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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, 1st: Failure disk. Point-rainfall forecast delayed.
April, 2nd: Failure of April, 1st solved. Point-rainfall system restored to produce forecasts as usual.
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Point Rainfall Description
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 rainfall.
What does sub-grid variability mean?
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 (Images show example radar-derived totals for cases that correspond to each of the example scenarios). 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. Clearly to anticipate point totals, one must recognise and understand these types of sub-grid variability. |
We define which parameters increase the sub-grid variability and biases in the total amounts of precipitations.
At the beginning of the project we aimed to assess the sub-grid variability of the total amounts of precipitations but with further studies, it is possible to correct some biases in the model (related, for instance, with the diurnal/nocturnal rainfall cycle).
We post-process the ECMWF ensemble by using some parameters like type of precipitation (mainly convective or large-scale), wind speed, cape and solar radiation in order to asses the rainfall sub-grid variability and biases (for instance between the diurnal/nocturnal cycle). This means that you will see differences between the point-rainfall and the raw ensemble when the model grid values (i.e. the average of the infinite point values within the grid box) are not representative of the point-values.
Just an example.
When you have mainly large scale precipitation and high wind speeds, there is no much sub-grid variability in the precipitation and therefore, the model grid value is representative of the point-values and the raw ensemble and the point-rainfall plots are pretty similar.
On the other hand, if there is mainly convective precipitation and light winds, this drives to a huge sub-grid variability in the total amounts of rainfall. Indeed, a plot of point-total amounts (let say a radar image) would show several areas/points with zeros and just few with huge totals. Therefore, if the raw ensemble predict 10 mm/12h you know that this is the average of lot of zeros and few very high values of rainfall and won't be representative of the point-rainfall. Therefore, this are cases where you will see high differences between the raw ensemble and the point-rainfall plots because what we show in our maps is that even if is a small probability, there is a small chance to have very huge point total amount of rainfall which for the raw ensemble won't occur. Although, it is worth to notice the following main thing. We give the probabilities for extreme rainfall but we can't say where, within the grid-box, this extreme point amount will happen.
This is not just enhancements of the amounts of rainfall values, indeed in some cases the values of the point-rainfall, for a certain probability, can be less than the raw ensemble.
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) |
|  |
| 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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