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1. In the main charts page, click on ecCharts.
2. On the ecChart, select the "Layers" tab.
3. On the Layers drop down, select (via "Add Layers") the "Total precipitation shift of tails (SOT) index" and "sub-seasonal range: EFI for total precipitation".
4. On the ecChart header, select "Views".
5. Via the "Views" drop down, select any required windows for display. 
6. On the "Views" drop down, select the Meteogram window.
7. If necessary choose the desired CDF option(s), for sub-seasonal ranges, using the "More ..." button
8. Display of product 

Each CDF plot displays the latest forecast along with the Sub-seasonal range CDF plots show the latest (red) and previous (coloured) ensemble forecasts valid for the same 7-day period.  The corresponding sub-seasonal range model climate (SUBS-M-Climate) corresponding to it, and also all previous sub-seasonal range ensemble forecasts valid for the same 7-day period. The following percentiles are is shown in black.  The percentiles used to draw both SUBS-M-Climate and ensemble forecast CDFs are:   0 (minimum), 1, 2, 5, 10, 25, 50 (median), 75, 90, 95, 98, 99 and 100 (maximum). For the   When forecasts , which have far fewer realisations than the SUBS-M-Climate, sub-seasonal range model climate then:

• percentiles 0, 1 and 2

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• are assigned the same value if this occurs in the CDF lower tail. 
• percentiles 98, 99 and 100 are assigned same value. if this occurs in the CDF upper tail. 
  

Fig8.2.4-2: The EFI and SOT for 2-metre weekly mean temperature anomalies, and CDF plots of temperature and precipitation anomalies for one site (see green pin). On the CDF widget the black curve represents the SUBS-M-Climate and coloured curves represent the different sub-seasonal range forecasts valid for the same 7-day period.

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For both precipitation and temperature, zero on the x-axis (and the thicker vertical gridline) simply corresponds to SUBS-M-Climate mean values (for the location, the time of year and the lead time displayed), because of course "anomaly" computations use these mean values as their reference points. This statement is true for all of the curves.    However, for different lead times (i.e. the different coloured curves) the absolute value that is the mean will vary varies a little bit ( due to model drift and under-sampling). In spite of such variations it is still reasonable, helpful and recommended to inter-compare the single black .   Strictly, the SUBS-M-Climate curve with all the coloured curves (even if this is only strictly (black) is valid for the same lead time that it represents - i.e. the red curve).of the latest ensemble (red).  Despite this, all curves from earlier ensembles (coloured) should be compared with the SUBS-M-Climate curve (black) as well.    

Note, incidentally, that the SUBS-M-Climate, as used here and on sub-seasonal range meteograms, is now based on re-forecasts initialised from ERA5 data; this is higher quality output, and has greater compatibility with actual forecasts, than was the case previously when the re-forecasts were initialised from ERA-Interim data (i.e. before model cycle 46r1 was introduced in June 2019).

There are many different options for how to construct CDF plots - i.e. axis limits etc. All have advantages and disadvantages. The structure ECMWF uses, described below, arose following several iterations, and arguably provides an optimal compromise.

For precipitation, the plot design is such that the :

The CDF is drawn for the given valid period and the lead time of the latest forecast (red).

The CDF x-axis always starts from the the SUBS-M-Climate minimum (black) , minimum for the given valid dates, for the lead time of the latest forecast (red) . So the black curve should always begin from the graph's lower left corner (Fig8.2.4-3). Note that over   Over the vast majority of the world this will equate to zero be 0mm precipitation in the 7-day period; it is of course impossible to get less than zero0mm. As the

The SUBS-M-Climate is a function of lead time.  So the other forecasts shown for different lead times (not redcoloured), which are referenced to (slightly) different SUBS-M-Climates to calculate anomalies, .  They may not have the same "zero" 0mm starting point on the diagram. 

On Fig8.2.4-3 for example , the end of the dashed purple curve, corresponding to the T+504-672h forecast, probably also corresponds to zero 0mm rain, but lies left of the plot area shown (and so is not visible). Equally   Equally, such a curve could end just to the right of the lower left point of the graph, but could still correspond to zero rain in absolute terms. It all depends on how the SUBS-M-Climate for the given lead time compares with the indicated SUBS-M-Climate (black curve).  In some very wet locations the SUBS-M-Climate CDF may have never been 0 0mm (i.e. no completely dry weeks).  In this case the x-axis would start from a value that in the SUBS-M-Climate (black) does not correspond to zero 0mm rain in absolute terms as shown on Fig8.2.4-3.

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Fig8.2.4-3: CDF plots for 7-day total precipitation anomalies. The plot design caters for two scenarios: .

• (a) - which is very common - used when

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• the SUBS-M-Climate minimum (black) = 0mm

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• (bottom left corner = 0mm)

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• (b) - which is very rare - used when

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• the SUBS-M-Climate minimum is >0mm. 

Note: on (a) the purple curve does not start from 0 On (a) the purple curve, for example, does not start from "0" (the point where x- and y-axis intersect) because of lead time dependence of the M-climate. A similar situation in which in which a curve "disappears" off the left of the plot could occur in case (b), but has not done so on this exampleon this example.

For temperature, the x-axis starts from the overall minimum encountered within all the displayed CDFs (SUBS-M-Climate and ensemble forecasts).

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Fig8.2.4-6:  ecChart of sub-seasonal Range EFI and SOT (quantile 10) for weekly T2m anomaly, from DTDT:00UTC 25 Jul 2019,  VTVT:week ending 29 Aug 2019.  The chart shows that the distribution of possible mean 2m temperatures is overall close to the SUBS-M-Climate distribution.  On this occasion, implying that the actual model ensemble forecast on this occasion is unable to add much to a (model-free) forecast that purely reflects climatological probabilities.  The CDF diagram at Fig8.2.4-3(b) would be similar to that for Nizhniy Novgorod.to a forecast that purely reflects climatological probabilities.  At Nizhniy Novgorod the trace from the most recent ensemble forecast would lie close to the climatology trace.

The user should Note that whilst negative SOT values can be computed, and are displayed in meteogram format (as above), and are illustrated below, we advise the user to generally focus on SOT values that are ≥0.8.

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It should be remembered that only upper tail SOT may be derived from rainfall CDFs as clearly .  Because there are no rainfall totals below 0mm and no anomaly in the sub-seasonal range model climatology can exist below this value.

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