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Perturbations, positive and negative, spread the ensemble forecasts either side of the ensemble control (CTRL) early Ensemble Control (ex-HRES) early in the forecast, and any jumps in the ensemble control are likely be shown by the ensemble also.  At very short lead-times, before perturbations have had time to amplify, the ensemble mean (EM) will be very similar to the ensemble control.   Later in the forecast non-linearity becomes more important, and so the ensemble members are less similar to the ensemble ensemble control.  Thus the ensemble mean forecast is, on average, a less jumpy and a more reliable forecast than the ensemble control.

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Persson and Strauss (1995), Zsótér et al. (2009) found:

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Table 7.2-1:The percentage of cases when >2mm/24hr has been observed when up to three consecutive ECMWF runs (T+84hr, T+96hr and T+108hr) have forecast >2mm/24hr for Volkel, Netherlands October 2007-September 2010.  R indicates where such rain has been forecast and has occurred.  Similar results are found for other west and north European locations and for other NWP medium-range models.

Weakness of an Intuitive Approach towards likely outcomes

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Fig7.2-7: An example of notable jumpiness in weekly ensemble mean anomaly of 2m temperature.  Sequential sub-seasonal range forecasts DTs 00UTC 13 to 24 Nov 2024; VT for week 24-30 Nov 2024.  

• In Great Britain: the forecasts from 13 to 18 Nov were colder than normal, 19 to 21 November were warmer than normal, 22 Nov colder than normal, 23 Nov warmer and finally 24 Nov close normal.
• In southern Scandinavia: the forecasts from 13 to 18 Nov were colder than normal, 19 to 24 Nov were warmer than normal.
• In southeastern Europe and southwest Russia: the forecasts on 13 Nov and 16 Nov were warmer than normal, at other times to 18 Nov were nearer normal, 19 to 24 Nov were much colder than normal .

Weakness of an Intuitive Approach towards likely outcomes

It is without doubt difficult to choose which is the more likely outcome when a series of forecasts are showing large variations, trends and/or flip-It is without doubt difficult to choose which is the more likely outcome when a series of forecasts are showing large variations, trends and/or flip-flops.  A simple exercise in recent ECMWF training courses illustrates the problem.  Students were asked to interpret the expected temperature from a series of previous sequential NWP model forecasts verifying on the same day, and to accordingly provide a single deterministic forecast for that day, based on that information.  The students used several, largely intuitive, “forecasting techniques” (see Table 7.2.-2) but in but in the end none of them can be deemed to be particularly efficient (though any one of them could possibly have captured the correct result in a given situation).  The spread of he of he student's forecasts gives some idea of confidence inherent in the pattern of the of the forecast information provided:

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Table 7.2-2: Efficiency of intuitive techniques to assess outcomes given a series of forecast values from a series of sequential deterministic forecasts.

Fig7.2-78: The graphs show sample schematic forecasts of 12UTC temperature over four successive NWP model runs: Jumpy (top) and Trend (bottom).  The histograms show the forecasts made by the students using their own techniques.   Spread was low with the jumpy forecast case since the oscillations remained fairly steady throughout, and the next forecast could be higher or lower without changing the range of the oscillation much. The spread was high with the trend forecast case illustrating the point that the next forecast may well be higher than the one before but destroy the trend, or lower than the one before, continuing the trend.

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• adjust a forecast value (e.g. temperature, rainfall, etc) slightly lower or higher to follow the latest indications (e.g. warmer/cooler, wetter/drier, etc), but nevertheless to remain within the range of ensemble solutions from the latest and previous runs.  Reducing the change suggested by a noteworthy jump in the forecast can be the most appropriate course of action - but it does run the risk that the forecast from the next run will be even further away from the earlier solutions (i.e. the forecaster could be trying catch up with the NWP model forecasts and this illustrates one of the ways in which accuracy will be reduced).  On the other hand, it should be remembered that to follow a trend is also unreliable ~50% of the time.

• check whether the ensemble mean and probabilities are fairly consistent with previous runs.   If not, consider creating a lagged ensemble of the last two or three ensemble forecasts to give two or three times the number of members.  This will smooth out sudden changes in evolution while preserving the breadth of possible forecast extremes and probability information from the latest run.  A grand ensemble of ECMWF forecast results may be considered to compare latest forecast results with those of other state-of-the-art NWP models. 
  

• follow the ensemble mean rather than the ensemble control.  This can be more informative, especially at longer lead-times (say ≥ ~ 4 days).   However, note that strong gradients are always weakened in the ensemble mean and fine scale features (e.g. sting jets) will not be visible.
• inspect the Cumulative Density Function (CDF) of ensemble forecasts.  This can give a useful indication on the ensemble forecast values during the jumpiness.  At longer lead-times forecast CDFs may be similar to the M-climate.  But, with time, CDF between successive runs should show less lateral variation and tend to become steeper implying higher confidence.
  
  

Fig7.2-89:An example of Cumulative Density Function (CDF) produced by a sequence of ensemble forecasts for precipitation at Zaga in Slovenia verifying for the 24hr 00UTC 27 to 00UTC 28 April 2017.  All show a very high extreme forecast index (EFI).  Note the four earlier CDFs (blues) showed a moderate slope indicating a spread of forecast precipitation intensities, and then jumped to a steeper slopes (purple and red) with lessening of spread of precipitation intensities.  Here the forecast showed a steady trend towards heavier precipitation with a jump to very heavy precipitation.  A forecaster would have been unwise at the time of the T+60 to 84hr forecast (rightmost dashed blue line) to think that this significantly wetter forecast overall was too much of  jump from the trend to be believed.  

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