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

Abstract

Abstract. 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 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 s −1 (depending on season and parameter) for daily temperature parameters and daily mean wind speed, respectively.

Three‐dimensional spatial interpolation of 2 m temperature over Norway

“…Hiebl and Frei () have divided the domain into three subregions, while Krähenmann et al () have defined eight subregions and in both cases the subregional background fields have been combined into the regional one by means of linear weighting across overlapping areas. In the article by Brinckmann et al (), the spatial domain is the whole of Europe and the interpolation procedure for temperature has been performed separately on seven regions. Frei (), Hiebl and Frei (), Krähenmann et al () and Brinckmann et al () have subjectively chosen a (fixed) number of subregions based on the climatology of the domain considered, relying on the implicit assumption that enough observations were available within each subregion.…”

Spatial interpolation of two‐metre temperature over Norway based on the combination of numerical weather prediction ensembles and in situ observations

“…(), Frei (), Hiebl and Frei () and Brinckmann et al . () to obtain daily temperature fields across Europe. As pointed out by Amezcua and Leeuwen (), the analysis step of the Kalman filter is optimal when “(a) the distribution of the background is Gaussian, (b) state variables and observations are related via a linear operator, and (c) the observational error is of additive nature and has a Gaussian distribution”.…”

“…An adaptation of OI to statistical interpolation aimed at the production of observational temperature datasets has been described by Uboldi et al (2008) and it has been subsequently used for daily mean temperature over Norway (Lussana et al, 2018b). Equivalent methodologies based on Kriging have been used by Krähenmann et al (2011), Frei (2014, Hiebl and Frei (2016) and Brinckmann et al (2016) to obtain daily temperature fields across Europe. As pointed out by Amezcua and Leeuwen (2014), the analysis step of the Kalman filter is optimal when "(a) the distribution of the background is Gaussian, (b) state variables and observations are related via a linear operator, and (c) the observational error is of additive nature and has a Gaussian distribution".…”

A New Fully Gap-Free Time Series of Land Surface Temperature from MODIS LST Data

“…Besides, processing such data can be quite time-consuming regarding computation time (especially to obtain higher spatial resolution datasets), difficult to automate (i.e., data requires a lot of "curation"), and therefore, it is not easily updated with new incoming data [14][15][16].…”

“…Regardless of the spatialization method used, unrepresentative smooth spatial patterns may occur due to the lack of dense data [11][12][13]. In the end, the obtained precision depends on the quality, representativeness and spatial distribution of the input network(s) of stations [12][13][14].…”