Page History
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Fig8.1.1-1: A example of postage stamps showing PMSL forecasts from ensemble control, and all 50 ensemble members, 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 ensemble members. 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 representative member of each cluster is here shown by arrows. The clusters are shown in Fig8.1.1-2 and Fig8.1.1-3.
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Fig8.1.1-5: Spaghetti Plot 500hPa geopotential height plotted at 560dam DT 12Z 26 May 2017, VT T+144hr 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.
Ensemble mean
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The ensemble mean (EM) forecast is a simple but average of the ensemble results. It is an effective product because averaging reduces or removes atmospheric features that differ amongst the members.
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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 due to the symmetry (equal positive and negative) of the initial perturbations.
Ensemble median
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. Ensemble median might be more suitable than the ensemble mean for wind speeds and precipitation as these have skewed distributions.
Fig8.1.1-6: Ensemble mean PMSL (red) and Spaghetti Plot of 990hPa isobars (grey) from ensemble DT 00UTC 10 June 2017 T+120 hr VT 00UTC 15 June 2017. There is a wide diversity amongst 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 ensemble members, the pressure gradients and associated winds will generally be less strong in the ensemble mean field than in the ensemble members themselves. Chart taken from ecCharts.
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Fig8.1.1-7: 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 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.
Fig8.1.1-89: As Fig8.1.1-5bwith 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 (Fig8.1.1-56) would not suggest such strong winds. There is very low probability (white) south of Greenland where the gradient is generally light 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).
Fig8.1.1-910: As Fig8.1.1-7 with the probability of significant wave height ≥4m. Chart Chart taken from ecCharts. Yellow >25%, Orange >50% probability.
Fig8.1.1-1011: 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 Fig8.1.1-1013) there there is a 20% probability of being ≥20°C, and from Fig8.1.1-10 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 probability of combined events of wind gust and total snowfall is available on ecCharts as an aid to forecasting drifting of snow).
Fig8.1.1-1113: Chart taken from ecCharts showing ensemble probability of maximum 2m temperature ≥20°C during a 12hr interval (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.
Fig8.1.1-1214: Chart taken from ecCharts showing ensemble probability of maximum 2m temperature ≤15°C during a 12hr interval (ecCharts colour bands for this scheme denote 5%-20%-40%-60%-80%-95%-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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Fig8.1.1-13: 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.
Additional Sources of Information
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