Added value of regional reanalysis for climatological applications

Regional reanalyses constitute valuable new data sources for climatological applications, by providing consistent meteorological parameter fields commonly requested for, e.g., wind speed, radiation, temperature and precipitation. The recent inter-comparison of European regional reanalyses and subsequent downscaling products based on three different numerical weather prediction (NWP) models within the European project Uncertainties in Ensembles of Regional ReAnalyses (UERRA) allows to characterize uncertainties. Here we provide guidance to the meteorological parameters and spatial-temporal scales where regional reanalyses add value to global reanalyses and compare 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 data 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 in 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, attractive high resolution data source, especially where conventional data are sparse or restricted by data policies.