Figure 1. List of the anomaly and uncertainty categories defined with the colours used on the map products.
The CEMS-flood sub-seasonal and seasonal products are essentially the same across the two systems and similarly the same across EFAS and GloFAS. Below, the available products and their main features are introduced. The example snapshots below are from EFAS seasonal forecasts, but the styling is the same for the sub-seasonal and for GLoFAS as well, other than the different forecast lead time periods and the domain.
The forecast signal is shown by colouring on the map product layers, either for individual river pixels or larger basins (see the further product details below in the subsequent sections). Each of these river pixels or basins are coloured by the expected anomaly category and the uncertainty sub-category defined for the actual forecast. There are altogether 7 anomaly categories (from 'Extreme low' to 'Extreme high') and 3 uncertainty sub-categories (from 'Low to 'High'), based on the extremity level of the 51 ensemble forecast members in the 100-value climatological distribution and the mean and standard deviation of these 51 rank values. The details of the computation methodology is described in: Placeholder CEMS-flood sub-seasonal and seasonal forecast anomaly and uncertainty computation methodology.
In total, there are 15 forecast signal categories coloured on the maps. Out of the possible combinations of the 7 anomalies and 3 uncertainties (7*3), 5*3=15 category combinations are represented by colours, after the middle three anomaly categories ('Bit below', 'Normal' and 'Bit high') are merged into one 'Near normal' category. The choice of 5 anomaly categories for the colouring allows the users to focus on the larger anomalies, supplemented by the 3-level uncertainty representation. The 7 base and 5 simplified anomaly categories, the 3 uncertainty categories and the related 15 colours are shown in Figure 1.
Each of the 5 anomaly categories indicated on the maps have a distinct colour ranging from red ('Extreme low') to blue ('Extreme high'), with the 'Near normal' category indicated by the neutral grey colour, while the 3 uncertainty sub-categories are indicated by different intensities of the same colour, going from darker to lighter versions as the uncertainty increases.
Figure 1. List of the anomaly and uncertainty categories defined with the colours used on the map products.
The river network summary map layer shows the combined expected forecast anomaly and uncertainty signal in a simplified way for each forecast lead time (Figure 1). The lead time is weekly (always Monday to Sunday with the weekly average river discharge) in the sub-seasonal and monthly in the seasonal (always calendar month with the monthly average river discharge).
The users can navigate between the different lead times by clicking on the chosen weeks (in sub-seasonal) and month (in seasonal) in the lead time controller (see Figure 1a bottom left corner). This way the users can check the individual signal for each lead time, which currently is 5 or 6 weeks for the sub-seasonal (depending on which day of the week the forecast run) and always 7 months for the seasonal.
The forecast signal is shown by colouring of all river pixels above a certain minimum catchment area (currently 50 km2 in EFAS and 250 km2 in GloFAS). The 15 pre-defined anomaly and uncertainty category combinations are used with the 15 colours, as described in the previous section (see e.g. Figure 1).
Figure 2 shows an example, which highlights some river sections with the explanation of the assigned colours and the corresponding anomaly and uncertainty levels. Both Figure 2a and 2b include the colour legend with the 15 categories and the corresponding colours (same as in Figure 1), for easier interpretation.
The river network summary map also contains the reporting points, which are labelled in Figure 2b. These are river locations, where detailed information is provided about the evolution of the forecast signal over the forecast horizon. These reporting points are either fixed points, which are also used in the medium-range flood products and the basin-representative points, which are selected locations on a one point per basin basis. Further details about the basins and the representative points are available here: Placeholder CEMS-flood sub-seasonal and seasonal basins and representative stations.
a)
| b)
|
Figure 2. Example snapshots of the river network maps with the reporting points, lead time navigation indicated and categories and colours explained.
At the predefined reporting point locations (either fixed or basin-representative) further detailed information is provided about the evolution of the forecast signal.
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.
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.
a)
| b)
| c)
|
Figure 4. Example snapshot of the reporting point pop-up window product's hydrograph, with different interpretation schemes (a-b-c).
The right half of the plot covers the forecast horizon, in the displayed example in Figure 4 this means 7 lead times of 7 calendar months from August to February (next year) (see Figure 4a). 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 coloured background is the model climatology (see Figure 4b). This climatology is generated using reforecasts over a 20-year period. Further information on the climatologies and their generation is given on: Placeholder CEMS-flood sub-seasonal and seasonal forecast signal generation methodology. In the past half of the hydrograph, the climatology is always from lead time 1, so first week (always as days 1-7) or first month (whichever month of the year it is), as that is the closest equivalent to the proxi-observation-based climatology. While in the forecast half, for each forecast period the equivalent climatology is plotted with that specific lead time. From the climatology, the 5 anomaly categories are coloured, below the 10th the 'Extreme low' with red, above the 90th percentile the 'Extreme high' with blue, the 10th to 25th percentiles zone as 'Low' with orange, the 75th to 90th percentiles as 'High' with cyan and finally the remaining 25th to 75th percentile as 'Near normal' with grey. This 'Near normal' category is the extended one by merging the original 25-40, 40-60 and 60-75 percentile categories, including the narrower 'Near normal', the 'Bit low' and 'Bit high' categories.
