This gridded observational dataset describes the spatial distribution of daily air temperature and precipitation sum over Austria. Appropriate applications include operational climate monitoring, climate model evaluation, detection of potential climate change signals in ecological and social systems as well as quantitative process modelling, e.g. in hydrology.

Project targets

Creation and evaluation of a spatial climate dataset

  • for the parameters minimum and maximum of air temperature as well as precipitation sum
  • at a spatial resolution of 1×1 km and a temporal resolution of 1 day
  • over the territory of Austria and the period from 1961, updated till yesterday
  • keeping statistical properties spatially and temporally as consistent as possible

The selected interpolation method for temperature deals explicitly with common nonlinearities in the vertical temperature structure and with topographical, non-Euclidean imprints in the spatial representativity of station observations.

Precipitation analysis is a step-by-step procedure: (1) Spatial interpolation of mean monthly precipitation in the calendar month of the pertinent day through kriging with a set of predefined topographical predictors as external drift. (2) Calculation of relative anomalies of station measurements on the pertinent day with respect to the climate mean from step 1. (3) Spatial interpolation of the relative anomalies through an adapted version of the weighting algorithm SYMAP. (4) Multiplication of climate mean and anomaly fields.


The presented dataset (SPARTACUS) contains about 61,000 grids and is updated on a daily basis. It is available for modelling, reanalysis, monitoring and planning applications in the file formats NetCDF, RDA and PNG.

As for minimum and maximum air temperature, systematic leave-one-out cross-validation reveals an interpolation error (mean absolute error, averaged over all stations) of 1.1 and 1.0 °C, respectively. Larger errors have to be expected in unsampled inner-Alpine valleys, especially during inversion conditions. Visual inspection suggests that the dataset provides plausible results in situations with complex meso-scale temperature patterns. Small-scale effects in temperature distribution by local topography or land cover (e.g. forest, lakes) are not included.

Example temperature analysis
Example temperature: Spatial analysis of maximum temperature, 31 January 2006

As for precipitation, the accuracy of the dataset depends on interpretation. Users interpreting grid point values as point estimates must expect systematic overestimates for light and underestimates for heavy precipitation as well as substantial random errors. Grid point estimates are typically within a factor of 1.5 from in situ observations. Interpreting grid point values as area mean values, conditional biases are reduced and the magnitude of random errors is considerably smaller. In addition, the systematically underestimating measurement error in input observations with respect to actual precipitation, especially in situations with strong wind and snowfall, has to be kept in mind.

Example precipitation analysis
Example precipitation: Spatial analysis of precipitation sum, 06 April 1975

Start of project 04.2013
End of project 03.2016
Project team
Project partners

Dr. Christoph Frei, MeteoSwiss




Hiebl J., Frei C. (2016): Daily temperature grids for Austria since 1961 – concept, creation and applicability. Theoretical and Applied Climatology 124, 161–178, doi:10.1007/s00704-015-1411-4

Hiebl J., Frei C. (2017): Daily precipitation grids for Austria since 1961 – development and evaluation of a spatial dataset for hydro-climatic monitoring and modelling. Theoretical and Applied Climatology, doi:10.1007/s00704-017-2093-x

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