Ensemble sensitivity and sampling error correction evaluated using a convective-scale 1000 member ensemble
Current regional forecasting systems particularly aim at the forecast of convective events and related hazards. Most weather centers use high-resolution ensemble forecasts that resolve convection explicitly. Nevertheless, sampling errors, as well as fast error growth on these scales, lead to a low predictability. Improving forecasts, therefore, requires a frequent cycling with observations that are dense in space and time. The modern regional observing system provides various observations that are not exploited yet but applicable for convective-scale data assimilation. However, little knowledge exists on what types of observations are most beneficial on which spatial and temporal scales and where to put efforts in the assimilation of currently unexploited observations.
For this reason, the HErZ research group at LMU investigates the potential impact of observations with a focus on the convective-scale ensemble data assimilation. For this purpose, we conducted the first convective-scale 1000 member ensemble simulation over Germany. Several forecasts are investigated using ensemble sensitivity analysis. The resulting sensitivities are used to quantify sampling errors on convective-scales and to evaluate a simple and efficient sampling error correction method. Furthermore, all sensitivities are used to estimate the potential impact of various observations on different scales.
Mr. Tobias Marcel Necker LMU Munich