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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 first ingredient is the actual forecasts that is produced in real time. 

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:

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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:

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Component-3. Climatologies

The sub-seasonal products rely on third major component is the range-dependent climatologies, that change with the forecast lead time, and which are produced are generated from the hydrological reforecasts. The climatologies will give the reference point for the different anomaly categories applied in the sub-seasonal range. These forecast anomaly computation. The reference points are some the percentiles of the specific quantiles from the climate distribution, such as the 10th and 90th percentile values. In the following we describe the main climate distribution from 1^st  to 99^th. The characteristics of the climatologies are described below. Where  Where appropriate, the difference between EFAS and GloFAS /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 two systems:

• Climate generation dates:
  • In the sub-seasonal,
  We currently produce
  • 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
  Feb.
  • 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
  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: For the
  • The sub-seasonal
  , we have
  • 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
  data
  • 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).
  All ensemble members are considered to be independent, and the total
  • The total size of the climate sample
  size
  • 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: Climate percentiles are produced 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, where the 1st . The 1^st percentile is the value that is exceeded 99% of the time, while the 99th 99^th percentile will be is the value that the reforecasts will exceed only 1% of the time. These , 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 similarly specific 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

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