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Let us assume here that the radar-derived totals shown are accurate, and also indicate what would have been measured locally by raingauges. Then we  consider the ENS gridbox highlighted. Within this box, whilst the gridbox average rainfall total is about 17mm, the minimum and maximum rainfall amounts are about 2mm and 60mm respectively. This implies a lot of sub-grid variability. A completely accurate ENS member forecast would predict 17mm. But clearly this of itself would give the user no idea that locally there was much more (and indeed much less) than this amount. And to cause flash floods, as were observed, probably a 17mm total, locally, would not have been sufficient. The point rainfall aims to estimate the range of totals likely within the gridbox, and indeed deliver probabilities for different point values within that gridbox (albeit without saying where the largest and smallest amounts are likely to be). In other scenarios (e.g. frontal) rainfall totals will be much more uniform across gridboxesgrid boxes, but there are also recorded instances of even larger sub-grid variability. An unusual event in southern Spain in 2018 lead to a range of 12h rainfall in one ENS gridbox from 0 to ~350mm.

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• The new product is a post-processed product, based on calibrating ENS fields, including the Control run
• It delivers probabilistic forecasts of rainfall totals for points - i.e. that would be measured by a raingauge randomly located within a model gridbox (hence the word 'point')
  
• By way of comparison the raw ENS delivers probabilities for gridbox-average rainfall (in the current ENS that means ~18x18km ~9km x 9km boxes, up to day 15)
• The term rainfall here means all precipitation types: rain, rain plus snow, snow etc., always converted into mm of rain equivalent.

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Fig8.1.7-2: Two CDF examples comparing raw grid-scale (red), post-processed bias-corrected 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.

Output formats

Output from ecPoint can in principle be for pre-defined periods of time, or instances in time. For point rainfall we currently make available overlapping 12h periods up to day 10, namely T+0-T+12, T+6-T+18,... T+234-T+246. In future we plan also to make available equivalent products for 6 hour and 24 hour intervals.

Calibration

As with any post-processing system ecPoint has to be calibrated. For this it uses short-range Control run forecasts of 12h rainfall covering one year (the "training period"), which are individually compared with rainfall observations, for the same times, within the respective gridboxesgrid boxes. The full procedure is not described here, but involves segregation according to gridbox-weather-types, which each have different sub-grid variability structures and/or different bias corrections associated. The 12h point rainfall system introduced into operations in April 2019 incorporated 214 such types. The type definitions are currently based on the following parameters: convective rainfall fraction, total 12h precipitation forecast, 700hPa wind speed, CAPE, 24h clear-sky solar radiation.

Verification

1 year of global verification of 12h point rainfall products indicates that when compared to point observations, and relative to raw ENS forecasts, the point rainfall forecasts are much more reliable, and have a much better discrimination ability. The net frequency of point observations of no (measurable) rain is much higher in reality than it is in raw ENS gridbox forecasts, whereas the net frequency in point rainfall forecasts is almost perfect, as shown by the verification reliability metric. Meanwhile large totals, such as 50mm/12h, are much better delineated by the point rainfall; using the ROC area metric day 10 forecasts in the point rainfall are as good as day 1-2 forecasts from the raw ENS for this threshold.

Some Uses of Point Rainfall

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Fig8.1.7-3: Colour fill options for a user-defined percentile (first plot, amounts in mm), and a user defined threshold (second plot, probability in %)

Fig 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):

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