Model Uncertainty Representation in a Convection-Permitting Ensemble-SPP and SPPT in HarmonEPS

摘要

The stochastically perturbed parameterizations scheme (SPP) is here implemented and tested in HarmonEPS-the convection-permitting limited area ensemble prediction system by the international research program High Resolution Limited Area Model (HIRLAM) group. SPP introduces stochastic perturbations to values of chosen closure parameters representing efficiencies or rates of change in parameterized atmospheric (sub)processes. The impact of SPP is compared to that of the stochastically perturbed parameterization tendencies scheme (SPPT). SPP in this first version in HarmonEPS perturbs 11 parameters, active in different atmospheric processes and under various weather conditions. The main motivation for this study is the lack of variability seen in cloud products in HarmonEPS, as reported by duty forecasters. SPP in this first version is able to increase variability in a range of weather variables, including the cloud products. However, for some weather variables the root-mean-squared error of the ensemble mean is increased and the mean bias is impacted, especially in winter. This indicates that (some) parameter perturbation distributions are not optimal in the current configuration, and a further sensitivity analysis is required. SPPT resulted in a smaller increase in variability in the ensemble, but the impact was almost completely masked out when combined with perturbations of the initial state, lateral boundaries, and surface properties. An in-depth investigation into this lack of impact from SPPT is here presented through examining, among other things, accumulated tendencies from the model physics. Significance StatementSmall inaccuracies, simplifications, or errors in any part of a complex and nonlinear system like a weather model can amplify and in a short time become significant. We wanted to introduce a physically consistent way of representing these uncertainties in a model that is used in several European countries. To do this we introduce variations in a few parameters that are used in the model description, and that we know are uncertain. By doing this we were able to increase the variability of the cloud products as desired. We see this as a promising approach for capturing the possibilities of fog occurring or not in this model. Further refinements are needed before it can be used in operational weather forecasts.