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Fig7.2.2: An example of a forecast exhibiting jumpiness in the form of a major flip-flop.  On the diagram the y-axis is for forecast 2m temperatures, the x-axis shows the data time of the ensemble forecast.  The plotted values are for the forecast temperature at Paris verifying at 00Z 8 Dec 2016.  Forecast ensemble results are shown by box and whisker plots (described in Meteogram section), forecast ensemble mean values shown by black dots (red dots show values from the now obsolete HRES the HRES).  Initially Day15 to Day11 forecasts were around 5°C or 6°C although with a broad range of up to ±8 to 10°C.   From 12UTC 27 Nov (Day10½) the forecast temperatures jumped to much colder values round -2°C with a relatively small spread of ±3 or4°C.  From 12UTC 30 Nov (Day7½) the forecast temperature rose suddenly back to around +6°C with a broader spread of ±8 to 10°C.  From 12UTC 3 Dec (Day4½) the forecast temperatures reverted to around +4°C with range of ±2 or 3C.   It should be remembered in general each ensemble solution should be viewed as one possible solution that is a member of a greater ensemble of the latest and recent solutions, although the later solutions do have the benefit of the most up to date data.

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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).  (Fig7.2.4).
  • There is only a very small correlation between forecast jumpiness and the quality of the latest forecast (Fig7.2.5).
  • 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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