کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10508470 | 948487 | 2015 | 17 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation
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موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
علوم زمین و سیاره ای (عمومی)
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Spatial Statistics - Volume 14, Part A, November 2015, Pages 22-38
Journal: Spatial Statistics - Volume 14, Part A, November 2015, Pages 22-38
نویسندگان
Milan Kilibarda, Melita PerÄec TadiÄ, Tomislav Hengl, Jelena LukoviÄ, Branislav Bajat,