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  • The proportion of previous forecasts that are "better" than the latest ones increases with lead-time:
    • at short lead-times a small but significant proportion appear better (~15% at Day2).
    • at longer lead-times a larger a larger proportion appear better (~40% at Day6).  (Fig7.2-5).
  • There is only a very small correlation between forecast jumpiness and the quality of the latest forecast (Fig7.2-6).
  • Beyond about Day3 the ensemble mean, by using results from all ensemble members, provides more consistent forecasts than the ensemble control.  This benefit gradually increases with forecast range.  
  • The frequency of a flip (single jump) is very similar for both the ensemble mean and ensemble control.
  • The frequency of flip-flopping occurs clearly less frequently in the ensemble mean than in the ensemble control.

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

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  • the connection between forecast inconsistency (flip-flopping etc) and forecast error is weak,
  • the average error of the ensemble mean relates quite strongly to the absolute spread in the ensemble.  
  • on average, larger spread implies larger errors (this does not apply to the ensemble median or ensemble control, even if they happen to lie mid-range within the ensemble).

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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).  Nevertheless, this should 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 this runs the risk that the forecast from the next run will be even further away from the earlier solutions. In effect, the forecaster could be trying catch up with the NWP model forecasts.  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.

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