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Keywords:

  • E-OBS;
  • climate data;
  • observation;
  • interpolation;
  • evaluation;
  • Europe

[1] Gridded data sets derived through interpolation of station data have a number of potential inaccuracies and errors. These errors can be introduced either by the propagation of errors in the station data into derived gridded data or by limitations in the ability of the interpolation method to estimate grid values from the underlying station network. Recently, Haylock et al. (2008) reported on the development of a new high-resolution gridded data set of daily climate over Europe (termed E-OBS). E-OBS is based on the largest available pan-European data set, and the interpolation methods used were chosen after careful evaluation of a number of alternatives, yet the data set will inevitably have errors and uncertainties. In this paper we assess the E-OBS data set with respect to: (1) homogeneity of the gridded data; (2) evaluation of inaccuracies arising from available network density, through comparison with existing data sets that have been developed with much denser station networks; and (3) the accuracy of the estimates of interpolation uncertainty that are provided as part of E-OBS. We find many inhomogeneities in the gridded data that are primarily caused by inhomogeneities in the underlying station data. In the comparison of existing data with E-OBS, we find that while correlations overall are high, relative differences in precipitation are large, and usually biased toward lower values in E-OBS. From the analysis of the interpolation uncertainties provided as part of E-OBS, we conclude that the interpolation standard deviation provided with the data significantly underestimates the true interpolation error when cross validated using station data, and therefore will similarly underestimate the interpolation error in the gridded E-OBS data. While E-OBS represents a valuable new resource for climate research in Europe, users of the data need to be aware of the limitations in the data set and use the data appropriately.