Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution
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 spa...
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ftunivwagenin:oai:library.wur.nl:wurpubs/490419 2024-02-11T09:56:20+01:00 Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution Kilibarda, M. Hengl, T. Heuvelink, G.B.M. Graler, B. Pebesma, E. Tadic, M.P. Bajat, B. 2014 application/pdf https://research.wur.nl/en/publications/spatio-temporal-interpolation-of-daily-temperatures-for-global-la https://doi.org/10.1002/2013JD020803 en eng https://edepot.wur.nl/353380 https://research.wur.nl/en/publications/spatio-temporal-interpolation-of-daily-temperatures-for-global-la doi:10.1002/2013JD020803 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-nd/4.0/ Wageningen University & Research Journal of Geophysical Research: Atmospheres 119 (2014) 5 ISSN: 2169-897X air-temperature daily climate extremes daily precipitation data set geostatistics part ii space-time climate spatial interpolation surface temperature variability info:eu-repo/semantics/article Article/Letter to editor info:eu-repo/semantics/publishedVersion 2014 ftunivwagenin https://doi.org/10.1002/2013JD020803 2024-01-17T23:47:22Z 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 Article in Journal/Newspaper Antarc* Antarctica Wageningen UR (University & Research Centre): Digital Library Journal of Geophysical Research: Atmospheres 119 5 2294 2313 |
institution |
Open Polar |
collection |
Wageningen UR (University & Research Centre): Digital Library |
op_collection_id |
ftunivwagenin |
language |
English |
topic |
air-temperature daily climate extremes daily precipitation data set geostatistics part ii space-time climate spatial interpolation surface temperature variability |
spellingShingle |
air-temperature daily climate extremes daily precipitation data set geostatistics part ii space-time climate spatial interpolation surface temperature variability Kilibarda, M. Hengl, T. Heuvelink, G.B.M. Graler, B. Pebesma, E. Tadic, M.P. Bajat, B. Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution |
topic_facet |
air-temperature daily climate extremes daily precipitation data set geostatistics part ii space-time climate spatial interpolation surface temperature variability |
description |
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 |
format |
Article in Journal/Newspaper |
author |
Kilibarda, M. Hengl, T. Heuvelink, G.B.M. Graler, B. Pebesma, E. Tadic, M.P. Bajat, B. |
author_facet |
Kilibarda, M. Hengl, T. Heuvelink, G.B.M. Graler, B. Pebesma, E. Tadic, M.P. Bajat, B. |
author_sort |
Kilibarda, M. |
title |
Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution |
title_short |
Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution |
title_full |
Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution |
title_fullStr |
Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution |
title_full_unstemmed |
Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution |
title_sort |
spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution |
publishDate |
2014 |
url |
https://research.wur.nl/en/publications/spatio-temporal-interpolation-of-daily-temperatures-for-global-la https://doi.org/10.1002/2013JD020803 |
genre |
Antarc* Antarctica |
genre_facet |
Antarc* Antarctica |
op_source |
Journal of Geophysical Research: Atmospheres 119 (2014) 5 ISSN: 2169-897X |
op_relation |
https://edepot.wur.nl/353380 https://research.wur.nl/en/publications/spatio-temporal-interpolation-of-daily-temperatures-for-global-la doi:10.1002/2013JD020803 |
op_rights |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-nd/4.0/ Wageningen University & Research |
op_doi |
https://doi.org/10.1002/2013JD020803 |
container_title |
Journal of Geophysical Research: Atmospheres |
container_volume |
119 |
container_issue |
5 |
container_start_page |
2294 |
op_container_end_page |
2313 |
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1790602317597245440 |