LAUSR

Deterministic numerical weather prediction (NWP) models and ensemble NWP models are routinely run worldwide to assist weather forecasting. Deterministic forecasts are capable of capturing more detailed spatial features, while ensemble forecasts, often with a coarser resolution, have the ability to predict uncertainty in future conditions. A comparative understanding of the performance of these two types of forecasts is valuable for both users of NWP products and model developers. Past published comparisons tended to be limited in scope, for example, for only specific locations and weather events, and involving only raw forecasts. In this study, we conduct a comprehensive comparison of the performance of a deterministic model and an ensemble model of the Australian Bureau of Meteorology in forecasting daily precipitation across Australia over a period of 3 years. The deterministic model has a horizontal grid spacing of approximately 25 km, and the ensemble model 60 km. Despite the coarser resolution, the ensemble forecasts are found to be superior by a number of measures, including correlation, accuracy and reliability. This finding holds true for both raw forecasts from the NWP models and forecasts post-processed using the recently developed seasonally coherent calibration (SCC) model. Post-processing is shown to greatly improve the forecasts from both models; however, the improvement is greater for the deterministic model, narrowing the performance gap between the two models. This study adds strong evidence to the general notion that coarser-resolution ensemble NWP forecasts perform better than deterministic forecasts.