Ensemble forecasting

Ensemble forecasting means producing multiple forecasts by making small alterations to the starting conditions or the forecast computer model.

How ensembles help forecasters

The ensemble forecasts give the forecaster a much better idea of what weather events may occur at a particular time.

By comparing these different forecasts the forecaster can decide how likely a particular weather event will be. If the forecasts vary a lot then the forecaster knows that there is a lot of uncertainty about what the weather will actually do, but if the forecasts are all very similar they will have more confidence in predicting a particular event.

Single forecasts - issues

A forecast is an estimate of the future state of the atmosphere. It is created by estimating the current state of the atmosphere using observations, and then calculating how this state will evolve in time using a numerical weather prediction computer model.

As the atmosphere is a chaotic system, very small errors in its initial state can lead to large errors in the forecast. This means that we can never create a perfect forecast system because we can never observe every detail of the atmosphere's initial state. Tiny errors in the initial state will be amplified, so there is always a limit to how far ahead we can predict any detail.

Ensemble forecasts - solutions

To test how these small differences in the initial conditions may affect the outcome of the forecast, an ensemble system can be used to produce many forecasts.

Instead of running just a single forecast, the computer 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. We design the ensemble forecast system so that each member should be equally likely.

The initial differences between the ensemble members are small, and consistent with uncertainties in the observations. But when we look several days ahead the forecasts can be quite different.

Medium-range ensemble forecasting

Forecast uncertainty grows rapidly as we try to predict the weather further ahead, so ensemble forecasts that focus on the uncertainty in medium-range forecasts (three-15 days) have been in use for much longer than the short-range ensemble forecasts.

We use medium-range ensemble forecasts provided by the European Centre for Medium-range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS), and we have also recently extended the range of Met Office ensemble system (MOGREPS) to produce forecasts to 15 days ahead.

Internal link iconMOGREPS

ECMWF

European Centre for Medium-Range Weather Forecasts (ECMWF) is an international organisation supported by many European states, including the UK, which specialises in numerical weather prediction for medium-range prediction.

The ECMWF EPS consists of 51 forecasts run using the ECMWF global forecast model with a horizontal resolution of around 32 km. 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 the original ECMWF analysis, to represent uncertainties in the initial state. Small random variations are also made to the forecast model, to represent uncertainties in how the forecast model represents atmospheric processes.

We use the ECMWF ensemble, along with our own model and models from other forecast centres, to assess the most likely outcome in the medium-range forecast, plus the uncertainty in that forecast. ECMWF ensemble forecasts are then processed further to generate an range of probability forecasts for our forecasters and for customers.

Internal link iconEnsemble forecasting in-depth

Probability and decision-making - using ensembles

The output from the ensemble systems allow the uncertainty of the forecast to be quantified, and the risk of a particular weather event occurring can be assessed. This can aid decision-making for those who are sensitive to the occurrence of certain weather events.

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Last updated: 24 August 2011