Using ensemble forecasts in decision-making
Ensemble prediction allows the uncertainty in forecasts to be assessed quantitatively.
Ensemble prediction allows the uncertainty in forecasts to be assessed quantitatively, and attaching numbers to the confidence or uncertainty can allow the user to assess the risks more accurately.
Ensemble forecasts contain a huge amount of information. Using the 12 forecast members shown on the right, at over a day ahead we can be confident that there will be showers and bands of heavy rain around the UK, but there is considerable uncertainty about the location and extent of the heavy rain (shown in the yellow and orange colours).
How we use the ensembles to help decision-making
Our forecasters often like to see the individual forecasts, but for other users we need to find efficient ways to summarise the information. One way is using probability forecasts.
To make best use of a probability forecasts, users must choose a probability threshold which gives the correct balance of alerts and false alarms for their particular application.
Probability forecasts can be used in two main ways:
For a specific weather element, such as temperature or wind speed, a range of values can be provided, along with a measure of how confident we are that the actual value will fall within that range.
Figure 1 shows the range of uncertainty in temperature at a specific location, plus some indication of the most probable values. At each forecast time a range of possible values are plotted, along with an estimate of the probability that the temperature will fall within that range.
Figure 1: Possible temperature values with associated levels of confidence.
A probability forecast can give a percentage of how likely a defined event is to occur, which can help users to assess the risks associated with particular weather events to which they are sensitive.
Ensembles are designed to estimate these probabilities by sampling the range of possible forecast outcomes. The probability of a particular event occurring is estimated by counting the proportion of ensemble members which forecast that event to occur. So if six out of the 24 members predict more than 5 mm of rain at a specified location in a defined period, we would estimate there to be a 1-in-4, or 25%, chance of the event happening.
Figure 2 is an illustration of a probability forecast. The darker the blue becomes, the greater the probability of the rainfall exceeding 5 mm in six hours. For additional information, the contour lines show the pressure at mean sea level predicted by averaging all the ensemble members. This gives an indication of the weather system producing the risk.
Figure 2: A chart showing the spatial variation in the probability of the 24 hour rainfall exceeding 10 mm.
If the probability of an event occurring is 10%, this means that the event will only occur on one occasion in every 10 (or equivalently 10 in 100). Therefore, on the other nine out of 10 occasions the event will not occur. This means that we can never say whether a single probability forecast is right or wrong. We can only measure how good our probability forecasts are by looking at a large set of forecasts. Then we can group all the 10% forecasts together and check that the event occurred on one in 10 of these occasions.
Although they give a useful guide, ensembles cannot provide a perfect representation of probability. By reviewing past performance we can use statistics to calibrate the forecast and give improved probability forecasts.