Spatial gridding of daily maximum and minimum temperatures in Europe

Abstract

We developed an operationally applicable landonly daily high-resolution (5 km 9 5 km) gridding method for station observations of minimum and maximum 2 m temperature (T min /T max ) for Europe (WMO region VI). The method involves two major steps: (1) the generation of climatological T min /T max maps for each month of the year using block regression kriging, which considers the spatial variation explained by applied predictors; and (2) interpolation of transformed daily anomalies using block kriging, and combination of the resulting anomaly maps with climatological maps. To account for heterogeneous climatic conditions in the estimation of the statistical parameters, these steps were applied independently in overlapping climatic subregions, followed by an additional spatial merging step. Uncertainties in the gridded maps and the derived error maps were quantified: (a) by cross-validation; and (b) comparison with the T min /T max maps estimated in two regions having very dense temperature observation networks. The main advantages of the method are the high quality of the daily maps of T min /T max , the calculation of daily error maps and computational efficiency.

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

“…The separation roughly follows the climate classification after Köppen and Geiger (e.g., Sanderson, 1999), and thus relatively homogeneous conditions for temperature are expected within each region. Compared to Krähenmann et al (2011), slight modifications were made in the partitioning of the regions in order to adapt them to the DecReg domain dealt with in this work.…”

“…These so-called predictor fields explain a major part of the spatial variation of monthly temperatures (Krähenmann et al, 2011). In order to receive a regionspecific regression model, usually only data from stations within the core region (weight one; compare Fig.…”

“…Another modification compared to Krähenmann et al (2011) is the use of station monthly means instead of climatological monthly values as input data for the monthly temperature analysis. In our work monthly hindcast periods are considered instead of current days (as in Krähenmann et al, 2011); therefore the values of the entire month are available.…”

“…By spatial interpolation of these station observations, grid data in a resolution of 0.044 • (≈ 5 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.…”

High-resolution grids of hourly meteorological variables for Germany

“…Over regions of complex terrain and sparse data coverage, gridding is most successful when the applied methodology captures terrain effects (Daly 2006;Frei 2014). Co-variables derived from elevation data are widely used to account for topographic effects (Stahl et al 2006;Kraehenmann et al 2011. Frick et al (2014 generated the HYRAS dataset which comprises gridded daily air temperature and relative humidity for Germany and the hydrological catchment areas of the rivers Rhine, Elbe, Oder and part of the Danube at a horizontal resolution of 5 km, covering the period .…”