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In April 2019 ECMWF introduced a new type of experimental product - "Point Rainfall" - into ecCharts, following several years of development work. The issue it aims to address is illustrated on Fig817.AFig8.1.7-1.

Fig817.AFig8.1.7-1: Radar-derived rainfall totals over part of Northern Ireland for the 12h period ending 00UTC 29 July 2018. Scale is in mm. Flash floods occurred in some locations.  Figure based on data from netweather.tv (external website).

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The sub-grid variability issue was illustrated on Fig817AFig8.1.7-1.  Model bias in ENS rainfall forecasts, on the gridscale, 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.

In practice, ecPoint post-processing creates, on the basis of the calibration results (see below), an "ensemble of ensembles", that is 100 new point rainfall realisations for each ENS member, and so comprises, at one intermediate stage in the computations, 5100 equi-probable values of point rainfall totals within each ENS gridbox. However, prior to saving, the values for each gridbox are sorted, and distilled down into 99 percentile fields (1,2,..99). As a set these percentile fields constitute the Point Rainfall product, that can be displayed in different ways. Put together they also make a point rainfall distribution, which one could compare directly with an equivalent distribution formed by putting together the 51 members of the raw ENS (compare red and blue curves on Fig817.BFig8.1.7-2).

Fig817.B shows 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 may sometimes be quite similar (second plot).

Fig817.BFig8.1.7-2: Two CDF examples comparing raw gridscale (red), post-processed bias-corrected gridscale (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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For percentiles 4 colour-fill styles and one contouring style are available for the point rainfall. For probabilities there are 2 colour-fill styles and one contouring style. All 6 colour-fill styles are illustrated in legend format on Fig817 Fig8.C. These 1.7-3 These styles have also been added as options for the raw ENS ("total precipitation percentile" and "total precipitation probability") layers to facilitate direct comparison with the point rainfall; within ecCharts users need to scroll down through the "style" options to find them.

Fig817.CFig8.1.7-3: Colour fill options for a user-defined percentile (first plot, amounts in mm), and a user defined threshold (second plot, probability in %)

Fig 817.D and Fig817.E 8.1.7-4 and Fig8.1.7-5 below show some ecCharts examples of new products, comparing with raw ENS, for lead time T+78-T+90. Because of the sophisticated post-processing undertaken, the relationship between point rainfall and raw ENS products can vary a lot, according to the amount and range of forecast rainfall totals, and the range of forecast gridbox-weather-types (which all depend also on lead time). The user-selected threshold is also of fundamental importance. However the following generalisations usually hold for map-based products, and may help forecasters to interpret the output (examples of some of these can be found on the plots below):

• Point rainfall charts are usually smoother than raw ENS charts
  
• Point rainfall output usually broadens the raw ENS distribution
  
• Values for high percentiles (e.g. 95+) are often much greater in the point rainfall
• The 50th percentile (median) is usually less in the point rainfall
• The percentile where point rainfall and raw ENS are similar tends to be around 85%
  
• Probabilities of dry (or more strictly "no measurable rain") are usually greater in the point rainfall
• Outlier extreme rainfall values in say 1 or 2 raw ENS members will usually be reduced down in the point rainfall
• Bias correction effects can very occasionally be very large - e.g. doubling or halving the rainfall
• It is more common for bias correction to reduce amounts than to increase amounts, but usually such changes are small in magnitude (e.g. up to ~20%)
  
• Point rainfall charts from successive forecasts, for a given valid time, tend to be less jumpy than equivalent raw ENS charts
  
  

Fig817.DFig8.1.7-4: 98th percentile of 12h rainfall from the raw ENS (first plot) and point rainfall (second plot)

Fig817.EFig8.1.7-5: Probabilities of >10mm from the raw ENS (first plot) and point rainfall (second plot)

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