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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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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 equally 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 Fig8.1.7-2).

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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.

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In each case the output forecast values should be thought of as corresponding to any point, within the requisit requisite gridbox, that the forecaster may require a forecast for. The lead times available are overlapping 12h periods up to T+246h, as stated above.

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1. Rainfall events that are themselves extreme for the world as a whole, such as those related to tropical cyclones, are unlikely to have been adequately represented in the calibration dataset, so point rainfall output can incorporate some misleading aspects - e.g. a finite probability of zero rain close to the TC track. We would recommend using the raw ENS rainfall near to TC tracks.
2. Similar to (1) above, if the rainfall characteristics of a given site are known to depend very strongly on nearby topographic/coastal features then it may be that these are not adequately captured in the calibration process. In which case local knowledge or local MOS (model output statistics) may outperform the point rainfall.
3. In a situation of large-scale (i.e. non-convective) rainfall over unresolved (sub-grid) orography, when cloud level winds are not light, users can reasonably expect the higher values in the point rainfall distribution to be on the upwind side, and the smaller values on the downwind side of any topographic barrier. However the magnitude of orographic enhancement, and potentially also the magnitude of the rain shadow effect, may be substantially under-estimated. "Spill over" of rainfall onto the lee side may also complicate the picture. Note also that the point rainfall probabilities ordinarily denote what is a spatially random draw from a gridbox; here we are advising that user experience/knowledge may be able to preferentially locate the smaller and larger values. This is something which would not be appropriate or possible in a situation of e.g. convection over an inland plain. Whilst biases in large scale rainfall over mountains may not be well handled, there is evidence that large biases in convective rainfall over mountains can sometimes be helpfully corrected for in the point rainfall.
4. Diurnal cycle errors in convection are not currently catered for in the point rainfall.
5. Calibration for very small totals is dependant on measurable rain ("trace" in observation records is counted as zero during the calibration). So if one wanted to count small (unmeasurable) amounts of rain as not dry, then using ecCharts to display "probability = 0mm" for the point rainfall would mostly give the user an overestimate of the probability of dry weather.
6. Whllst Whilst ecPoint can increase or reduce, using a multiplying factor, the net rainfall in a given forecast from one ENS member (i.e. bias correct), it will never convert a forecast of zero rainfall to anything other than zero. This means that if all ENS members have zero rain in a given 12h period, the point rainfall will also show a 100% chance of zero rain in that period.
7. Calibration data for ecPoint comes from land sites only, so strictly speaking forecasts might not be as valid for sea areas (particularly for surface-based convection). Nonetheless experience suggests that we do not generate unrealistic-looking output over sea areas.

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