category (how uncertain the forecast is; either low, medium or high). The expected category and the related uncertainty are defined by the ensemble member ranks in the 100-value climatology, based on the rank mean and rank standard deviation (of all the 51 ensemble members' ranks), respectively, as described in Placeholder CEMS-flood sub-seasonal and seasonal forecast anomaly and uncertainty computation methodology.
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