Comparison of spatial interpolation methods for operational climatology in the Czech Republic

 

Spatial interpolation of meteorological variables represents a key component of operational climatology at the Czech Hydrometeorological Institute (CHMI). A variety of interpolation approaches are currently used, ranging from simple deterministic techniques to more advanced regression-based and geostatistical methods, implemented both in specialized software and open-source environments.

This study presents a systematic comparison of selected interpolation methods applied to the Czech Republic, including inverse distance weighting (IDW), approaches based on local linear regression (LLR, Clidata-DEM), multiple weighted linear regression (MWLR) and kriging implemented in R and Python. The tested methods differ in their treatment of topographic predictors and spatial relationships, ranging from purely distance-based approaches to regression- and terrain-informed models

The evaluation is based on a dataset of daily temperature, precipitation and snow cover measurements from approximately 200 climatological stations over a five-year period. Interpolation performance is assessed using cross-validation strategies with systematically removed stations (5–10%) and stratification across elevation zones (150–1600 m a.s.l.) and selected geographical regions. Additional independent validation is performed using observations from stations not included in the interpolation process.

The study aims to quantify the relative performance of individual methods under varying geographical and climatic conditions, with a particular focus on the added value of terrain-informed and regression-based approaches. The results provide guidance for optimizing interpolation procedures in operational climate data production and contribute to the development of high-resolution gridded climate datasets in Central Europe.

 

This work was financially supported by the Ministry of the Environment through institutional support under the Long-term Concept of the Development of a Research Organization, and by the Technology Agency of the Czech Republic within the project SS02030040 – Prediction, Evaluation and Research for Understanding National sensitivity and impacts of drought and climate change for Czechia. Further we acknowledge support from the AdAgriF project – Advanced methods of greenhouse gases emission reduction and sequestration in agriculture and forest landscape for climate change mitigation (CZ.02.01.01/00/22_008/0004635) and the ACECE project (24-14581L) – Atmospheric Circulation and weather Extremes in Central Europe and their representation in climate models, funded by the Czech Science Foundation (GA ČR).