Page History

...

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.

...

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.

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.

Fig8.1.1-8: 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).

Fig8.1.1-9: As Fig8.1.1-7 with the probability of significant wave height ≥4m. Chart  Chart taken from ecCharts.  Yellow >25%, Orange >50% probability.

Fig8.1.1-10: 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:

Image Added

Fig8.1.1-11: 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 Fig8.1.1-1012) there  there is a 20% probability of being ≥20°C, and from Fig8.1.1-10 13 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).

Fig8.1.1-1112: 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.

Fig8.1.1-1213: 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:

...

Image Removed

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

...