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Advances in Science and Research Contributions in Applied Meteorology and Climatology
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Volume 12, issue 1
Adv. Sci. Res., 12, 187–198, 2015
https://doi.org/10.5194/asr-12-187-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Adv. Sci. Res., 12, 187–198, 2015
https://doi.org/10.5194/asr-12-187-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  29 Jul 2015

29 Jul 2015

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

A. K. Kaiser-Weiss1, F. Kaspar1, V. Heene1, M. Borsche1, D. G. H. Tan2, P. Poli2, A. Obregon1, and H. Gregow3 A. K. Kaiser-Weiss et al.
  • 1Deutscher Wetterdienst, Frankfurter Straße 135, 63067 Offenbach, Germany
  • 2European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, RG2 9AX, UK
  • 3Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland

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.

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Wind speed measured at the German stations correlate well with reanalysis fields. Monthly means from two global reanalyses (ERA-20C, ERA-Interim) and one regional reanalysis (COSMO-REA6) were analysed and correlate well for the majority of the German stations. Thus we conclude that the monthly and seasonal anomalies recorded at these stations can be understood as representative for a spatial area comparable to the resolution of the reanalyses, at least for the recent years.
Wind speed measured at the German stations correlate well with reanalysis fields. Monthly means...
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