Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution
2014
Аутори
Kilibarda, MilanHengl, Tomislav
Heuvelink, Gerard B. M.
Graeler, Benedikt
Pebesma, Edzer
Tadić-Percec, Melita
Bajat, Branislav
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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 te...mperatures 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
Кључне речи:
spatio-temporal kriging / spatio-temporal interpolation / daily air temperature / MODIS LSTИзвор:
Journal of Geophysical Research-Atmospheres, 2014, 119, 5, 2294-2313Издавач:
- Wiley-Blackwell
Финансирање / пројекти:
- Просторни, еколошки, енергетски и друштвени аспекти развоја насеља и климатске промене - међусобни утицаји (RS-MESTD-Technological Development (TD or TR)-36035)
DOI: 10.1002/2013JD020803
ISSN: 2169-897X
WoS: 000333885700019
Scopus: 2-s2.0-84898400851
Институција/група
GraFarTY - JOUR AU - Kilibarda, Milan AU - Hengl, Tomislav AU - Heuvelink, Gerard B. M. AU - Graeler, Benedikt AU - Pebesma, Edzer AU - Tadić-Percec, Melita AU - Bajat, Branislav PY - 2014 UR - https://grafar.grf.bg.ac.rs/handle/123456789/639 AB - 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 PB - Wiley-Blackwell T2 - Journal of Geophysical Research-Atmospheres T1 - Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution EP - 2313 IS - 5 SP - 2294 VL - 119 DO - 10.1002/2013JD020803 ER -
@article{ author = "Kilibarda, Milan and Hengl, Tomislav and Heuvelink, Gerard B. M. and Graeler, Benedikt and Pebesma, Edzer and Tadić-Percec, Melita and Bajat, Branislav", 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", publisher = "Wiley-Blackwell", journal = "Journal of Geophysical Research-Atmospheres", title = "Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution", pages = "2313-2294", number = "5", volume = "119", doi = "10.1002/2013JD020803" }
Kilibarda, M., Hengl, T., Heuvelink, G. B. M., Graeler, B., Pebesma, E., Tadić-Percec, M.,& Bajat, B.. (2014). Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution. in Journal of Geophysical Research-Atmospheres Wiley-Blackwell., 119(5), 2294-2313. https://doi.org/10.1002/2013JD020803
Kilibarda M, Hengl T, Heuvelink GBM, Graeler B, Pebesma E, Tadić-Percec M, Bajat B. Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution. in Journal of Geophysical Research-Atmospheres. 2014;119(5):2294-2313. doi:10.1002/2013JD020803 .
Kilibarda, Milan, Hengl, Tomislav, Heuvelink, Gerard B. M., Graeler, Benedikt, Pebesma, Edzer, Tadić-Percec, Melita, Bajat, Branislav, "Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution" in Journal of Geophysical Research-Atmospheres, 119, no. 5 (2014):2294-2313, https://doi.org/10.1002/2013JD020803 . .