Comparison of regional and global reanalysis near-surface winds with station observations over Germany

Abstract

Abstract. Reanalysis near-surface wind fields from multiple reanalyses are potentially an important information source for wind energy applications. Inter-comparing reanalyses via employing independent observations can help to guide users to useful spatio-temporal scales. Here we compare the statistical properties of wind speeds observed at 210 traditional meteorological stations over Germany with the reanalyses' near-surface fields, confining the analysis to the recent years (2007 to 2010). In this period, the station time series in Germany can be expected to be mostly homogeneous. We compare with a regional reanalysis (COSMO-REA6) and two global reanalyses, ERA-Interim and ERA-20C. We show that for the majority of the stations, the Weibull parameters of the daily mean wind speed frequency distribution match remarkably well with the ones derived from the reanalysis fields. High correlations (larger than 0.9) can be found between stations and reanalysis monthly mean wind speeds all over Germany. Generally, the correlation between the higher resolved COSMO-REA6 wind fields and station observations is highest, for both assimilated and non-assimilated (i.e., independent) observations. As expected from the lower spatial resolution and reduced amount of data assimilated into ERA-20C, the correlation of monthly means decreases somewhat relative to the other reanalyses (in our investigated period of 2007 to 2010). Still, the inter-annual variability connected to the North Atlantic Oscillation (NAO) found in the reanalysis surface wind anomalies is in accordance with the anomalies recorded by the stations.We discuss some typical examples where differences are found, e.g., where the mean wind distributions differ (probably related to either height or model topography differences) and where the correlations break down (because of unresolved local topography) which applies to a minority of stations. We also identified stations with homogeneity problems in the reported station values, demonstrating how reanalyses can be applied to support quality control for the observed station data.Finally, as a demonstration of concept, we discuss how comparing feedback files of the different reanalyses can guide users to useful scales of variability.Published by Copernicus Publications.

Optimal Siting of Wind Farms in Wind Energy Dominated Power Systems

“…A comparison with actual wind speed mast measurements at relevant heights that demonstrates the suitability for the case study in this paper can be found in [15]. Moreover, daily and monthly average wind speeds are compared with wind mast measurements in [16] and general improvements to present reanalysis models, namely ERA-Interim and ERA-20C, are found. The spatial resolution is 0.055 • or 6 km and the temporal resolution of the model output is one hour or a quarter of an hour.…”

“…In general, market values are higher in the borderland of Germany. The low values in the Alps might be explained by low correlations between wind mast measurements and reanalysis model results due to the complex topography as discussed in [16]. Hence, real market factors could be higher in the Alps.…”

Evaluating the atmospheric drivers leading to the December 2014 flood in Schleswig-Holstein, Germany

“…New high-resolution reanalyses like COSMO-REA6 (Bollmeyer et al, 2015) by the Hans-Ertel-Zentrum (HErZ), based on DWD's operational forecast model COnsortium for Small-Scale MOdelling limited-area model (COSMO-LAM; Schättler et al, 2011), will improve the hindcast evaluations and serve as input for RCM runs. In a first comparison, Kaiser-Weiss et al (2015) have already shown advantages over global reanalyses for ground level wind data, especially in coastal and mountainous regions due to the improved spatial (6 × 6 km) and temporal (hourly) resolution. The same might be expected for the evaluation of (extreme) precipitation and derived indices, like R3d and API.…”

An Overview of European Efforts in Generating Climate Data Records

“…As such, they yield information that assists users in deciding which reanalysis product might be most suitable for their particular application. The CORE-CLIMAX project developed a set of procedures for comparing reanalyses and for comparing reanalyses to assimilated observations and CDRs (Gregow et al 2014b;Kaiser-Weiss et al 2015). To do so, five categories of comparisons were identified: i) descriptive product comparison, ii) comparison with third-party observation-based CDRs, iii) intercomparison between different reanalyses, iv) thematic comparison (i.e., how well can the reanalysis products be applied to understand a particular problem), and v) internal metrics comparison.…”