more active in the extra-tropics, while the EDA perturbations are more active in the tropics. The NCEP ensemble, the Global Ensemble Forecasting System, uses a technique known as vector breeding. Model uncertainty Model uncertainty arises due to the limitations of the forecast model. The process of representing the atmosphere in a computer model involves many simplifications such as the development of parametrisation schemes, which introduce errors into the forecast. Several techniques to represent model uncertainty have been proposed. Perturbed parameter schemes When developing a parametrisation scheme, many new parameters are introduced to represent simplified physical processes. These parameters may be very uncertain. For example, the 'entrainment coefficient' represents the turbulent mixing of dry environmental air into a convective cloud, and so represents a complex physical process using a single number. In a perturbed parameter approach, uncertain parameters in the model's parametrisation schemes are identified and their value changed between ensemble members. While in probabilistic climate modelling, such as climateprediction.net, these parameters are often held constant globally and throughout the integration, in modern numerical weather prediction it is more common to stochastically vary the value of the parameters in time and space. The degree of parameter perturbation can be guided using expert judgement, or by directly estimating the degree of parameter uncertainty for a given model. Stochastic parametrisations A traditional parametrisation scheme seeks to represent the average effect of the sub grid-scale motion (e.g. convective clouds) on the resolved scale state (e.g. the large scale temperature and wind fields). A stochastic parametrisation scheme recognises that there may be many sub-grid scale states consistent with a particular resolved scale state. Instead of predicting the most likely sub-grid scale motion, a stochastic parametrisation scheme represents one possible realisation of the sub-grid. It does this through including random numbers into the equations of motion. This samples from the probability distribution assigned to uncertain processes. Stochastic parametrisations have significantly improved the skill of weather forecasting models, and are now used in operational forecasting centres worldwide. Stochastic parametrisations were first developed at the European Centre for Medium Range Weather Forecasts. Multi model ensembles When many different forecast models are used to try to generate a forecast, the approach is termed multi-model ensemble forecasting. This method of forecasting can improve forecasts when compared to a single model-based approach. When the models within a multi-model ensemble are adjusted for their various biases, this process is known as "superensemble