Added Value Of Regional Reanalyses For Climatological Applications

Regional reanalyses constitute valuable new data sources for climatological applications by providing consistent meteorological parameter fields commonly requested, e.g., wind speed, solar radiation, temperature and precipitation. Within the European project Uncertainties in Ensembles of Regional ReAnalyses (UERRA) three different numerical weather prediction (NWP) models have been employed to generate different European regional reanalyses and subsequent surface reanalysis products. The uncertainties of the individual reanalysis products and of the combined UERRA multi-model ensemble are investigated by comparing against observations. Here, we provide guidance on the meteorological parameters and spatial-temporal scales where regional reanalyses add value to global reanalyses. The reanalysis fields are compared to station measurements and derived gridded fields, as well as satellite data. In general, reanalyses are especially valuable in data sparse areas, where the NWP models are superior in transporting information compared to the traditional gridding procedures based on station observations. For wind speed at heights relevant for wind energy, where little conventional observations exist, regional reanalyses can provide higher resolution horizontally, vertically, and in time, adding value to global reanalyses. Solar radiation fields capture the variability in general, however, they are prone to model-dependent biases. Temperature fields were generally found to be in good agreement with station observations, with biases for the (moderately) extreme values causing potential pitfalls for threshold applications such as climate indices. Comparisons of the precipitation fields in different areas of Europe demonstrate that various reanalyses excel in different regions. The multi-model ensemble of regional reanalyses was found to provide better uncertainty estimates than an ensemble realisation from one reanalysis system alone. The freely available regional reanalyses provide a new, high resolution data source, which might be attractive for many applications, especially when conventional data are sparse or restricted by data policies.