University of Cambridge > Talks.cam > Centre for Atmospheric Science seminars, Chemistry Dept. > Representing Model Uncertainty in the ECMWF Convection Scheme

Representing Model Uncertainty in the ECMWF Convection Scheme

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If you have a question about this talk, please contact Dr Amanda Maycock.

Probabilistic forecasts enable users to make better informed decisions. In particular, it is important that these probabilistic forecasts are reliable, i.e. the forecast probability of an event matches the subsequent observed probability of that event taking place. In order to produce reliable probabilistic weather forecasts, it is important to account for all sources of error in atmospheric models. In the case of weather prediction, the two main sources of error are due to initial condition uncertainty and model uncertainty – this talk will focus on how to represent the latter. Two approaches are considered. A perturbed parameter approach identifies uncertain parameters in the physical parametrisation schemes and varies their values between forecasts. Stochastic parametrisation schemes are also considered, which introduce random numbers into the forecast equations to represent the effect of errors in the forecast model on the evolution of the forecast. These two techniques will be illustrated in terms of uncertainty due to the parametrisation of convection, and tested using the European Centre for Medium Range Weather Forecasts global NWP model.

This talk is part of the Centre for Atmospheric Science seminars, Chemistry Dept. series.

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