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Basic ensemble Products

Postage stamps

Basic products display only the raw ensemble forecast data, without any particular modification or post-processing.  Individually plotted

Postage stamps

Postage Stamps” (or "Postage Stamps Maps or Charts") show the individual forecast charts of MSLP and 850hPa temperature of all the ensemble members, including the ensemble control, are displayed.  For  For ease of visual comparison these are shown together as "Postage Stamps” (or "Postage Stamps Maps or Charts")all together.  They cover a limited area of the globe, normally Europe and eastern North Atlantic.  The charts are intended to be used for reference - for example (e.g. to explain the spread in terms of the synoptic developments and, in particular, the reasons for extreme weather etc).

It    It might seem attractive to identify the member which verifies best in the early part of the forecast (say at T+12) and assume this member will continue to provide the best forecast during the rest of the medium range period.  But this is not true; the performance of any member during the first 12hrs of the forecast has little relevance to its skill beyond T+48hrs in the same area.

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Fig8.1.1.1: A example of postage stamps showing the PMSL forecasts from ensemble control, and all 50 ensemble members, data time DT 00UTC 19 May 2017, verifying time VT T+120hr at 00UTC 24 May 2017.   Some  Some large differences in the pressure pattern can be seen on individual ENS ensemble members.  Each member has been allocated a cluster, shown in a different colour above each frame for lead-times T+120 and above only.  The representative member of each cluster is here shown by arrows. The clusters are shown in Fig8.1.1.2.

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In order to compress the amount of information being produced by the ENS ensemble and highlight the most predictable parts, individual ENS ensemble members that are "similar" according to some measure (or norm) can be grouped together.   This process is known as Clustering.  

Fig8.1.1.2: Clustering for the case shown in Fig8.1.1.1.  The three clusters for T+120hr are in the left column.  Clustering is based on 500hPa geopotential height pattern.  

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Spaghetti diagrams are available on ecCharts.  The charts display isolines from each ensemble member for Surface Pressure ( MSLP) , or 500hPa geopotential height, or 850hPa temperature.  The isoline values are selected by the user (e.g. 1015hPa, or 546dam, or 0°C).  The isolines , are drawn for each member, .  At short lead times the isolines are very tightly packed for forecasts at short lead times.  However, because the spread of the ensembles is quite limited.  As the forecast progress they become increasingly spread out as the forecasts progress reflecting showing the flow-dependent increase in forecast uncertainty.   Spaghetti diagrams are sensitive to gradients; in areas of weak gradient they can show a large spread of the isolines, even if the situation is highly predictable.  Conversely, in areas of strong gradient they can display a small spread of the isolines, even if there are important forecast variations.

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Fig8.1.1.3: Spaghetti Plot 500hPa geopotential height plotted at 560dam DT 12UTC 26 May 2017, VT T+30hr , ensemble DT 12Z 26 at 18UTC 27 May 2017.  The ensemble members (grey) are quite tightly packed except at about 20°W  where gradients are slack and there is some uncertainty in the trough disruption.  The control ensemble member is shown in Redred.

Fig8.1.1.4: Spaghetti Plot 500hPa geopotential height plotted at 560dam DT 12Z 26 May 2017, VT T+144hr , ensemble DT 12Z 26 May 2017.  The ENS members at 01 June 2017.  The ensemble members (grey) have become more spread out, but retain the general pattern of an upper ridge over the northwest Atlantic and another over the North Sea but there are differences in position (or speed of movement eastwards) and amplitude.  The control ensemble member is shown in Redred.

Ensemble mean and ensemble median

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The ensemble mean tends to weaken gradients.  It may show only a weak or spread-out feature but this does not imply:

• there is no noteworthy development in any, or all, of the members.
• that the more developmental outcomes are themselves less probable, since all evolutions shown by ENS ensemble members are equally likely.

All ensemble members might forecast an intense low-pressure system with gale force winds, but in different positions.  But in this case, the ensemble mean will only show a rather shallow spread out depression giving the impression of weak average winds.  High-impact events, which in the ensemble mean appear weak or absent, can be easily overlooked, or at best regarded as less predictable. 

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The ensemble mean is most suited to parameters like temperature and pressure, which usually have a rather symmetric Gaussian distribution at each point.  In the short range the ensemble mean is very similar to the ensemble control (CTRL) due to the symmetry (equal positive and negative) of the initial perturbations.

The ensemble mean is less suitable for wind speeds and precipitation because these have skewed distributions.  For these the ensemble median might be more useful.  The ensemble median is defined as the value of the middle ensemble member where the members have been ranked by value. 

Fig8.1.1.5: Ensemble Mean mean PMSL (Redred) and Spagetti Spaghetti Plot of 990hPa isobars (Greygrey) from ENS ensemble DT 00UTC 10 June 2017 T+120 forecast hr VT 00UTC 15 June 2017.  There is a wide diversity amongst ENS ensemble members in the location and shape of the depressions.  The ensemble mean depression is smooth by comparison and less deep (the inner isobar has a value of 995hPa).   Because of averaging of the ENS ensemble members, the pressure gradients and associated winds will generally be less strong in the ensemble mean field than in the ENS ensemble members themselves.  Chart taken from ecCharts.

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Fig8.1.1.6: As Fig8.1.1.5 but with the spread of mean sea level pressure (PMSL) by ensemble members (coloured - orange: high spread, blue: low spread).  Highest spread of PMSL is on the eastern side of the depression indicating greater uncertainty in the strength of a southerly wind.  There is lower spread to the west where most members suggest a fairly low pressure and higher probability of a northerly flow.  The smallest spread is near the centre of the depression indicated by the ensemble mean but wind direction is very uncertain here; it depends upon the positioning of the low in the of individual ENS ensemble members.  Chart taken from ecCharts.

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Fig8.1.1.10: Chart taken from ecCharts showing ENS ensemble probability of maximum 2m temperature ≥20°C (ecCharts colour bands for this scheme denote 5-20-40-60-80-95-100%). There is a 20% probability of maximum temperatures ≥20°C at Vilnius (shown in the box). The location of Vilnius is shown by the pin.

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Fig8.1.1.11: Chart taken from ecCharts showing ENS ensemble probability of maximum 2m temperature ≤15°C (ecCharts colour bands for this scheme denote 5-20-40-60-80-95-100%). There is a 25% probability of maximum temperatures ≤15°C at Vilnius (shown in the box).  The location of Vilnius is shown by the pin.

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Fig8.1.1.12: Chart taken from ecCharts showing ENS ensemble probability of wind gust ≥10m/s and ≥2mm/12hr (ecCharts colour bands for this scheme denote Light blue 5-35%, Blue 35-65%, Dark blue 65-95%, Purple >95%). There is a 31% probability of exceeding the thresholds at Munich (shown in the box).  The location of Munich is shown by the pin.

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