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1 year of global verification of 12h point rainfall products indicates that when compared to point observations, and relative to raw ensemble 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 ensemble 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 ensemble for this threshold.

Some Uses of Point Rainfall

By giving, for example, non-zero probabilities for very large totals that had a zero probability in the raw ensemble output, the Point Rainfall can provide a useful new pointer to when flash floods are possible locally. Likewise if one wants a better idea of how likely it is that a given period remains dry, the point rainfall can usually provide better guidance; indeed in convective situations the point rainfall probabilities for dry should be much better. And where a certain criteria has to be met as the basis for a warning or alert, which might not always be a large total, again the point rainfall should overall provide customers with better guidance than the raw ensemble.

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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 ensemble 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. Whilst ecPoint can increase or reduce, using a multiplying factor, the net rainfall in a given forecast from one ensemble member (i.e. bias correct), it will never convert a forecast of zero rainfall to anything other than zero. This means that if all ensemble 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.
8. Locally ecPoint can give smaller peak totals than the raw medium range ensemble products.
9. ecPoint depends upon how well the medium range ensemble itself forecasts the evolution and location of precipitation.
10. Low dewpoint depression at the surface can favour under-prediction.

Future products may address items 3 and 4, and include a cdf comparison option.

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