As Figure 4c highlights, the seasonal hydrograph indicates a property of the model climatologies. The seasonal hydrograph is designed to have exactly 13 (12+1) monthly periods, which guarantees that the last month of the forecast (February in Figure 4) will feature both as a month-7 forecast climatology and as a month-1 forecast climatology in the past period, as the oldest month plotted. This way, the comparison between the left-most and right-most background colouring of the hydrograph gives a visual impression of the drift in the river discharge forecasts. Drift in this context means, the month-7 climatology could occasionally be very different to the month-1 climatology. There can be a noticeable shift or drift in the forecast behaviour, represented by the change in the reforecast behaviour from shorter to longer ranges (see Figure 4c for visual indication of this).
The example in Figure 4 highlights that the 'Extreme low' and 'Low' categories shift only very little from month-1 to month7, with the median being very stable. However, the higher percentiles (75th and 90th) are noticeably higher in the month-7 climatology, indicating a noticeable drift for larger values in the reforecasts for longer ranges. While for month-1, the 90th percentile is about 20 m3/s, so about 10% of the time the monthly mean river discharge can exceed this value, in the longer month-7 range the same 90th percentile increases to 25 m3/s. So, the seasonal forecasts are more likely to show larger values in the longer ranges than in the shorter range.
Unfortunately, this feature of contrasting the climatological behaviour at shorter and longer lead times, does not work for the sub-seasonal, as there is no 52+1 weeks available in the hydrograph (it would not be physically possible), which would be necessary to see the same week appearing as a week-1 climatology of the past period and as a week-6 climatology of the forecast.
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.
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.
The used colours in the table are the same as in the river network and basin summary maps (see Figure 1), representing the 15 categories with 15 colours (5 anomaly categories combined with the 3 uncertainty sub-categories), explained above in the 'Product colouring' paragraph. The coloured cell will have the exact same colour that the corresponding pixel (that represents the reporting point location in the river network) has on the 'River network' map layer. In addition, the coloured cell's probability value is displayed bold to be more noticeable.
The coloured cell's probability is often the highest of the 7 categories as well, but not always, like in many forecasts in Figure 4. For example, the forecast for August shows a gradual progression from 'Bit low' anomaly with high uncertainty (lightest grey colour; grey group as the colour of the extended 'Near normal' category) to 'Extreme low' category with low uncertainty (darkest red colour). In addition, the probability value of the coloured cells is generally increasing as we go towards the shorter lead times. 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 (light orange in the June forecast and light grey in earlier forecasts), which means the uncertainty was 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' category (dark red). However, at the same time, 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 for the earlier forecast the categories shifting towards normal conditions is directly related to the increasing uncertainty with most or all of the categories having some ensemble members.
The basin summary map is the equivalent of the river network map averaged onto the larger basin scale. The basins are predefined, as described in Placeholder CEMS-flood sub-seasonal and seasonal basins and representative stations, with 204 basins in the EFAS domain and 942 basins globally in GloFAS.
Figure 6 shows an example snapshot in the same area as Figure 1a above. One can compare, how the variable signal on the river network averages into the basin signal. The averaging is done from the river pixel information. A Rank-mean and a Rank-std value will be calculated for each basin and with those the exact same method is used (as for the river pixels on the river network summary map) to define the forecast anomaly category and the uncertainty category and thus the colour of the basin (see the inset figure with the colour legend in Figure 4). The colours used are the same as for the river network summary map, except there is a slight shift in transparency, in order to allow visibility for all river pixels, even for those with the same colour as the basin colour.
The basin Rank-mean and Rank-std values are determined using all the large enough river pixels in the basin. Currently, only pixels above 50km2 are used in EFAS and pixels above 250 km2 are used in GloFAS in the averaging. The basin Rank-mean/Rank-std values are calculated as an arithmetic average of the Rank-mean and Rank-std values of the individual river pixels, weighted by the square of the upstream area, as described below:
Figure 6. Example snapshot of the basin summary maps with the lead time navigation indicated and the categories and basin colours explained.
The forecasts can be advanced with the lead time control panel, both on the river network and the basin summary map layers (see Figure 2a and Figure 6 bottom left corner). The users can check the individual forecast signal for each lead time, which currently is 5 or 6 weeks for the sub-seasonal (depending on which day of the week the forecast run) and always 7 months for the seasonal.
