High-resolution daily gridded data sets of air temperature and wind speed for Europe

• Published in: Earth System Science Data
• Main Authors: S. Brinckmann, S. Krähenmann, P. Bissolli
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2016
• Subjects: Environmental sciences; GE1-350; Geology; QE1-996.5; North Pole
• Online Access: https://doi.org/10.5194/essd-8-491-2016; https://doaj.org/article/2307333cc22f43bf8df39540c6e7a37c

New high-resolution data sets for near-surface daily air temperature (minimum, maximum and mean) and daily mean wind speed for Europe (the CORDEX domain) are provided for the period 2001–2010 for the purpose of regional model validation in the framework of DecReg, a sub-project of the German MiKlip project, which aims to develop decadal climate predictions. The main input data sources are SYNOP observations, partly supplemented by station data from the ECA&D data set ( http://www.ecad.eu ). These data are quality tested to eliminate erroneous data. By spatial interpolation of these station observations, grid data in a resolution of 0.044° (≈ 5<mspace linebreak="nobreak" width="0.125em"/>km) on a rotated grid with virtual North Pole at 39.25° N, 162° W are derived. For temperature interpolation a modified version of a regression kriging method developed by Krähenmann et al.(2011) is used. At first, predictor fields of altitude, continentality and zonal mean temperature are used for a regression applied to monthly station data. The residuals of the monthly regression and the deviations of the daily data from the monthly averages are interpolated using simple kriging in a second and third step. For wind speed a new method based on the concept used for temperature was developed, involving predictor fields of exposure, roughness length, coastal distance and ERA-Interim reanalysis wind speed at 850 hPa. Interpolation uncertainty is estimated by means of the kriging variance and regression uncertainties. Furthermore, to assess the quality of the final daily grid data, cross validation is performed. Variance explained by the regression ranges from 70 to 90 % for monthly temperature and from 50 to 60 % for monthly wind speed. The resulting RMSE for the final daily grid data amounts to 1–2 K and 1–1.5 m<mspace width="0.125em" linebreak="nobreak"/>s −1 (depending on season and parameter) for daily temperature parameters and daily mean wind speed, respectively. The data sets presented in this article are ...