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• medium-range (day 0 to day 15) forecast products.  This output generally differs significantly from that dealing with short-range or seasonal NWP.   
• extended range (day 16 to day 42) forecasts products.  These concentrate on the probabilities of anomalies from the norm during 5-7 day forecast periods at a for given location for any and time of year.
• seasonal forecasts (month 1 to month 7 or 13).  These give an indication of likely conditions beyond six weeks ahead.  They are run monthly giving forecasts to 7 months ahead, and run quarterly with forecasts extended to 13 months ahead.  Output concentrates on the anomalies relative to the seasonal climate.  

The IFS models areconfigurations are:

• the 10-day ensemble High Resolution forecast (ENSHRES).
• the 15-day ensemble forecast (ENS).
• the 46-day extended range forecast including the extended range control member.
• the 7 or 13 month seasonal forecast.

The ECMWF model output is delivered in the form of charts or GRIB format datasets.  It is readily available to forecasters via:

• imports into their own

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• work station environments.
• on the web as Opencharts.
• using the highly interactive ecCharts (ECMWF members and Co-operating States) web-based application

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The ECMWF IFS is upgraded at roughly half yearly intervals to incorporate better improved representation of physical processes and/or higher vertical or horizontal resolution changes.   New products increasingly aid early warning of severe or hazardous weather.  Information on the latest upgrade is given below.

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Section 2 describes in broad, non-technical terms the ECMWF Integrated Forecast System (IFS).  This comprises the global atmospheric model, the wave and the oceanic dynamical models, and the data assimilation systems.  It gives an overview of the way the atmospheric model uses sub-gridscale parameterisations for processes within the atmosphere and at the surface.  There are large differences in energy fluxes between land or sea and the atmosphere.  Thus the definition of the model coastline by the land-sea mask is extremely important, . This is especially true for meteograms in coastal areas or on islands.

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• charts, plumes, meteograms (and wave meteograms).
• charts showing the evolutions of tropical cyclones and extratropical extra-tropical depressions.
• charts giving an indication of the variability and uncertainty among the basic model forecasts or .  These also compare the latest model output with its predecessors.

The model climates are used extensively to highlight locally extreme weather conditions for time of year and for the given forecast lead time.  The Extreme Forecast Index (EFI), pioneered at ECMWF, compares the forecast probability distribution with the corresponding model climate distribution.  The Shift of Tails (SOT) index complements the Extreme Forecast Index (EFI) by giving information about how extreme an event might be.  This is done by comparing the tail of the ENS distribution with the tail of the M-climate.

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Section 9 gives pointers towards features which can have an impact on model output.  This allows users to modify and improve forecasts for issue to customers.  Some other short-comings of the models are noted which .  These will be addressed in the future but which meanwhile they need to be considered by the forecaster.  It is through forecaster user feedback that important points will be identified and addressed.  The importance of critical assessment of model output by human forecasters cannot be understated.

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Section12 contains additional detail on statistical concepts for verifying model forecasts, the current structure of IFS, and a list of acronyms, and some references.

Comments on application of IFS and the Forecaster User Guide

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Some major changes were made to the IFS with the introduction of Cy48r1 in June 2023.  These are:

• For the medium range ensemble forecast system:
  • the horizontal resolution is increased to 9 Km
  • the vertical resolution remains unchanged at 137 model levels. 
  • the number of ensemble members remains unchanged at 50 members plus a control member.
  • the horizontal and vertical resolutions are identical to those of the High Resolution (HRES) in earlier versions of IFS.
  • the medium range ensembles are run twice daily from Day0-Day10 and slightly later from Day-0 to Day15.  
• For the extended range ensemble forecast system:
  • the horizontal resolution remains unchanged at 36 km.
  • the vertical resolution is increased to 137 model levels.  This is the same vertical resolution as the medium range ensemble ( and the High Resolution (HRES) in earlier versions of IFS)model.
  • the number of ensemble members is 100 members plus a control member.
  • the extended range ensemble is run daily from Day0-Day46.
• A multi-layer snow scheme was introduced.

Note: The extended range forecasts are not just an extension of the medium-range forecasts but are completely separate forecast systems.  However, both start from very similar analyses.  There are two sets of re-forecasts, one for the medium range and one for the extended range. 

The HRES and medium range unperturbed (control) ensemble member of the medium range ensemble (CTRL):

• have the same horizontal and vertical resolution.
• are meteorologically equivalent.
• are equally skilful on average.  

However, they can diverge on a day-to-day basis due to small technical differences and also the chaotic nature of the atmosphere.  Nevertheless and are virtually identical.   Nevertheless the HRES will continue for the time being for ease of use by customers and users.

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Users are advised to keep themselves updated about changes and improvements to products and model processes through the ECMWF Newsletter and web site (e.g. via the Forecast User portal)

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This User Guide has been compiled  by Bob Owens, with assistance from Tim Hewson, and with contributions from many other scientists and ex-forecasters at ECMWF. It is an updated version of the "User Guide to ECMWF Forecast Products" written originally by Anders Persson and published in 2011 (that had minor adjustments in 2013 and 2015).

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