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At the predefined reporting point locations (either fixed or basin-representative) further detailed information is provided about the evolution of the forecast signal.
Point information table
Figure 3. Example snapshot of the reporting point pop-up window product's Point information table.
The first table in the popup window is the 'Point information', which provides metadata information of the station (Figure 3). The 'Station ID' is the station identification number, while the 'Point ID' is an internal product generation related process number. In addition, the station name (if available), country, basin and river names are also available. While the point's location is described by two sets of lat/lon coordinates and upstream area. The first 3 values describe the real location, that is either a river gauge (if it is an observation point) or a location requested by users. The second 3 values are the LISFLOOD river network equivalent, the coordinates and upstream area that the hydrological simulations have. For the basin-representative points, however, only the LISFLOOD coordinates and upstream area are available, as these points were defined solely on the simulated LISFLOOD river network, therefore the first 3 numbers will be identical to the second 3 numbers. Apart from the different appearance with either black dot or rectangles, the basin-representative points' 'Point ID' will start with 'SR', while the fixed reporting points' will start with 'Sl' in the metadata table, respectively.
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Next in the popup window is the hydrograph, which graphically summarises the climatological, antecedent and forecast conditions (see Figure 4). The left half of the plot, left of the horizontal dotted line which indicates the forecast start date, shows the past always with 6 lead time periods included (either weeks in sub-seasonal or months in seasonal as in Figure 4a). The black dots (connected by black line) indicate 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. These black dots show the simulated reality of the river discharge conditions, as close as the simulations can go at the actual conditions over the forecast periods (average river discharge over months in seasonal and calendar weeks in sub-seasonal). These 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. The users are encouraged to go back and check previous forecasts to see how well the earlier forecasts predicted the anomalies.
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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.
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The coloured cell's probability is often the highest of the 7 as well, but not always the highest, like in many forecasts in the example in Figure 4's probability table. For example, the forecast for August shows a gradual progression from 'Bit low' anomaly with high uncertainty (lightest grey colour category, ; grey group as the colour of the extended 'Near normal' category; and also light grey, the lightest of the three grey colours as the uncertainty is in the high category) to 'Extreme low' category with low uncertainty (darkest red colour). MoreoverIn addition, the number probability value of the coloured cells is also on the increase generally , increasing as we go towards the shorter lead times. Until . But again, this is not always the case, as sometimes the mean of the ensemble member ranks, that define the expected anomaly category, will not coincide with the most probable category.
For example, until the June forecast, the colours are the lightest of the three versions , highlighting high uncertainty (light orange in the June forecast and light grey in earlier forecasts), while which means the uncertainty is high. While in the July forecast the uncertainty drops to medium level in the same 'Low' category (medium dark orange) and finally in the August forecast we arrive to the low uncertainty in the 'Extreme low' situationcategory (dark red). However, at the same time, for in all of these forecasts the more likely of the 7 categories are constantly the 'Extreme low' category, with the probability values gradually increasing from 2X in February to 100% in the August forecast (all of the 51 ensemble members being in the 'Extreme low' category). The reason why for the earlier forecasts shift towards the forecast the categories shifting towards normal conditions in the mean sense, is the larger is directly related to the increasing uncertainty with most or all of the categories having some ensemble members.
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