Numerical models and data assimilation systems have improved enormously over recent years so that today's 3-day forecast is as good as a 1-day forecast 20 years ago. Despite this, an NWP forecast looking a few days ahead can frequently be quite wrong, and even 1-day forecasts can occasionally have large errors. The reason for this lies in the chaotic nature of the atmosphere, which means that very small errors in the initial conditions can lead to large errors in the forecast, the so-called butterfly effect. This means that we can never create a perfect forecast system because we can never observe every detail of the initial state of the atmosphere. Tiny errors in the initial state will be amplified such that after a period of time the forecast becomes useless. This sensitivity varies from day to day, but typically we can forecast the main weather patterns reasonably well up to about three days ahead. Beyond that uncertainties in the forecasts can become large.

To cope with this uncertainty, we use Ensemble Forecasts. Instead of running just a single forecast, the model is run a number of times from slightly different starting conditions. The complete set of forecasts is referred to as the ensemble and individual forecasts within it as ensemble members. The initial differences between ensemble members are very small so that if we compared members with observations it would be impossible to say which members fitted the observations better. All  members are therefore equally likely to be correct, but when we look several days ahead the forecasts can be quite different. Some days the forecasts from different ensemble members are all quite similar, which gives us confidence that we can issue a reliable forecast. On other days the members can differ radically and then we have to be more cautious.

As an illustration of the sensitivity, the following charts show an example of two equally valid 4-day forecasts of the surface pressure (isobars) from an ensemble forecast. Differences at the start of the forecast, in the top row, are so small that we cannot tell which is more accurate, but the forecasts below are very different! (In reality, of course, the weather systems over the British Isles at Day 4 would probably have originated further west at the start of the forecast, but it illustrates how very similar ensemble members grow apart during the forecast.)

For operational medium-range ensemble forecasting, the Met Office makes use of the Ensemble Prediction System (EPS) run by the European Centre for Medium-Range Weather Forecasts (ECMWF). ECMWF is an international organisation supported by many European states, including the UK, and specialises in NWP for medium-range prediction. ECMWF does not issue weather forecasts itself, but distributes its products to the National Meteorological Services of its member states, including the Met Office, for use in production of weather forecasts.

As part of the THORPEX programme, the Met Office is carrying out research into the possible use of multi-model ensemble techniques for medium-range weather forecasting, with the emphasis on improving the forecasting of high-impact weather.

Back to top

The ECMWF EPS consists of 51 forecasts run twice daily using the ECMWF global forecast model with a horizontal resolution of around 80km. One member, called the control forecast, is run directly from the ECMWF analysis, our best guess at the initial state of the atmosphere. Initial conditions for the other 50 members are created by adding small "perturbations" to this analysis. These perturbations are designed to identify those regions of the atmosphere which are most likely to lead to errors in the forecast on each particular occasion. Small random variations in the model itself are also introduced to allow for some of the approximations which have to be made in how the model represents the atmosphere.

The charts below show an example of surface pressures (isobars) for all 51 members of the ECMWF ensemble for a sample 4-day forecast from November 2003. On many occasions the atmosphere is much more predictable than this, but this illustrates the level of uncertainty that regularly occurs in forecasts only a few days ahead.

More about the EPS from the ECMWF user guide

Back to top

Ensemble prediction allows the uncertainty in forecasts to be assessed quantitatively. This uncertainty can be passed on to users of the forecast in several ways.

For example, we can provide a range of possible values for a forecast parameter (such as temperature or windspeed) such that we know how confident we are that the actual value will fall within that range.  For the scientist this is very similar to putting an error bar on the forecast. The example below shows how maximum and minimum temperatures for each day can be given a range of uncertainty. The full length of each vertical line represents the 95% confidence range, while the central bar represents a 50% confidence range. The horizontal line across this bar is the mid-point of the distribution, and may be used to estimate the most likely temperature.Thus for the first night we can be 95% certain the minimum temperature will be between 8 and 13 Celsius, and 50% certain it will be between about 11 and 12 Celsius.

Alternatively we can estimate the probability of certain events happening, for example of the temperature falling below freezing or the wind speed reaching gale force.  Probability forecasts can help users to assess the risks associated with particular weather events which are important to them.

General information on uncertainty in forecasts
More examples of how uncertainty can be presented in forecasts
More about probability forecasts

Back to top

Ensemble forecasts from ECMWF are post-processed to produce a wide variety of chart displays to aid forecasters, and also high-quality probability forecasts which can be supplied to customers. Two examples illustrate how these can be used.

A daily task for our weather forecasters is to assess the most likely developments of major weather systems for several days ahead - most of our forecast products depend on them getting this right! Among the most important systems for NW Europe are Atlantic low pressure centres (cyclones). To aid the forecasters in assessing the most likely positions and movements of lows, cyclone tracks predicted by all the ensemble members are plotted on a single chart. The example below for a 3-day forecast in February 2004 shows that it is most likely that a low pressure will move northeastwards to the west of the British Isles. However for anyone interested in risks, it is also worth noting that there is a small chance that the low will pass further south over Scotland, bringing rain and wind much further south across the country. This is just one example of the types of charts used to summarise ensemble forecasts for forecasters.

The ensemble provides useful estimates of probabilities for many weather events, but these can often be further improved by statistical post-processing. Calibrated probability forecast data are generated daily for over 300 sites worldwide.  The graph below shows an example of a calibrated 5-day forecast of the relative probabilities of different temperatures at Heathrow Airport for midday on 28th February 2004.  

Current operational use of ensembles is restricted to application of the ECMWF EPS for medium-range prediction. Important uncertainty can also occur in short-range forecasts. Usually at short-range, up to 3 days ahead the general weather pattern is well forecast by a single model run, but there can still be uncertainty in the resulting fine details of the weather, for example in the amount, location or timing of rainfall. On rare occasions there can also be significant uncertainty in the large-scale weather patterns, and these occasions can be particularly important as they may be associated with severe weather developments. Research is currently being undertaken to investigate whether ensembles designed specifically for short-range use can help in quantifying the uncertainty in these areas.

Back to top