|Title||Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation|
|Author(s)||Kilibarda, Milan; Tadić, Melita Perčec; Hengl, Tom; Luković, Jelena; Bajat, Branislav|
|Source||Spatial Statistics 14 (2015). - ISSN 2211-6753 - p. 22 - 38.|
|Department(s)||ISRIC - World Soil Information|
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Daily temperature interpolation - Global space-time kriging model - GSOD - MaxEnt - MODIS LST - Spatio-temporal analysis|
This article highlights the results of an assessment of representation and usability of global temperature station data for global spatio-temporal analysis. Datasets from the Global Surface Summary of Day (GSOD) and the European Climate Assessment & Dataset (ECA&D) were merged and consisted of 10,695 global stations for the year 2011. Three aspects of data quality were considered: (a) representation in the geographical domain, (b) representation in the feature space (based on the MaxEnt method), and (c) usability i.e. fitness of use for spatio-temporal interpolation based on cross-validation of spatio-temporal regression-kriging models. The results indicate significant clustering of meteorological stations in the combined data set in both geographical and feature space. The majority of the distribution of stations (84%) can be explained by population density and accessibility maps. Consequently, higher elevations areas and inaccessible areas that are sparsely populated are significantly under-represented. Under-representation also reflects on the results of spatio-temporal analysis. Spatio-temporal regression-kriging model of mean daily temperature using 8-day MODIS LST images, as covariate, produces average global accuracy of 2-3 °C. Prediction of temperature for polar areas and mountains is 2 times lower than for areas densely covered with meteorological stations. Balanced spatio-temporal regression models that account for station clustering are suggested.