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Pregled bibliografske jedinice broj: 760258

Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation


Kilibarda, Milan; Perčec Tadić, Melita; Hengl, Tomislav; Luković, Jelena; Bajat, Branislav
Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation // Spatial Statistics, 14 (2015), Part A; 22-38 doi:10.1016/j.spasta.2015.04.005 (međunarodna recenzija, članak, znanstveni)


Naslov
Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation

Autori
Kilibarda, Milan ; Perčec Tadić, Melita ; Hengl, Tomislav ; Luković, Jelena ; Bajat, Branislav

Izvornik
Spatial Statistics (2211-6753) 14 (2015), Part A; 22-38

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
GSOD; MaxEnt; MODIS LST; Spatio-temporal analysis; Daily temperature interpolation; Global space–time kriging model
(GSOD; MaxEnt; MODIS LST; Spatio-temporal analysis; Daily temperature interpolation; Global space-time kriging model GSOD; MaxEnt; MODIS LST; Spatio-temporal analysis; Daily temperature interpolation; Global space–time kriging model)

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Fizika, Geologija

Napomena
Spatial and Spatio-Temporal Models for Interpolating Climatic and Meteorological Data.



POVEZANOST RADA


Projekt / tema
HRZZ-IP-2013-11-2831 - Klima jadranske regije u njenom globalnom kontekstu (Mirko Orlić, )

Ustanove
Državni hidrometeorološki zavod

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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