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.
Product colouring
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.
...
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.
...
Figure 2. Example snapshots of the sub-seasonal and seasonal river network summary forecast maps with the reporting points, lead time navigation 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.
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 The first table in the popup window ( 'Point information') , which provides metadata information of the station (Figure 23). These are The 'Station ID' is the station ID and also an internal ID (Point ID)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 , and also coordinates (are also available. While the point's location is described by two sets of lat/lon ) coordinates and upstream area in two flavours, the provided ones and the LISFLOOD river network equivalent. The provided coordinates and upstream area are from the users as those represent the real river gauge locations. These are available only for the fixed points (sometimes provided upstream area is missing). 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. The fixed reporting points have a Point ID in the metadata table starting with 'SI', while , 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 starting ' 'Point ID' will start with 'SR'.
Hydrograph section
, while the fixed reporting points' will start with 'Sl' in the metadata table, respectively.
Hydrograph
Next in the Next item in the popup window is the hydrograph, which graphically summarises the climatological, antecedent and forecast conditions (see Figure 2 and Figure 3).
Figure 2. Example snapshot of the reporting point pop-up window product (for a seasonal forecast).
4). The left half of the plot, left of the horizontal dotted line , which indicates the forecast start date, shows the past (see Figure 3aalways 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, The right half of the plot covers the forecast horizon, in the displayed example in Figure 2 and 3 4 this means 7 lead times of 7 calendar months from August to February (next year) (see Figure 2a4a). 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 (which are the 25th and 75th percentiles as well) and the median (which is the 50th percentile).
The coloured background is the model climatology (see Figure 3b4b). This climatology is generated using reforecasts over a 20-year period. Further information on the climatologies and their generation is given hereon: 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 While in the forecast half, the climatologies are lead time dependent and for each forecast lead time 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 (it was mentioned also with the river network summary map above), 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 3c 4c highlights it, the seasonal hydrograph (it would not work for the sub-seasonal due to the much shorter weekly lead times) 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 2 and 34) will feature both as a month-7 forecast climatology and also as a month-1 forecast climatology in the past period, as the oldest period month plotted. This way, the hydrograph gives 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 reforecast-based climatology percentiles could occasionally be very different to the month-1 reforecast-based percentiles and by this show climatology. There can be a noticeable shift or drift in the forecast behaviour. This means, values going lower or higher , represented by the change in the reforecast behaviour from shorter to longer ranges (see Figure 3c 4c for visual indication of this). In fact, for this comparison, the left-most and right-most parts of the hydrograph need contrasting. In the example in Figure 2-3, the shaded climatological categories highlight that
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 in the for month-1 average river discharge climatological distribution, 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 range month-7 reforecasts range the same 90th percentile , the 10% of the time to exceed this value, increases to 25 m3/s. So, in sort, the forecast is the seasonal forecasts are more likely to show larger values in the longer ranges than in the short shorter range.
...
a)
...
b)
...
c)
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.
Probability evolution table
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
Figure 3. Different interpretation schemes (a-b-c) for the sub-seasonal and seasonal hydrographs.
Probability evolution table section
Finally, the last part of the reporting point popup window is the probability evolution table. This table shows all the 7 anomaly categories (extreme dry to extreme wet as left to right) and the related probabilities for all the forecast lead time periods and from all the previous forecast runs that verify during the most recent 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 run date is, 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 2 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 real time forecast, and it again depends on which day of the week the forecast run date is. The bottom right corner of the probability evolution table is emptyblank, as those lead times are not available from the earlier forecast runs.The cells in the table are not coloured, with one exception, which is the forecast anomaly category (of the whole ensemble). That cell's number is bold and the cell is coloured by the same colour that the river pixel has on the river network summary map
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), included also the related uncertainty category (how uncertain the forecast is; either low, medium or high) are 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 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 . As described in Placeholder CEMS-flood sub-seasonal and seasonal forecast anomaly and uncertainty computation methodology, the dominant category is determined by methodology.
The used colours in the table are the same as in the river network and basin summary maps, 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 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 (grey colour category, 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). Moreover, the number of the coloured cells is also on the increase generally, as we go towards the shorter lead times. Until the June forecast, the colours are the lightest of the three versions, highlighting high uncertainty (light orange in the June forecast), while in the July forecast the uncertainty drops to medium level (medium dark orange) and finally in the August forecast we arrive to the low uncertainty 'Extreme low' situation. However, at the same time, for 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 to 100% in the August forecast (all of the 51 ensemble members being in the 'Extreme low' category). The reason why the earlier forecasts shift towards the normal conditions in the mean sense, is the larger uncertainty with most or all of the categories having some ensemble members.
Out of the 7*3 possible category combinations, 5*3 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 expected anomaly category and the uncertainty subcategory are represented by cell colouring in the table below, having one coloured cell for each lead time and run date. Out of the 7*3 possible category combinations, only 5*3 are represented by colours, after the middle three anomaly categories ('Bit below', 'Normal' and 'Bit high') are merged into one 'Near normal' category. Having only 5 anomaly categories for the colouring was a compromise, which allows the users to focus on the larger anomalies and at the same time retaining the 3-level uncertainty representation with reasonably distinguishable colours the rank-mean (the mean of the ensemble members' ranks). The coloured cell's number is not always the highest, like in many forecasts in the example in Figure 2' probability table. For example, the forecast for August shows a gradual progression from near normal (grey colour, the original 'Bit low' of the 7 categories) and high uncertainty (lightest grey colour of the three), to 'Extreme low' category with low uncertainty (darkest red colour). Moreover, the number of the coloured cells is also on the increase generally, as we go towards the shorter lead times. Until the June forecast, the colours are the lightest of the three versions, highlighting high uncertainty (light orange in the June forecast), while in the July forecast the uncertainty drops to medium level (medium dark orange) and finally in the August forecast we arrive to the low uncertainty 'Extreme low' situation. However, at the same time, for 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 to 100% in the August forecast (all of the 51 ensemble members being in the 'Extreme low' category). The reason why the earlier forecasts shift towards the normal conditions in the mean sense, is the larger uncertainty with most or all of the categories having some ensemble members.
Basin summary map
The basin summary map is the equivalent of the river network summary 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 944 basins globally in GloFAS.
...