Stochastic Parameterization of Deep Convection in Short-Range Ensemble Weather Forecasts

The numerical models that are used to perform weather forecasts and to simulate the earth's climate are incapable of representing explicitly the motions on all space and time scales. Rather, those motions with short scales must be taken into account by using parameterization schemes. A model will contain a number of such schemes, each relating to a particular small-scale physical process that has been omitted. These will include turbulence in the atmospheric boundary layer, gravity waves and deep, moist convection. The convection scheme is the focus in this project since existing representations of deep convection are known to be responsible for some of the largest and most stubborn systematic errors in weather forecasting and climate modelling. Parameterization schemes have traditionally been assumed to be deterministic. Thus, the input to a scheme is taken from the current state of the local, resolved-scale flow and the output is unique for a given input. The philosophy is that the small-scale motions can be considered statistically and an estimate of their ensemble-mean effect is fed back to the large scale. In recent years, this deterministic assumption has been challenged: it may not be valid to neglect the fluctuating component of the small-scale motions, which is capable of interacting strongly with the resolved-scale flow. There is good evidence to suggest that neglect of such fluctuations is not just theoretically unsatisfactory but that it may have significant impacts on model performance. An attractive alternative is to use a stochastic-dynamic parameterization method, which aims to account for the fluctuations. The philosophy of a stochastic scheme is to feed back the effects of a particular small-scale state. The state is chosen at random based on a model for the statistics of the small-scale motions. In June 2005, a workshop on the subject was organized by the European Centre for Medium-range Weather Forecasts. In the Proceedings (p. vii) the current situation is summarized thus: 'Stochastic-Dynamic Parameterization is a relatively new concept, yet one that has potential to impact significantly on all areas of weather and climate forecasting.' Studies to date on the stochastic approach have been consistently encouraging and have tended to fall into two distinct categories. In one category, the stochastic component is treated in relatively simply, but is included as part of a full forecast system and subject to extensive testing. Examples include methods currently being investigated for inclusion in MOGREPS (Met Office Global and Regional Ensemble Prediction System), a new system for operational weather forecasting. In another category, detailed models are constructed for the stochastic component, but testing is typically rather limited and occurs in somewhat idealized configurations. An example is the stochastic convective parameterization of Plant & Craig. The key question for this project, and a major issue for the community, is whether efforts to construct detailed models of the stochastic variability are worthwhile, or whether a simple treatment might be sufficient. In order to answer that question, it is necessary first to implement a detailed, state-of-the-art stochastic parameterization into a full operational forecast system, and second to compare its performance with simpler treatments of variability. Here, the Plant & Craig scheme will be implemented as part of MOGREPS and its performance assessed in parallel with the operational system. When implementing the scheme, and to allow for appropriate comparisons, it is necessary to determine the length and time scales over which the stochastic variability is to be correlated. The project will establish these scales and their sensitivities in a generic context (i.e., these results will not be specific to the Plant & Craig scheme). This is because knowledge of the correlation scales is important for the use of any stochastic-dynamic model.