Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation
Само за регистроване кориснике
2015
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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 significantl...y 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.
Кључне речи:
GSOD / MaxEnt / MODIS LST / Spatio-temporal analysis / Daily temperature interpolation / Global space-time kriging modelИзвор:
Spatial Statistics, 2015, 14, 22-38Издавач:
- Elsevier Sci Ltd, Oxford
Финансирање / пројекти:
- Croatian Science Foundation [2831]
- Просторни, еколошки, енергетски и друштвени аспекти развоја насеља и климатске промене - међусобни утицаји (RS-MESTD-Technological Development (TD or TR)-36035)
- Истраживање климатских промена и њиховог утицаја на животну средину - праћење утицаја, адаптација и ублажавање (RS-MESTD-Integrated and Interdisciplinary Research (IIR or III)-43007)
- Улога и имплементација државног просторног плана и регионалних развојних докумената у обнови стратешког истраживања, мишљења и управљања у Србији (RS-MESTD-Integrated and Interdisciplinary Research (IIR or III)-47014)
DOI: 10.1016/j.spasta.2015.04.005
ISSN: 2211-6753
WoS: 000368912700003
Scopus: 2-s2.0-84947862977
Колекције
Институција/група
Geografski fakultetTY - JOUR AU - Kilibarda, Milan AU - Percec-Tadić, Melita AU - Hengl, Tomislav AU - Luković, Jelena AU - Bajat, Branislav PY - 2015 UR - https://gery.gef.bg.ac.rs/handle/123456789/700 AB - 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. PB - Elsevier Sci Ltd, Oxford T2 - Spatial Statistics T1 - Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation VL - 14 SP - 22 EP - 38 DO - 10.1016/j.spasta.2015.04.005 UR - https://hdl.handle.net/21.15107/rcub_gery_700 ER -
@article{ author = "Kilibarda, Milan and Percec-Tadić, Melita and Hengl, Tomislav and Luković, Jelena and Bajat, Branislav", year = "2015", abstract = "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.", publisher = "Elsevier Sci Ltd, Oxford", journal = "Spatial Statistics", title = "Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation", volume = "14", pages = "22-38", doi = "10.1016/j.spasta.2015.04.005", url = "https://hdl.handle.net/21.15107/rcub_gery_700" }
Kilibarda, M., Percec-Tadić, M., Hengl, T., Luković, J.,& Bajat, B.. (2015). Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation. in Spatial Statistics Elsevier Sci Ltd, Oxford., 14, 22-38. https://doi.org/10.1016/j.spasta.2015.04.005 https://hdl.handle.net/21.15107/rcub_gery_700
Kilibarda M, Percec-Tadić M, Hengl T, Luković J, Bajat B. Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation. in Spatial Statistics. 2015;14:22-38. doi:10.1016/j.spasta.2015.04.005 https://hdl.handle.net/21.15107/rcub_gery_700 .
Kilibarda, Milan, Percec-Tadić, Melita, Hengl, Tomislav, Luković, Jelena, Bajat, Branislav, "Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation" in Spatial Statistics, 14 (2015):22-38, https://doi.org/10.1016/j.spasta.2015.04.005 ., https://hdl.handle.net/21.15107/rcub_gery_700 .