Overview of applied statistical interpolation methods in SPARTACUS, a spatial dataset of the surface climate in Austria

Spatially comprehensive information of climate variables that extends over many decades is essential for various modelling, reanalysis, monitoring, and planning applications, like snow cover modelling, drought monitoring, model evaluation or environmental planning. For the territory of Austria, a gridded dataset called SPARTACUS is produced by the national weather service of Austria, by spatially analysing daily station observations. This operational climate-monitoring dataset has a spatial resolution of 1 km to 1 km and provides data since 1961 with a temporal resolution of one day and further temporal aggregates (month, season and year).

The current operational version covers the parameters minimum and maximum air temperature, precipitation amount and sunshine duration. Parameter-specific methods are used to interpolate the surface in-situ observations. An overview of the applied geostatistical interpolation methods including key results from evaluation will be presented, focusing on temperature and precipitation. Moreover, requirements on input station data, like constant station networks over time, will be specified. Limitations in the analysis of such a gridded observation dataset, due to for example conditional biases, will be pointed out as well.

A gridded dataset of air humidity is currently under development. Such a dataset is highly relevant, for example for the investigation of droughts or snowmaking potential. The actual spatial analysis is performed for the parameter of dew-point depression, which allows adapting the interpolation method previously used for air temperature. Preliminary findings of the current approach including results from the evaluation will be shown. They might be relevant for similar projects in other regions with complex topography.