On the Impact of NWP Model Background on Very High–Resolution Analyses in Complex Terrain

Abstract

Abstract The capability to accurately analyze the spatial distribution of temperature and wind at very high spatial (2.5–1 km) and temporal (60–5 min) resolutions is of interest in many modern techniques (e.g., nowcasting and statistical downscaling). In addition to observational data, the generation of such analyses requires background information to adequately resolve nonstatic, small-scale phenomena. Numerical weather prediction (NWP) models are of continuously increasing skill and are more capable of providing valuable information on convection-resolving scales. The present paper discusses the impact of two operational NWP models on hourly 2-m temperature and 10-m wind analyses as created by the Integrated Nowcasting through Comprehensive Analysis (INCA) system, which includes a topographic downscaling procedure. The NWP models used for this study are a revised version of ARPEGE–ALADIN (ALARO; 4.8-km resolution) and the Applications of Research to Operations at Mesoscale (AROME; 2.5-km resolution). Based on a case study and a longer-term validation, it is shown that, generally, the finer the grid spacing of the background model and the higher the resolution of the target grid in the downscaling procedure, the slightly more accurate is the analysis. This is especially true for wind analyses in mountainous regions, where a realistic simulation of topographic effects is crucial. In the case of 2-m temperature, the impact is less pronounced, but the topographic downscaling at very high resolution at least adds detail in complex terrain. However, in the vicinity of station observations, the analysis algorithm is capable of spatially adjusting the larger biases found in the ALARO model while having a lesser effect on the downscaled AROME model.