Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution

Journal of Geophysical Research

Volumn 119, Issue 5, 2014, Pages 2294-2313

  Kilibarda, Milan ^a   Hengl, Tomislav ^b   Heuvelink, Gerard B M ^c   Gräler, Benedikt ^d   Pebesmatadić, Edzermelita Perčec ^d,e   Bajat, Branislav ^a  

^a UNIVERSITY OF BELGRADE   (Serbia)

^b ISRIC WORLD SOIL INFORMATION   (Netherlands)

^c WAGENINGEN UNIVERSITY   (Netherlands)

^d UNIVERSITY OF MÜNSTER   (Germany)

^e METEOROLOGICAL AND HYDROLOGICAL SERVICE   (Croatia)

Author keywords

[No Author keywords available]

Indexed keywords

ATMOSPHERIC TEMPERATURE; CLIMATE MODELS; INTERPOLATION; METEOROLOGY; RADIOMETERS; SATELLITE IMAGERY;

AIR TEMPERATURE; KRIGING; LAND SURFACE TEMPERATURE; LEAVE-ONE-OUT CROSS VALIDATIONS; MODERATE RESOLUTION IMAGING SPECTRORADIOMETER; MODIS LST; SPATIO-TEMPORAL INTERPOLATIONS; TOPOGRAPHIC WETNESS INDEX;

FORECASTING;

ACCURACY ASSESSMENT; AIR TEMPERATURE; DATA SET; DIGITAL ELEVATION MODEL; DIURNAL VARIATION; ERROR ANALYSIS; GEOSTATISTICS; GLOBAL PERSPECTIVE; INTERPOLATION; KRIGING; LAND SURFACE; MODIS; RESOLUTION; SPATIOTEMPORAL ANALYSIS; TIME SERIES; WEATHER STATION;

ANTARCTICA;

EID: 84898400851     PISSN: 01480227     EISSN: 21562202     Source Type: Journal    
DOI: 10.1002/2013JD020803     Document Type: Article

References (45)

1. Alexander, L. V., et al. (2006), Global observed changes in daily climate extremes of temperature and precipitation, J. Geophys. Res., 111, D05109, doi:10.1029/2005JD006290.
2. Bader, M. Y., and J. J. Ruijten (2008), A topography-based model of forest cover at the alpine tree line in the tropical Andes, J. Biogeogr., 35(4), 711–723, doi:10.1111/j.1365-2699.2007.01818.x.
3. Becker, A., P. Finger, A. Meyer-Christoffer, B. Rudolf, K. Schamm, U. Schneider, and M. Ziese (2012), A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present, Earth Syst. Sci. Data Discuss., 5(2), 921–998, doi:10.5194/essdd-5-921-2012.
4. Böhner, J., T. Blaschke, and L. Montanarella (Eds.) (2008), SAGA — Seconds Out, vol. 19, 113 pp., Hamburger Beiträge zur Physischen Geographie und Landschaftsökologie, Hamb.
5. Burrough, P. (Ed.) (1998), Principles of Geographical Information Systems, 2nd ed., 220 pp., Oxford Univ. Press, Oxford.
6. Caesar, J., L. Alexander, and R. Vose (2006), Large-scale changes in observed daily maximum and minimum temperatures: Creation and analysis of a new gridded data set, J. Geophys. Res., 111, D05101, doi:10.1029/2005JD006280.
7. Carrera-Hernández, J., and S. Gaskin (2007), Spatio temporal analysis of daily precipitation and temperature in the Basin of Mexico, J. Hydrol., 336(3), 231–249.
8. Coll, C., Z. Wan, and J. M. Galve (2009), Temperature-based and radiance-based validations of the V5 MODIS land surface temperature product, J. Geophys. Res., 114, D20102, doi:10.1029/2009JD012038.
9. Cressie, N., and C. K. Wikle (2011), Statistics for Spatio-Temporal Data, Wiley, N. J.
10. Di Luzio, M., G. L. Johnson, C. Daly, J. K. Eischeid, and J. G. Arnold (2008), Constructing retrospective gridded daily precipitation and temperature datasets for the conterminous United States, J. Appl. Meteorol. Climatol., 47(2), 475–497, doi:10.1175/2007JAMC1356.1.
11. Gething, P., P. Atkinson, A. Noor, P. Gikandi, S. Hay, and M. Nixon (2007), A local space-time kriging approach applied to a national outpatient malaria data set, Comput. Geosci., 33(10), 1337–1350, doi:10.1016/j.cageo.2007.05.006.
12. Gräler, B., L. Gerharz, and E. Pebesma (2011), Spatio-temporal analysis and interpolation of PM10 measurements in Europe, ETC/ACM Technical Paper, 10.
13. Haylock, M. R., N. Hofstra, A. M. G. Klein Tank, E. J. Klok, P. D. Jones, and M. New (2008), A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006, J. Geophys. Res., 113, D20119, doi:10.1029/2008JD010201.
14. Hengl, T., G. B. Heuvelink, M. Perčec Tadić, and E. J. Pebesma (2012), Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images, Theor. Appl. Climatol., 107, 265–277, doi:10.1007/s00704-011-0464-2.
15. Heuvelink, G. B. M., and D. A. Griffith (2010), Space-time geostatistics for geography: A case study of radiation monitoring across parts of Germany, Geog. Anal., 42(2), 161–179, doi:10.1111/j.1538-4632.2010.00788.x.
16. Heuvelink, G. B. M., D. A. Griffith, T. Hengl, and S. J. Melles (2012), Sampling Design Optimization for Space-Time Kriging, pp. 207–230, John Wiley, Oxford, doi:10.1002/9781118441862.ch9.
17. Hijmans, R. J., S. E. Cameron, J. L. Parra, P. G. Jones, and A. Jarvis (2005), Very high resolution interpolated climate surfaces for global land areas, Int. J. Circuit Theory Appl., 25, 1965–1978, doi:10.1002/joc.1276.
18. Hofstra, N., M. Haylock, M. New, P. Jones, and C. Frei (2008), Comparison of six methods for the interpolation of daily, European climate data, J. Geophys. Res., 113, D21110, doi:10.1029/2008JD010100.
19. Jarvis, C. H., and N. Stuart (2001), A comparison among strategies for interpolating maximum and minimum daily air temperatures. Part II: The interaction between number of guiding variables and the type of interpolation method, J. Appl. Meteorol., 40(6), 1075–1084.
20. Kiktev, D., D. M. