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 Figure 8.1.14.1.
Figure 8.1.14.1. 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).
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 gridboxes, 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.
So three key aspects to appreciate from the outset are:
Terminology:
There are two reasons why point rainfall and ENS rainfall distributions commonly differ:
The sub-grid variability issue was illustrated on Figure 8.1.14.1. 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 Figure 8.1.14.2).
Figure 8.1.14.2 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).
Figure 8.1.14.2. 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.
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.
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 gridboxes. 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.
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.
By giving, for example, non-zero probabilities for very large totals that had a zero probability in the raw ENS 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 ENS.
Users can type "point" into the "Add Layers" filter box to find point rainfall layers. The first release of point rainfall products into ecCharts in April 2019 came with two new layer options for map display:
In each case the output forecast values should be thought of as corresponding to any point, within the requisit gridbox, that the forecaster may require a forecast for. The lead times available are overlapping 12h periods up to T+246h, as stated above.
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 Figure 8.1.14.3. 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.
Figure 8.1.14.3. Colour fill options for a user-defined percentile (first plot, amounts in mm), and a user defined threshold (second plot, probability in %)
Figures 8.1.14.4 and 8.1.14.5 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):
Figure 8.1.14.4. 98th percentile of 12h rainfall from the raw ENS (first plot) and point rainfall (second plot)
Figure 8.1.14.5: Probabilities of >10mm from the raw ENS (first plot) and point rainfall (second plot)
Users should be aware of some known limitations of point rainfall output, which are listed below:
Future products may address items 3 and 4, and include a cdf comparison option.
From the 12UTC ensemble runs on Monday 23rd May 2022 onwards the post-processing algorithm differs in the following three ways:
Together these changes should result in post-processed forecasts that are more reliable and more skillful.
See https://confluence.ecmwf.int/display/FCST/ecPoint+output+improved for further information.
(Note: Some aspect of older material may now be out of date)
View a recording of an ecPoint webinar
Read more on New point-rainfall forecasts for flash flood prediction.
Read more on Forecasting Convective Events in late May.