Pregled bibliografske jedinice broj: 760258
Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation
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)
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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
Projekti:
HRZZ-IP-2013-11-2831 - Klima jadranske regije u njenom globalnom kontekstu (CARE) (Orlić, Mirko, HRZZ - 2013-11) ( CroRIS)
Ustanove:
Državni hidrometeorološki zavod
Citiraj ovu publikaciju:
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
