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A glossary is included in an Appendix.

Section2: The ECMWF Integrated Forecasting System (IFS)

Section 2 describes in broad, non-technical terms the ECMWF Integrated Forecast System (IFS) which 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. 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.

Section3: Availability and interpolation of NWP output

Section 3 gives an overview of the way ECMWF graphical forecast products are presented to the forecaster.  It gives some insights into ways the analysed and forecast data may be reduced in accuracy by the way it is presented.

Section4: NWP evolution versus reality

Section 4 discusses model error growth with time and the relationship between predictability and scale.  An indication is given of how anomalies propagate downstream and gives some pointers towards recognition of these in the analysis.

Section5: Forecast ensemble (ENS) - rationale and construction

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 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.

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 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.  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 forecast state of the atmosphere.

Section7: Dealing with uncertainty

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 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 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 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 model climates.  ENS output in the form of charts, plumes, meteograms (and wave meteograms), and charts showing the various evolutions of tropical cyclonesand extratropical depressions all give an easy-to-use presentation of data.  Other charts give indication of the variability and uncertainty among the basic model forecasts or compare the latest model output with its predecessors.  The model climates are used extensively to highlight occasions when 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 information about how extreme an event might be 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. 

Section9: Physical considerations when interpreting model output

Section 9 gives pointers towards features which can have an impact on model output and allow 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.

Section10: Interfaces for displaying model output

Section10 gives an outline of the way forecast data may be presented to the user.  ECMWF Web Charts (Open Access) give easy access to ECMWF IFS output.  The more flexible and interactive ecCharts allows users to pick-and-mix the IFS data.

Section11: Conclusion

Section11 highlights the continuing importance of the forecaster in providing a consistent and useful product to the customer.

Section12: Appendices

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

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