Real time forecasts

The first major component necessary to generate the EFAS/GloFAS sub-seasonal and seasonal products is the hydrological forecasts produced in real time (see Figure 1). This component gives the predicted conditions for the sub-seasonal and seasonal products to be compared to the climatologies to derive the forecast anomaly and uncertainty.


Figure 1. Flowchart of real-time forecast section of the sub-seasonal and seasonal anomaly and uncertainty signal generation methodology.

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 (LISFLOOD*)
  • Meteorological forcing:
    • Sub-seasonal:
      • The combination of the 9km (horizontal resolution) ECMWF ensemble forecasts and the 36km ECMWF sub-seasonal forecasts (see EFAS Meteorological forecasts and GloFAS meteorological forecasts for further details)
      • The high- and low-resolution forcing is 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 51 ensemble members in the same order (i.e. member 1 of ECMWF ensemble forecasts followed by member 1 of ECMWF sub-seasonal forecasts). 
      • 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 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 was shown not to lead to any larger discontinuities.
    • Seasonal:
      • 36 km seasonal forecasts (SEAS5)
    • 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 for the 00 UTC forecast run
    • Seasonal forecasts are generated monthly for the run date of 1st of each month at 00 UTC
  • Lead time:
    • Sub-seasonal has 46-day (1104 hours) lead time.
    • Seasonal has 7-month lead time (strictly speaking 215 days for each run date).
  • Forecast step:
    • Sub-seasonal: 24-hourly in both EFAS and GloFAS 
    • Seasonal: 24-hourly in both EFAS and 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 (see Figure 2). 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.


Figure 2. Flowchart of the details in the model climatology generation for the sub-seasonal and seasonal forecasts.

The characteristics of the reforecast 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 (LISFLOOD*)
  • Meteorological forcing:
    • Sub-seasonal:
      • The combination of the 9km (horizontal resolution) ECMWF ensemble forecasts and the 36km ECMWF sub-seasonal forecasts (see EFAS Meteorological forecasts and GloFAS meteorological forecasts for further details)
      • The high- and low-resolution forcing is 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 in the same order (i.e. member 1 of ECMWF ensemble forecasts and member 1 of ECMWF sub-seasonal forecasts)
      • 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 is not be expected to lead to any larger discontinuities.
    • Seasonal:
      • 36 km seasonal forecasts (SEAS5)
    • 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 24 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 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.
    • Seasonal reforecasts have the same 7-month (or 215-day) lead time.  
  • Forecast steps:
    • Sub-seasonal:
      • EFAS: 6-hourly
      • GloFAS: 24-hourly 
    • Seasonal: 24-hourly in both EFAS and 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.

Climatologies

The third major component is the range-dependent climatologies, that are generated from the hydrological reforecasts (see Figure 2 above). The climatologies give the reference point for the forecast anomaly computation. The reference points are the percentiles of the climate distribution from 1st  to 99th. 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).
    • The seasonal hydrological reforecasts and real-time forecasts use the same (fixed) meteorological modelling system (SEAS5), with all seasonal meteorological reforecasts generated in one go for all dates (and therefore the hydrological reforecasts also generated for all dates). Therefore, for the seasonal, all climatologies could be produced at once and used for any future forecast. This is not the same for the sub-seasonal, where the meteorological modelling system changes over time (usually with 1 or 2 updates a year). This means, the meteorological reforecasts (and then the subsequent hydrological reforecasts) are going to be produced in a real-time manner instead, to guarantee model consistency between the reforecasts and real-time forecasts.
  • Climate lead times:
    • In the sub-seasonal, for each of the reforecast run dates, climate files are produced with weekly-averaged river discharge for all lead times from day7 to day46 (starting from days1-7, then days2-8, ..., out to days 40-46). The reason, that all possible 7-day lead times are considered is to allow the right climate files, with the appropriate lead time, to be used for the real-time forecasts. This is necessary due to the calendar weekly (Monday to Sunday) lead time periods being used in the sub-seasonal. With the fixed calendar weeks, the lead times will change according to which date of the week is the real-time forecast run date (i.e. which corresponding climate lead time to choose in order to get to the Monday-Sunday periods). For example, if the sub-seasonal forecast run is on a Wednesday, then the first forecast period will be the following Monday-Sunday with a corresponding forecast lead time of days 6-12, therefore requiring the climatology set with this days6-12 lead time. However, if for example the forecast run date is on a Monday, then the period starts immediately on that Monday with lead time of days1-7, etc.
    • In the seasonal, climate files are produced with the monthly-averaged river discharge for each month of the year with all of the 7 monthly lead times.
  • Climate sample:
    • The sub-seasonal uses 20 years of hydrological reforecasts with run dates roughly twice a week, using 11 ensemble members. For the climate sample, we combine 3 run dates: the actual climate date (we generate the climate for) and the previous/ following reforecast dates. This is to 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 are 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 uses 20 years of hydrological reforecasts. For each month, this means 20 sets of reforecasts in the climate sample, using all 25 ensemble members. The total size of the climate sample is 20 * 25 = 500.
  • Climatologic distribution: The climatologic distribution is 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 1st percentile is the value that is exceeded 99% of the time, while the 99th percentile is the value that the reforecasts exceed only 1% of the time, based on the 20-year climate period. These climate percentiles are 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 are specific for all 12 months and for all the 7 monthly lead times for all of them.