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Note: HRES and Ensemble Control Forecast (ex-HRES) are scientifically, structurally and computationally identical.  With effect from Cy49r1, Ensemble Control Forecast (ex-HRES) output is equivalent to HRES output where shown in the diagrams.   At the time of the diagrams, HRES had resolution of 9km and ensemble members had a resolution of 18km.

Also with effect from Cy49r1 the extended range forecast is now known as the sub-seasonal forecast. 

An alternative name for for Ensemble Control Forecast (ex-HRES) is IFS-CF.

Alternative names for Sub-seasonal Forecast are IFS-SUBS, IFS-SSP, or IFS-S2S

Aim of the Forecaster User Guide

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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 and atmospheric physics 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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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.  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 medium range ENS, ER-M-climate for sub-seasonal Range range ENS, S-M-climate for Seasonal seasonal forecasting.  They are a wholly model-based assessment of worldwide climatology based on analyses and re-forecasts over a previous period of 20 or 30 years.  

Section6: Using

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ensemble forecasts

Section 6 discusses the reliance that can be placed upon the ensemble as the forecast lead-time increases.  Each ENS slightly perturbed ensemble member evolves a little differently from the others and gives a range of possible forecast results.  The variation seen 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 a forecast.

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Section 7 concentrates on methods that may be used to assess confidence in model results.   This section gives guidance on interpretation of latest and previous ENS ensembles output to allow insight into the uncertainty of the forecast.  It also gives guidance on assessing the skill of a forecast and how to use run-to-run variability in the forecasts to best advantage.  The continuing role of the human forecaster is emphasized.

Section8: ENS products - what they are and how to use them

Section 8 concentrates on making best use of the extensive range of products that are available.  The IFS produces a very wide range of products which is delivered in the form of charts or GRIB format datasets.  It is readily available to forecasters via:

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Model products may be deterministic, probabilistic, or in the form of anomalies from normal as defined by model climates.  ENS  Ensemble output is shown in an easy-to-use form as:

• charts, plumes, meteograms (and wave meteograms).
• charts showing the evolutions of tropical cyclones and extra-tropical depressions.
• charts giving an indication of the variability and uncertainty among the basic model forecasts.  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 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 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 ensemble 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. 

Section9: Physical considerations when interpreting model output

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.  These will be addressed in the future but 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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Section 11 highlights the continuing importance of the forecaster in providing a consistent and useful product to the customer.

Section12: Appendices

Section 12 contains additional detail on statistical concepts for verifying model forecasts, the current structure of IFS, and a list of acronyms.

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