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POINT RAINFALL
The point-rainfall is a test product, developed at ECMWF. It is not part of ECMWF's operational suite. It is released on daily basis at 13 UTC but please note that it could be delayed in certain cases. In those cases, a note will appear in the section "NEWS" to let the users know about any delay or problem with the release of this test product. Apologises for the inconveniences that this may cause.
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April, 24th: Failure of April, 22nd solved. Point-rainfall system restored to produce forecasts as usual.
April, 25th: Point-rainfall system upgrading. Possible delays on today's forecast.
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Point Rainfall Description
(work in progress)
This test product consists in a statistical post-processing of ECMWF's ensemble (Control Run + 50 Perturbed Forecasts) in order to produce probabilistic forecasts for point-rainfall.
The methodology consists in identifying weaknesses in the model and test some physically-relevant parameters to infer sub-grid variability (and biases) in rainfall totals, and thereby forecast the probability of extreme point-rainfall.
What does sub-grid variability mean?
Table 1. Example of different scenarios that lead to different types of sub-grid variability in precipitation totals. Images show example radar-derived totals for cases that correspond to each of the example scenarios | The second column of this table shows the different types of sub-grid variability in precipitation totals. Based on clear-cut observational evidence from radar-derived totals, and from physical reasoning), one notes from the outset that within global ensemble member grid boxes (18km X 18km) very different geometries of sub-grid variability in precipitation totals can be observed. In principle we could have more (or indeed less) than three, though these serve as a useful illustration. Let us assume that the number of meaningful different scenarios is given by n, and that each one is indexed with label i. Scenario (i=) 1 could be said to be "zeroth order", i.e. point totals exhibit little sub-grid variability; scenario 2 is "first order", showing strong variability in one dimension; and scenario 3 is "second order" showing strong variability in two dimensions. Accordingly, within the grid box the distribution of point totals (i.e. the pdf, or probability density function) is as follows: for scenario 1 it is roughly Gaussian, with a sharply defined peak, and so "confident"; For scenario 3 it is roughly exponential, with a high probability of small or zero totals, tapering down to a small probability of very high totals, and so not confident at all; and for scenario 2 it lies somewhere in between.
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Clearly to anticipate point totals, one must recognise and understand these types of sub-grid variability.
Those depend on what the prevailing meteorological situation (i.e. weather type) is. Global numerical models include standard parameters in their output that allow what that situation is to be easily inferred, therefore, in a real-time forecasting mode one could then naturally anticipate the type of sub-grid variability that would be likely to occur. In this way one could automatically create a single forecast, covering all points in the gridbox that showed probabilities of different totals arising.
Let us now consider the physics of rainfall generation, and specifically what types of meteorological situation/model parameters would associate with sub-grid patterns similar to scenarios 1, 2, 3 in Table 1.
When mainly large scale precipitation is forecast, there is no much sub-grid variability in the precipitation and therefore, the model grid value is representative of the point-values. On the other hand, if mainly convective precipitation and light winds are forecast, this drives to a huge sub-grid variability in the total amounts of rainfall. Indeed, scenario 3 shows a cellular pattern in the totals showing several areas/points with zeros and just few with huge totals. Therefore, if the raw ensemble predict 10 mm, it can be said that this is an average between lot of zeros and only few very high values of rainfall and won't be representative of the point-rainfall.
The above discussion use just simple scenarios but the model allows for any number of different parameters to be used and any number of scenarios to be defined. As well, we are not limited to atmospheric parameters, since fixed model parameters, like the sub-grid orography, could be also employed, as well as computed parameters from the model parameters like the "cell drift parameter" or the orographic modulation (not yet included in the current model, they are still in the research phase).
The current model take into account
- type of precipitation (mainly convective or large-scale);
- total precipitation;
- speed of steering winds (700 mbar);
- cape;
- solar radiation.
Point Rainfall Verification
3 month-verification (June, September and December 2016) has already been computed and has been shown the benefits of this method at global scale.
The project aims to compute the 6 and the 12 hourly accumulations for the post-processed rainfall. At the moment just the 12 hourly accumulations are available. In 2017 the 6 hourly accumulations will be computed as well.
Due to the fact that Europe has the biggest dataset of observations for future verification, in order to better take into account the diurnal and nocturnal cycle, the 12 hourly accumulations between 6-18 UTC and 18-6 UTC are considered (since those times define day and night time in Europe).
After this testing period, we aim to proceed with the computations until day 5 for the following intervals:
- for the 12 hourly accumulations, 00-12 UTC, 06-18 UTC, 12-00 UTC, 18-06 UTC;
- for the 6 hourly accumulations, 00-06 UTC, 06-12 UTC, 12-18 UTC, 18-24 UTC;
Point Rainfall Outputs
12 hourly accumulations - 95th percentile
Midnight Run (00 UTC)
Lead time: up to day 5
Legend:
- The values of the Point Rainfall are in mm
- VT = "Verifying Time"
| Lead - Time | ECMWF Ensemble | Point - Rainfall (TEST PRODUCT) |
|---|---|---|
| 00UTC (t+06,t+18) |
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| 00UTC (t+18,t+30) |
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| 00UTC (t+30,t+42) |
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| 00UTC (t+42,t+54) |
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| 00UTC (t+54,t+66) |
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| 00UTC (t+66,t+78) |
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| 00UTC (t+78,t+90) |
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| 00UTC (t+90,t+102) |
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| 00UTC (t+102,t+114) |
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| 00UTC (t+114,t+126) |
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