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Basic
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ensemble Products
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Basic products display only the raw ENS 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 HRES and the Control, can be displayed individually, but for ensemble control. For ease of visual comparison these are displayed together as "Postage Stamps” (or "Postage Stamps Maps or Charts")shown 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 terms 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.
Clustering
In order to compress the amount of information being produced by the ensemble and highlight the most predictable parts, individual ensemble members that are "similar" according to some measure (or norm) can be grouped together. This process is known as clustering.
Fig8.1.1.-1: A example of postage stamps showing the PMSL forecasts from HRES, ENS CTRLensemble control, and all 50 ENS ensemble members, data time 00UTC 19 May 2017, verifying time T+120hr at 00UTC 24 May 2017. Some DT 12UTC 15 October 2023, VT T+144hr at 12UTC 21 October 2023. Some large differences in the pressure pattern can be seen on individual ENS ensemble members. Each Each member has been allocated to a cluster, shown in a different colour heading above each frame for lead-times T+120 and above only. The The representative member of each cluster is here shown by arrows. The clusters are shown in Fig8.1.1-2 and Fig8.1.21-3.
In order to compress the amount of information being produced by the ENS and highlight the most predictable parts, individual ENS 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 or the case shown in Fig8.1.1-1. The three clusters for T+144hr are in the left column. Clustering is based on 500hPa geopotential height pattern.
Fig8.1.1.2-3: Clustering for or the case shown in Fig8.1.1.-1. The three clusters for T+120hr 144hr are in the left column. Clustering is based on 500hPa geopotential height pattern.
Spaghetti diagrams
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, and the . 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, but because the spread of the ensembles is quite limited. As the forecast progress they become increasingly spread out as the forecasts progress reflecting the 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.
Fig8.1.1.3-4: Spaghetti Plot 500hPa geopotential height plotted at 560dam for DT 12UTC 26 May 2017, VT T+30hr . ENS data time 12Z 26 at 18UTC 27 May 2017. The ENS 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 Red and HRES in Bluered.
Fig8.1.1.4-5: Spaghetti Plot 500hPa geopotential height plotted at 560dam for DT 12Z 26 May 2017, VT T+144hr . ENS data time 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 Red and HRES in Bluered.
Ensemble
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mean
The ensemble mean (EM) forecast is a simple but average of the ensemble results. It is an effective product as the averaging serves as a filter to reduce or remove because averaging reduces or removes atmospheric features that differ amongst the members.
The EM ensemble mean tends to weaken gradients; all . 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 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, but the EM ensemble mean will only show a rather shallow spread out depression giving the impression of weak average winds. High High-impact events, which in the EM ensemble mean appear weak or absent, can can be easily overlookedor , or at best regarded as less predictable. The EM may show only a weak feature but it does not mean that there is no noteworthy development in any member, nor that the more developmental outcomes are themselves less probable, since all evolutions shown by ENS members are equally likely. Inspection of
It is essential to inspect the postage stamps the Postage Stamps and/or use of probabilities is therefore essential in conjunction with examining the EM.ensemble mean charts and diagrams.
The EM 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 EM ensemble mean is very similar to the ensemble control (CTRL) or HRES due to the anti- symmetry (equal positive and negative) of the initial perturbations. The EM is less suitable for wind speeds and precipitation because these exhibit skewed distributions and for these parameters the ensemble median might be more useful.
Ensemble median
The ensemble median is defined as the value of the middle ensemble member where the members have been ranked by value. Ensemble median might be more suitable than the ensemble mean for wind speeds and precipitation as these have skewed distributions.
Fig8.1.1.5-6: Ensemble Mean mean PMSL (Redred) and Spagetti Spaghetti Plot of 990hPa isobars (Greygrey) from ENS data time ensemble DT 00UTC 10 June 2017 2017 T+120 forecast verifying at 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.
Fig8.1.1.6-7: As Fig8.1.1.-5 but 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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Probabilities
The most consistent way to convey forecast uncertainty information is by the probability of the occurrence of an event. The event can be general or user-specific regarding probability of exceeding an event threshold. The event threshold may correspond to the point at which the user has to take some action to mitigate potential damage from a significant weather event. Probabilities can be:
- instantaneous (e.g. probability 10m wind speed exceeds 20m/s as a given time),
- calculated over a time interval (e.g. probability precipitation exceeds 50mm during a defined 12 hour period). This is possible because the values are themselves originally computed as values accumulated over some (shorter) time interval.
