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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.
There are links to more detailed descriptions of processes, mainly at the end of each section. Separate online ECMWF training resources explain aspects of the ECMWF IFS more visually.
A key component of the work at ECMWF 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). 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, not least in the way data is presented (e.g. especially 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 years (currently 20 years, but 20 or 30 years for seasonal forecasting).
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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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 will be addressed in the future but which meanwhile 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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