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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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The User Guide is broadly divided into two parts. Sections 2 to 5 describe the structure of the ECMWF Integrated Forecasting System, while 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. Webinars (recordings), Slidecasts (slides and audio recordings), Tutorials, Training lectures (presentations in PDF)). 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. 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 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.
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).
Section6: Using ENS forecasts
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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 extratropical depressions
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- .
- charts giving an 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 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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