Quantifying the uncertainties in weather prediction and estimating the risks of particular weather events.
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 (NWP) computer model. As the atmosphere is a chaotic system, very small errors in the initial state are amplified and can lead to large errors in the forecast. This effect also limits how far ahead we can predict any detail.
An ensemble forecasting system samples the uncertainty inherent in weather prediction to provide more information about possible future weather conditions. Rather than producing a single forecast with an NWP model, multiple forecasts are produced by making small alterations either to the starting conditions or to the forecast model itself, or both.
We design the ensemble forecast system so that each member should be equally likely, so that the ensemble can be used to forecast the probabilities of different possible outcomes. Where all the forecasts in an ensemble are similar we can be more confident in the forecast; where they differ we must take more account of uncertainty.
Two ensemble forecasting systems are used at the Met Office: the Met Office Global and Regional Ensemble Prediction Systems (MOGREPS) is used for short-range prediction, while the ECMWF ensemble prediction system is used for the medium range (3-15 days ahead).
As well as researching and running ensemble forecasts, the ensemble forecasting team provides advice on how to communicate uncertainty in forecasts to customers and to the public, and provides a consultancy service to Met Office customers to understand how decision-making can be improved using ensemble forecast information.
To develop ensemble prediction systems, improving the representation of uncertainties in both the initial conditions and the forecast models.
To continue to develop a range of probabilistic forecast products, particularly to improve forecasts of the risk of high-impact weather events.
To support customers in using uncertainty information to manage weather-related risks effectively.
In collaboration with the data assimilation team, we are working on the use of ensemble methods in a future operational data assimilation system.
New 'stochastic physics' schemes are being developed, in order to get a better representation of how small errors in the numerical models affect uncertainties in the forecasts.
Research on the benefits of combining ensemble forecasts from several different models is being undertaken as part of the international TIGGE project.
In conjunction with the Environment Agency, we are extending ensemble forecasting to predict the risks of coastal storm surges and hydrological floods.