Since the implementation of EFAS version 5.4 and GloFAS v4.3 in 2025, the same design of products are used for both the sub-seasonal and seasonal range forecasts in EFAS and GloFAS. This means, the previous EFAS/GloFAS seasonal products and the EFAS sub-seasonal product were replaced by the new ones, while for GloFAS the sub-seasonal product was newly introduced. 

The generation of the sub-seasonal and seasonal forecast signal relies on few major steps. The process is illustrated by a flowchart in Figure 1. The forecast anomaly and uncertainty signal is derived by comparing the real time forecast (top left section in Figure 1) to the 99-value model climate percentiles. The climatology is generated using reforecast over a 20-year period, which provides range-dependent climate percentiles that change with the lead time. The climate generation is described in Figure 1 in the bottom right corner. Further details of the real time forecast, reforecast and the generation of the climatologies are available on this page: 

The revised sub-seasonal products cover calendar week periods (i.e. always Monday-Sunday), while the seasonal products are valid for whole calendar month periods. The forecast signal is derived from the relationship between the calendar weekly or monthly averaged river discharge and the climatological distribution of the same weekly- or monthly-averaged values. While this naturally works for the calendar months, the fixed calendar week lead times in the sub-seasonal allow the users to directly compare forecasts from different forecast runs, as the verification period is fixed onto the calendar weeks. This way, the evolution of the subsequent daily sub-seasonal forecast runs (always at 00 UTC) can be monitored by looking at the exact same verification period.

The following step in generating anomaly and uncertainty signals is to determine, how extreme the ensemble members of the forecast are in the context of the climatological behaviour, which is represented by the 99 percentiles and the corresponding 100 equally likely bins of the climate range. This is done by ranking each ensemble member and sorting them into one of the 100 climatological bins. This gives 51 ranks, that range from 1 to 100.

In order to display the information of the 51 ensemble values, which all have either the calendar-weekly- or calendar-monthly-mean river discharge values or the 51 climatological ranks, a simplification was necessary. For this, 7 anomaly categories were defined from 

Figure 1. Flowchart of the sub-seasonal and seasonal anomaly and uncertainty signal generation methodology.

The generation of the sub-seasonal and seasonal forecast signal relies on few major steps. The process is illustrated by a flowchart in Figure 1. 

Real time forecasts

The first major component is the hydrological forecasts produced in real time. This will give the actual predicted conditions for the sub-seasonal and seasonal products that will be compared to the climatologies to derive the forecast anomaly and uncertainty. The characteristics of the real time forecast simulations are described below. Where appropriate, the difference between EFAS/GloFAS and sub-seasonal/seasonal is specified. If there is no EFAS/GloFAS or sub-seasonal/seasonal mentioned, then the method is identical between the systems:

• Hydrological model used: LISFLOOD (LINK!)
• Meteorological forcing:
  • Sub-seasonal:
    • The combination of the 9km (horizontal resolution) ECMWF ensemble forecasts (LINK!) and the 36km ECMWF sub-seasonal forecasts (LINK!) from the previous run date 
    • The low-resolution sub-seasonal forcing is taken from the previous available forecast run, simply because of timing, as the sub-seasonal meteo forecasts for the same day are only available with several hours delay after the high-resolution forecasts. 
    • The high- and low-resolution forcing is currently combined by a simple mechanical blending by using the high-resolution meteorological forcing for days 1-15, while the low-resolution meteorological forcing for days 16-45, for each of the 51 ensemble members. 
    • The blending can create inconsistencies locally over complex orographical areas, due to the resolution change at day15, and also there could be some smaller inconsistencies between the ensemble because of the forcing change from 15 to 16 day, due to the mechanical blending which will mix different weather conditions from the low- and high-resolution meteorological data. However, overall as an ensemble of 51 scenarios, the blending not expected to lead to any larger discontinuities.
  • Seasonal:
    • 36 km seasonal forecasts (SEAS5, LINK)
  • Number of ensemble members: 51 ensemble members (one unperturbed and 50 perturbed) to represent the equally likely forecast scenarios of the future in both sub-seasonal and seasonal.
• River resolution:
  • 1 arcmin (~1.5 km) in EFAS
  • 3 arcmin (~5 km) in GloFAS
• Run frequency:
  • Sub-seasonal forecasts are generated daily at 00 UTC
  • Seasonal forecasts are generated monthly on the 1st of each month at 00 UTC
• Lead time:
  • Sub-seasonal has 45-day (1080 hours) lead time. Only 45 days, as although the low-resolution sub-seasonal meteo forecasts run out to 46 days, we can only rely on the 1-day-old (yesterday's) run, which means we can only run the simulations to 45 days ahead.
  • Seasonal has 7-month lead time (strictly speaking 215 days for each run date)
• Forecast step:
  • 6-hourly in EFAS
  • 24-hourly in GloFAS
• Hydrological forecast initialisation:
  • Sub-seasonal is initialised from a fillup simulation forced with the shortest-range ENS-Control (unperturbed member of the ECMWF ensemble forecast) meteorological conditions.
  • Seasonal is initialised from hydrological monitoring simulation, which is forced with gridded meteorological observations in EFAS and ERA5 meteorological reanalysis data in GloFAS.

