Journal
SPATIAL STATISTICS
Volume 14, Issue -, Pages 22-38Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.spasta.2015.04.005
Keywords
GSOD; MaxEnt; MODIS LST; Spatio-temporal analysis; Daily temperature interpolation; Global space-time kriging model
Categories
Funding
- Ministry of Education and Science of the Republic of Serbia [43007, 47014]
- Croatian Science Foundation [2831]
- [TR 36035]
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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 degrees 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. (C) 2015 Elsevier B.V. All rights reserved.
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