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We present a vine copula based composite likelihood approach to model spatialdependencies, which allows to perform prediction at arbitrary locations. Thisapproach combines established methods to model (spatial) dependencies. On theone hand the geostatistical concept utilizing spatial differences between thevariable locations to model the extend of spatial dependencies is applied. Onthe other hand the flexible class of C-vine copulas is utilized to model thespatial dependency structure locally. These local C-vine copulas areparametrized jointly, exploiting an existing relationship between the copulaparameters and the respective spatial distances and elevation differences, andare combined in a composite likelihood approach. The new methodology calledspatial local C-vine composite likelihood (S-LCVCL) method benefits from thefact that it is able to capture non-Gaussian dependency structures. Thedevelopment and validation of the new methodology is illustrated using a dataset of daily mean temperatures observed at 73 observation stations spread overGermany. For validation continuous ranked probability scores are utilized.Comparison with two other approaches of spatial dependency modeling introducedin yet unpublished work of Erhardt, Czado and Schepsmeier (2014) shows apreference for the local C-vine composite likelihood approach.

Spatial composite likelihood inference using local C-vines