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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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