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e “Behind good forecast practices are often hidden good theories; equally, good theories should provide a basis for good forecast practices.” Professor Tor Bergeron, personal communication, 1974
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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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The User Guide is broadly divided into two parts. Sections 2 to 5 describe the structure of the ECMWF Integrated Forecasting System, while Sections . Sections 6 to 11 describe how the IFS may be used to best advantage by forecasters.
Links There are links to more detailed descriptions of processes are given, mainly at the end of each section, whilst separate . Separate online ECMWF training resources are also available to explain explain aspects of the ECMWF IFS more visually. Education is a
A key component of the work at ECMWF and further is education and training. Further educational material is available through the web site (e.g.:
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ECMWF Newsletters issued quarterly give information on IFS models and applications and ECMWF plans.
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Section 2 describes in broad, non-technical terms the ECMWF Integrated Forecast System (IFS) which . 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 Thus the definition of the model coastline by the land-sea mask is extremely important, not least in the way data is presented (e.g. . This is especially true for meteograms in coastal areas or on islands).
Numerical weather prediction (NWP) output is complicated by its often counter-intuitive and non-linear behaviour. Understanding model processes enables forecasters to assess model output critically.
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Section 5 describes the way the members of the ensemble are generated. The use of ENS allows assessment of uncertainty in the model forecast by giving a range of results. Each ensemble member starts from slightly perturbed initial data and . Consequently each evolves a little differently from the other members of the ensemble to give a range of possible forecast results. The variation seen within the ensemble forecasts gives an indication of predictability of the atmosphere.
Model climates are an important product produced within the IFS. These are: M-climate for ENS, ER-M-climate for Extended Range ENS, S-M-climate for Seasonal forecasting. They are a wholly model-based assessment of worldwide climatology based on analyses and re-forecasts over a period of 20 or 30 years.
Section6: Using ENS forecasts
Section 6 discusses the reliance that can be placed upon the ensemble as the forecast lead-time increases. Each ENS member starts from slightly perturbed initial data and member evolves a little differently from the other members of the ensemble to give others and gives a range of possible forecast results. The variation seen within the ensemble forecasts gives an indication of predictability of the atmosphere. The use of probabilities or other risk assessments is an essential part of building forecasts useful to the customer. This section emphasizes the benefit of using ensemble products to get the best description of evolution and uncertainty of the a forecast state of the atmosphere.
Section7: Dealing with uncertainty
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- imports into their own workstation environments,
- on the web as Opencharts,
- using the highly interactive ecCharts (ECMWF members and Co-operating States) web-based application.
Model climatesare an important product produced within the IFS. These are: M-climate for ENS, ER-M-climate for Extended Range ENS, S-M-climate for Seasonal forecasting. They are a wholly model-based assessment of worldwide climatology based on analyses and re-forecasts over a period of years (currently 20 years, but 30 years for seasonal forecasting). Model products may be deterministic, probabilistic, or in the form of anomalies from normal where normal is defined by the as defined by model climates. ENS ENS output in the form of is shown in an easy-to-use form as:
- charts, plumes, meteograms (and wave meteograms)
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- .
- charts showing the
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- evolutions of tropical cyclones and
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- extra-tropical depressions.
- charts giving an indication of the variability and uncertainty among the basic model forecasts
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- . These also compare the latest model output with its predecessors.
The model climates are used extensively to highlight occasions when locally extreme weather conditions that have been forecast by the ENS are locally extreme for that 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 providing 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.
The overall aim is to allow assessment of uncertainty to provide the customer with the best and most useful guidance possible.
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Section 9 gives pointers towards features which can have an impact on model output and allow . 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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