Reforecasts

The second major component is the hydrological reforecasts, that are used to generate the model climatologies. These climatologies are range-dependent, i.e. they change with the lead time. The climatologies are produced from a large set of hydrological reforecasts, which allows for robust estimate of the climatological behaviour of the systems. The characteristics of the reforecast simulaions are described below. Where appropriate, the difference between EFAS/GloFAS and sub-seasonal/seasonal is specified. If there is no EFAS/GloFAS or sub-seasonal/seasonal mentioned, then the method is identical between the systems:

• Hydrological model used: LISFLOOD (LINK!)
• Meteorological forcing:
  • Sub-seasonal:
    • The combination of the 9km (horizontal resolution) ECMWF ensemble forecasts (LINK!) and the 36km ECMWF sub-seasonal forecasts (LINK!) from the same run date
    • The low-resolution sub-seasonal and high-resolution medium-range forcings are taken from the same forecast run date. The timing of the availability of the low-resolution forecasts is not an issue here, as these reforecasts are only produced retrospectively without any time constraint.
    • The high- and low-resolution forcing is currently combined by a simple mechanical blending by using the high resolution meteorological forcing for days 1-15, while the low-resolution meteorological forcing for days 16-46, for each of the 11 ensemble members. 
    • The blending can create inconsistencies locally over very complex orographical areas, due to the resolution change at day15, and also there could be some smaller inconsistencies between the ensemble because of the forcing change from 15 to 16 day, due to the mechanical blending which will mix different weather conditions from the low- and high-resolution meteorological data. However, overall as an ensemble of 11 scenarios, this will not be expected to lead to any larger discontinuities.
  • Seasonal:
    • 36 km seasonal forecasts (SEAS5, LINK)
  • Number of ensemble members:
    • Sub-seasonal has 11 ensemble members (one unperturbed and 10 perturbed) to represent the equally likely forecast scenarios of the future.
    • Seasonal has 25 ensemble members (one unperturbed and 10 perturbed) to represent the equally likely forecast scenarios of the future.
• River resolution:
  • 1 arcmin (~1.5 km) in EFAS
  • 3 arcmin (~5 km) in GloFAS.
• Run frequency:
  • Sub-seasonal reforecasts are generated always for the past 20 years. Before 12 Nov 2024, they were generated twice-weekly on Mondays and Thursdays (at 00 UTC), while from 12 Nov 2024 they are generated on given days of the months, i.e. 1, 5, 9, 13, 17, 21, 25 and 29 (excluding 29 Feb).
  • Seasonal reforecasts are generated for each month of the past back to 1981, although we only use them from 2004-2023. The shorter period, in the same way as for the sub-seasonal, provides better better stationarity (see https://www.ecmwf.int/en/elibrary/81194-trends-glofas-era5-river-discharge-reanalysis), which can be very important for the reliability of the forecasts.
• Lead time:
  • Sub-seasonal reforecasts have 46-day (1104 hours) lead time. This is 1-day longer than the real time forecasts, as these for the reforecasts there is no time constraint and the blending can be done with the high-resolution and low-resolution meteorological forecasts of the same run date (no need to delay by 1 day the low-resolution).
  • Seasonal reforecasts have the same 7-month (or 215-day) lead time.  
• Forecast steps:
  • 6-hourly in EFAS
  • 24-hourly in GloFAS
• Forecast hydrological initialisation
  • All reforecasts are initialised from the hydrological monitoring simulation, which is forced with gridded meteorological observations in EFAS and ERA5 meteorological reanalysis data in GloFAS. There is no reason to use fillup, since these are produced for dates well in the past (as for the real time forecasts).

Climatologies

The third major component is the range-dependent climatologies, that are generated from the hydrological reforecasts. The climatologies will give the reference point for the forecast anomaly computation. The reference points are the percentiles of the climate distribution from 1^st  to 99^th. The characteristics of the climatologies are described below. Where appropriate, the difference between EFAS/GloFAS and sub-seasonal/seasonal is specified. If there is no EFAS/GloFAS or sub-seasonal/seasonal mentioned, then the method is identical between the systems:

