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Fig7.2-3: An example of a forecast showing a major flip with a major difference in the forecast depth and extension of the upper trough over Eastern Europe.  The HRES charts show 500hPa forecasts at 9km resolution VT 00UTC 25 Dec 2012.  Upper chart: T+144 DT 00UTC 19 Dec 2012 with cold air across forecast Greece and SE Europe.  Lower chart: T+120 DT 00UTC 20 Dec 2012 with warm airmass over Greece and SE Europe.  

Note: HRES and Ensemble Control Forecast are scientifically, structurally and computationally identical.  With effect from Cy49r1, Ensemble Control Forecast output is equivalent to HRES output shown in the diagrams.   At the time of the diagrams, HRES had resolution of 9km and ensemble members had a resolution of 18km.

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Right panel: The 500 hPa temperature forecast (at 9km resolution) and the actual ensemble Standard Deviation (at 18km resolution).  This shows that in absolute terms (as well as relative terms) there is a wide spread of ensemble solutions over eastern Europe (standard deviation between 4.5°C and 8.0°C).

Note: HRES and Ensemble Control Forecast are scientifically, structurally and computationally identical.  With effect from Cy49r1, Ensemble Control Forecast output is equivalent to HRES output shown in the diagrams.   At the time of the diagrams, HRES had resolution of 9km and ensemble members had a resolution of 18km.

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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).

    • ...

    • 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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    Fig7.2-9: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 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.  

    Note: HRES and Ensemble Control Forecast are scientifically, structurally and computationally identical.  With effect from Cy49r1, Ensemble Control Forecast output is equivalent to HRES output shown in the diagrams.   At the time of the diagrams, HRES had resolution of 9km and ensemble members had a resolution of 18km.

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    (Note: In older material there may be references to issues that have subsequently been addressed)

    Nil currently.



    (FUG Associated associated with Cy50r1)