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Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution
- Source :
- Journal of Geophysical Research: Atmospheres 119 (2014) 5, Journal of Geophysical Research-Atmospheres, Journal of Geophysical Research: Atmospheres, 119(5), 2294-2313
- Publication Year :
- 2014
-
Abstract
- Combined Global Surface Summary of Day and European Climate Assessment and Dataset daily meteorological data sets (around 9000 stations) were used to build spatio-temporal geostatistical models and predict daily air temperature at ground resolution of 1km for the global land mass. Predictions in space and time were made for the mean, maximum, and minimum temperatures using spatio-temporal regression-kriging with a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) 8 day images, topographic layers (digital elevation model and topographic wetness index), and a geometric temperature trend as covariates. The accuracy of predicting daily temperatures was assessed using leave-one-out cross validation. To account for geographical point clustering of station data and get a more representative cross-validation accuracy, predicted values were aggregated to blocks of land of size 500x500km. Results show that the average accuracy for predicting mean, maximum, and minimum daily temperatures is root-mean-square error (RMSE) =2 degrees C for areas densely covered with stations and between 2 degrees C and 4 degrees C for areas with lower station density. The lowest prediction accuracy was observed at high altitudes (>1000m) and in Antarctica with an RMSE around 6 degrees C. The model and predictions were built for the year 2011 only, but the same methodology could be extended for the whole range of the MODIS land surface temperature images (2001 to today), i.e., to produce global archives of daily temperatures (a next-generation repository) and to feed various global environmental models. Key Points Global spatio-temporal regression-kriging daily temperature interpolation Fitting of global spatio-temporal models for the mean, maximum, and minimum temperatures Time series of MODIS 8 day images as explanatory variables in regression part
- Subjects :
- space-time climate
Atmospheric Science
Topographic Wetness Index
air temperature
global
spatio-temporal prediction
air-temperature
010504 meteorology & atmospheric sciences
Meteorology
daily climate extremes
0207 environmental engineering
data set
part ii
02 engineering and technology
Geostatistics
surface temperature
01 natural sciences
Cross-validation
European Climate Assessment and Dataset
Multivariate interpolation
MODIS LST
Earth and Planetary Sciences (miscellaneous)
Range (statistics)
spatio-temporal kriging
geostatistics
020701 environmental engineering
Digital elevation model
0105 earth and related environmental sciences
spatio-temporal interpolation
variability
daily air temperature
Bodemgeografie en Landschap
Geophysics
13. Climate action
Space and Planetary Science
Climatology
Soil Geography and Landscape
Environmental science
daily precipitation
ICSU World Data Centre for Soils
Moderate-resolution imaging spectroradiometer
spatial interpolation
ISRIC - World Soil Information
Subjects
Details
- Language :
- English
- ISSN :
- 2169897X
- Database :
- OpenAIRE
- Journal :
- Journal of Geophysical Research: Atmospheres 119 (2014) 5, Journal of Geophysical Research-Atmospheres, Journal of Geophysical Research: Atmospheres, 119(5), 2294-2313
- Accession number :
- edsair.doi.dedup.....9010f69399f419a45d380582cee07674