• Climate generation dates:
  • In the sub-seasonal, climate files are produced for each reforecast run date, so in total 8 (days per month)*12-1 = 95 dates in a calendar year with 1/5/9/13/17/21/25/29 of each month, excluding 29 February. The sub-seasonal climate files are generated continuously in a real time manner, right after the hydrological reforecasts are produced.
  • The seasonal climatology is produced only once for each month of the year (so 12 sets).
• Climate lead times:
  • In the sub-seasonal, for each of these climate dates, there will be climate files for each possible daily lead time of the 46-day sub-seasonal range. Here, not only calendar weeks are considered, but all possible 7-day lead times, starting from days1-7, then days2-8, ..., out to days 40-46 (40 possible lead times). This way, the weekly mean climatology for all possible lead times will be available, and so for each real time forecast the right climatology can be used, with the correct lead time in days, depending on which day of the week the real time forecast run happens (i.e. which corresponding climate lead time to choose in order to get to the calendar weeks). For example, if the sub-seasonal forecast run is a Wednesday, then the first lead time will be the following Monday-Sunday and the corresponding climatology will have the lead time of days6-12. However, if the forecast date is on Monday, then the first lead time will be days1-7 in the climatology, etc.
• Climate sample:
  • The sub-seasonal uses 20 years of hydrological reforecasts with run dates roughly twice a week. For the climate sample, always 3 run dates are used, the actual climate date (we generate the climate for) and one reforecast date before and after. The 3 dates combined will guarantee more robust estimates of the percentiles (especially the most extreme ones), with no impact on the seasonal variability (i.e. combining too many dates could negatively impact on the behaviour in climate zones with rapid shift between seasons). For example, when generating the climate sample for 15 December 2024, all reforecasts produced for 11, 15 and 19 December will be used in the climate sample from years 2004-2023 (so, 11 Dec 2004, 11 Dec 2025, ... 11 Dec 2023, then 15 Dec 2004, 15 Dec 2025, ..., 15 Dec 2023, then 19 Dec 2004, 19 Dec 2005, ..., 19 Dec 2023). The total size of the climate sample is 3*20*11 = 660.
  • The seasonal also uses 20 years of hydrological reforecasts. For each month, this will mean 20 sets of reforecasts in the climate sample, using all 25 ensemble members. The total size of the climate sample is 20 * 25 = 500.
  • , with reforecast values for each river pixel.
• Climate distribution: The climate will be generated from the climate sample by sorting the 660/500 values for each river pixel. The percentiles then are produced by dividing the climate range into 100 equally likely bins, separated by the percentiles from 1 to 99. The 1^st percentile is the value that is exceeded 99% of the time, while the 99^th percentile is the value that the reforecasts will exceed only 1% of the time, based on the 20-year climate period. These climate percentiles will be specific to the 40 possible lead times from days1-7 to days 40-46 and to all reforecast run dates in the sub-seasonal, and will be specific for all 12 months and for all the 7 monthly lead times.

Generation of the forecast anomaly and uncertainty signal

In a sub-seasonal forecast, especially at the the longer ranges, the day-to-day variability of the river flow, with prediction of the actual expected flood severities, can not be expected due to the very high uncertainties. What is possible, is to rather give an indication of the river discharge anomalies and confidence in those predicted anomalies. As the forecast range increases, the uncertainty will also generally increase and with it the sharpness of the forecast will gradually decrease and more and more often the forecast is just going to show the climatologically expected conditions with some possible positive or negative shift.

The generation of the sub-seasonal forecast signal is reflective of this and was designed to deliver a simple to understand categorical information on the anomalies and uncertainties present in the forecast, relative to the underlying weekly-mean-discharge-based climatology. The methodology to compute the anomaly and uncertainty information for the weekly mean sub-seasonal ensemble forecast is described on the page below. This page is generic and describes the procedure for any river pixel and for both the sub-seasonal and seasonal systems.

Placeholder CEMS-flood sub-seasonal and seasonal forecast anomaly and uncertainty computation methodology

Sub-seasonal prodcuts

There are two sub-seasonal layers available on the EFAS and GloFAS websites, listed below:

Product no-1: Seasonal outlook

This product highlights the forecast signal on the rivers, including all river pixels above 50 km² in EFAS and above 250 km² in GloFAS. The forecast signal is indicated by colouring. The colours represent the forecast anomaly and uncertainty categories, with 5 anomaly and 3 uncertainty sub-categories, in total with 15 different types of forecast signal. Each (large enough area) river pixel will be coloured all the time, as there is always a forecast signal, even a no signal (i.e. perfectly normal condition) is going to be indicated.

Each forecast lead time is going to be individually plotted in the layer, with the control panel allowing the option to navigate/animate, the users being able to go back and force within the forecast horizon and check each forecast weekly lead time.

Details about the layer can be found in the generic description of the river network summary layer for both the sub-seasonal and seasonal, as the structure and most features are the same across the two systems here: Placeholder sub-seasonal and seasonal forecast products

Product no-2: Seasonal outlook - basins

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