Probability of precipitation
Probability of precipitation (PoP) totals include all precipitation types (rain, snow, etc. but not hail) in mm of rainfall or rainfall equivalent falling in 6 hour or 12 hour periods using colour shading. As a rough guide 1 mm rainfall equivalent approximates to 1 cm of snowfall.
Fig8.1.1-8: Probability of precipitation chart showing probability of total precipitation (large scale precipitation plus convective precipitation) exceeding 1mm during the 6 hours preceding the validity time.
Probability of extreme gusts
Probabilities for extreme wind gusts are computed as probabilities over 24 hours because it is considered more important to know that an extreme wind gust might occur than to know the precise time.
Fig8.1.1.7-9: As Fig8.1.1.5 -7 with the probability of wind ≥10m/s. There is a higher probability (dark blue) in the area west of Ireland where the pressure gradient is uncertain although the direction is fairly certain. The ensemble mean (Fig8.1.1.5-6) would not suggest such strong winds. There is very low probability (white) south of Greenland where the gradient is generally light and the but the direction is uncertain. Chart taken from ecCharts. Light blue >25%, Blue >50%, Dark blue >75% probability.
Probability of combined events
Additionally, ecCharts can display the probability of a combination of events occurring together. For example:
- 2m temperature and total precipitation (to aid rain/snow forecasting).
- strong wind gusts gusts and heavy heavy snowfall (for assessment of drifting snow),.
- precipitation and surface temperature (to aid rain/snow forecasting),
- 10m wind speed and total precipitation.
- 10m wind speed strong winds and significant wave height (to help forecast dangerous conditions at sea).
Fig8.1.1.8-10: As Fig8.1.1.-7 with the probability of significant wave height ≥4m. Chart Chart taken from ecCharts. Yellow >25%, Orange >50% probability.
Fig8.1.1.9-11: As Fig8.1.1.-7 with the probability of significant wave height ≥4m AND wind ≥10m/s. Chart taken from ecCharts. Light blue >10%, Green >20%, Yellow >30% probability.
thresholds under user control. The probability is computed as the ratio of the number of the ensemble members in which both event conditions are met to the total number of ensemble members. The current available charts are probabilities of:
Fig8.1.1-12: Chart taken from ecCharts showing 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.
Forecast expressed in terms of intervals
Forecast intervals (e.g. “temperatures between 2°C and 5°C”, or “precipitation between 5 and 8mm/24hr”) can be used as a hybrid between categorical and probabilistic forecasts. ecCharts provide a simple way of displaying probabilities above or below thresholds and by intercomparison can give a indication of probability of a parameter lying between the thresholds. For example for maximum temperatures at Vilnius, (see Fig8.1.1.10) there -13) there is a 20% probability of being ≥20°C, and from Fig8.1.1.11 -14 there is a 25% probability of being ≤15°C. Therefore there is a 55% probability that the maximum temperature will lie between these two values. Combinations of parameters are possible (e.g. The the probability of combined events of wind gust and total snowfall is available on ecCharts as an aid to forecasting drifting of snow).
Fig8.1.1.10-13: Chart taken from ecCharts showing ENS ensemble probability of maximum 2m temperature ≥20°C during a 12hr interval (ecCharts colour bands for this scheme denote 55%-2020%-4040%-6060%-8080%-9595%-100%). There There is a 20% probability of maximum temperatures ≥20°C at Vilnius (shown in the box). The The location of Vilnius is shown by the pin.
Fig8.1.1.11-14: Chart taken from ecCharts showing ENS ensemble probability of maximum 2m temperature ≤15°C during a 12hr interval (ecCharts colour bands for this scheme denote 55%-2020%-4040%-6060%-8080%-9595%-100%). There There is a 25% probability of maximum temperatures ≤15°C temperatures ≤15°C at Vilnius (shown in the box). The location of Vilnius is shown by the pin.
Probability of Combined Events
Several charts on ecCharts are available to show the probability of combined events with thresholds under user control. The probability is computed as the ratio of the number of the ensemble members in which both event conditions are met to the total number of ensemble members. The current available charts are probabilities of:
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Fig8.1.1.12: Chart taken from ecCharts showing ENS 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.
Additional Sources of Information
(Note: In older material there may be references to issues that have subsequently been addressed)
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