Page History

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Note:

• ECMWF strategy 2015-2025 centres is centred on ENS development
• The medium range control member (CTRL) became almost identical to HRES upon the introduction of Cy48r1 in June 2023.

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The main strategy to adopt is to avoid over-interpreting non-predictable features.  Therefore the detail of the most recent ensemble members should not be used in isolation.   Run-to-run jumpiness can on the one hand be tackled as something negative that has to be dampened, but on the other hand as something positive which can enrich the forecast information by giving alternative scenarios.  Ensemble members can give an indication of the probability and the consistency of features of the forecast.

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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).  This is particularly true for parameters, such as mean sea level pressure (MSLP) and temperature.  However, with increasing spread amongst ensemble 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 ensemble mean also displays a higher degree of day-to-day consistency.  Jumpiness in the ensemble mean is also markedly less, on average, than seen in the ensemble control, particularly when examining forecasts beyond about Day3. 

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Ensemble mean forecasts provide more accurate and considerably less “jumpy” solutions.  Averages of forecasts from the same or different NWP models are similarly more accurate.  However, 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 extreme forecast index (EFI) should  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 ensemble mean fields (e.g. ensemble average 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).
  

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