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• an atmospheric model run at various resolutions appropriate to the forecast length: HRES for (Day0-Day10), ensemble (ENS) (for Day0-Day15), extended-range (for Day0-Day46), and seasonal forecast (for month0-month7 or month0-month13).
• an ocean wave model (ECWAM).
• an ocean model (NEMO) including a sea ice model, the Louvain-la-Neuve Sea Ice Model (LIM2).
• a land surface model (HTESSEL) including a lake model (FLake).
• a data analysis system (4D-VAR).
• perturbation techniques for generation of the ensembles.
  
  

These are outlined in Fig2-1, Fig 2-2 and Fig2-3.  The models and programs run co-operatively to produce forecasts from analysis time out to days, weeks or months ahead.

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Fig2-1:  ECMWF Integrated Forecasting System (IFS) Illustrates interactions between components of the IFS (observation assimilation and post-processing not shown)

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• the land is the lower boundary for the atmosphere for a less extensive area than the sea but it has a complex orography and exhibits far more temporal and spatial variability in the characteristics of energy storage and exchange.
• the lower boundary gives important feedback to the atmosphere if accurately modelled (important for short and long-range forecasting).
• the state of the landmasses (including soil moisture, cover and depth of snow, and lake ice) can change on a daily timescale and these variations can be important in certain situations during the forecast evolution.  Important aspects of the modelled state of the land that can affect the atmospheric evolution include:
  • rainfall absorption and soil moisture (give more information on boundary moisture flux),
  • ice variations on lakes (gives more information on albedo and boundary heat and moisture fluxes),
  • land/lake temperature variations for evaluation of heat fluxes,
  • snow variations and extent (gives more information on albedo and boundary heat and moisture fluxes).
     

The coupled model configuration is used with:

• the ENS and HRES forecast HRES forecast (Day0 to Day10) twice daily based on 00UTC and 12UTC data.
• the ENS forecast (Day0 to Day15) twice daily based on 00UTC and 12UTC data.
• the Extended Range forecast (Day0 to Day46) once daily based on 00UTC data. 
• the Seasonal forecast (Month0 to Month7 once each month, Month0 to Month13 once each quarter) on first day of the month based on 00UTC data.  

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