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Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution (auxiliary data sets)

Authors: Milan Kilibarda, Tomislav Hengl , Gerard Heuvelink , Benedikt Graeler , Edzer Pebesma , Melita Percec Tadic , Bajat Branislav

Paper highlights:

Around 9000 stations from merged GSOD and ECA&D daily meteorological data sets were used to build spatio-temporal geostatistical models and predict daily air temperature at ground resolution of 1 km for the global land mass. Predictions were made for the mean, maximum and minimum temperature using spatio-temporal regression-kriging with a time series of MODIS 8 day images, topographic layers (DEM and TWI) and a geometrical temperature trend as covariates. The model and predictions were built for the year 2011 only, but the same methodology can be extended for the whole range of the MODIS LST images (2001-today). The accuracy of predicting daily temperatures has been assessed using leave-one-out cross-validation; the standard approach is extended with block approach. The values were aggregated for blocks of land of size 500x500 km to account for geographical point clustering of station data. All computations were implemented in the R environment for statistical computing by combining functionality of the gstat package (geostatistical modelling), rgdal and raster packages (raster data loading and analysis), and snowfall package (cluster computing). The results show that the average accuracy for predicting mean, maximum and minimum daily temperatures is RMSE=+/-2oC for areas densely covered with stations, and between +/-2oC and +/-4oC for areas with lower station density. The lowest prediction accuracy was observed in highlands (>1000 m) and in Antarctica with a RMSE around 6oC. This automated geostatistical framework could be used to produce global archives of daily temperatures (new generation WorldClim repository) and to feed various global environmental models.

http://onlinelibrary.wiley.com/doi/10.1002/2013JD020803/abstract (Journal of Geophysical Research: Atmospheres)

Full article PDF: http://onlinelibrary.wiley.com/doi/10.1002/2013JD020803/pdf 

Explore interactive map:

Space-time regression kriging of mean daily temperature observations on MODIS LST 8 day images, topographic layers (DEM and TWI) and a geometrical temperature trend; Cross-validation results (8MB htm file!)

 

Map of the validation errors (RMSE) averaged per counties.

Map of the validation errors (RMSE) averaged per year for each station, calculated by using GHCN-M stations which were not used for model and prediction.

 

Map of mean daily temperature for 01/01/2013 and 02/01/2013, interpolated by using spatio-temporal regression kriging on the GSOD and ECA\&D stations observation. The maps are zipped GeoTIFFs in Sinusoidal projection. Download the sample maps:

MET20110101_1km.tif.gz , MET20110102_1km.tif.gz

GHCN-Daily, space-time regression kriging of maximum daily temperature observations on MODIS LST 8 day images, topographic layers (DEM and TWI) and a geometrical temperature trend;shows results per station comparing observations and cross-validation prediction obtained by using 35 neighbours without any observation from selected station. Cross-validation results (8.5 MB htm file! 13,395 stations it can take few mins)

GHCND CV

 

 

 

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