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Medium Range Forecasting - using ensemble forecasts alone

Ensemble forecasts (ENS) offer the most consistent method of achieving good and consistent forecasts but they are usefully augmented by HRES forecasts to give information on possible finer scale detail.  Using ENS alone is not recommended, although this may be necessary very rarely.   Nevertheless, the information in this section regarding use and interpretaion of ENS output can and should be usefully employed whether HRES is available or not.  Note also that ECMWF strategy 2015-2025 centres on ENS development, rather than on HRES, so it is vital that users adopt ways of working successfully with the ensemble.

Use of the ensemble mean (EM)

Generally, whether the ensemble spread is small or large, the EM (or median if applicable) will, beyond the short range, exhibit higher accuracy than the ensemble control (CTRL) or HRES forecast.  This is particularly true for parameters, such as mean sea level pressure (MSLP) and temperature.  However, with increasing spread amongst EM members, it becomes more appropriate to couch the forecast information in the form of probabilities rather than as predicted values  - this is particularly true for parameters such as precipitation and cloud amounts.  The EM also displays a higher degree of day-to-day consistency.  Jumpiness in the EM is also markedly less, on average, than seen in CTRL or HRES, particularly when examining forecasts beyond about Day3. 

Criticism of the ensemble mean (EM)

EM forecasts and, similarly averages of forecasts from the same or different NWP models, provide more accurate and considerably less “jumpy” solutions, but meteorologists are somewhat apprehensive about using them.  This reluctance derives mainly from three reasons:

  • Ensemble averages do not constitute genuine, dynamically consistent, three-dimensional representations of the atmosphere.
  • Ensemble averages are less able to represent extreme or anomalous weather events.  Event probabilities or the Extreme Forecast Index (EFI) should be used instead.
  • Ensemble averages might lead to inconsistencies between different parameters.  For example, the ensemble cloud average (or median) might not be consistent with the average (or median) of the precipitation.
  • On average, gradients in EM fields (e.g. mean sea level pressure) systematically reduce with lead time, which can give misleading guidance on other parameters (e.g. wind strength, which is commonly inferred from the isobaric gradient).

A synoptic example of combining ensemble mean and probabilities

It is important to avoid over-interpretation of the EM, in particular underestimation of the risk of extreme weather events.   To aid visual interpretation by the user, EM output should be presented together with a measure of the ensemble spread.  The EM and the probabilities relate naturally to each other and can be most effective when shown together.  So, for example, the EM of the MSLP (or 1000hPa) presented together with gale probabilities will put the latter into a synoptic context that will help interpretation (see Fig6.1.1).

Fig6.1.1 (same as Fig4.1.5): 1000hPa forecast from 12UTC 13 August 2010 T+156hr to 00UTC 16 August 00 UTC T+96 h, all valid at 00UTC 20 August 2010.   Full lines are the 1000 hPa geopotential EM overlaid by the probabilities of wind speeds >10m/s.  Probabilities are coloured in 20% intervals starting from 20%.  Compare with Fig4.1.3 and Fig4.1.4.


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