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• Sub-grid variability, which itself varies according to the "gridbox-weather-type"
  
• Model biases, on the gridscalegrid-scale, also intrinsic to the "gridbox-weather-type"

The sub-grid variability issue was illustrated on Fig8.1.7-1.  Model bias in ENS rainfall forecasts, on the gridscalegrid-scale, can also be discussed by referencing this figure. Let's say that, when a particular gridbox-weather-type is forecast in an ENS member, which was what was occurring in the highlighted gridbox on this day, that ENS member will tend, on average, to over-predict the gridbox rainfall by 15%, then one would expect the ENS to deliver a forecast not of 17mm, but of ~20mm. During the post-processing ecPoint should, as well as accounting for sub-grid variability for that ENS member, also reduce the gridbox average from 20mm to 17mm.

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Fig8.1.7-2 shows CDF examples for different sites on two different occasions for the full ensemble, illustrating both bias correction and the addition of sub-grid variability within ecPoint (notably for the first plot), but showing also that the point rainfall and raw model gridscale distributions grid-scale distributions may sometimes be quite similar (second plot).

Fig8.1.7-2: Two CDF examples comparing raw gridscale grid-scale (red), post-processed bias-corrected gridscale grid-scale (green), and post-processed point rainfall (blue). The first plot is for a site in Spain in summer at day 2. The second plot is for a southern England site in autumn, at day 4. Note that for each case the areas to the left of the green and blue curves, that represent the mean gridbox rainfall over the ENS, should be the same.

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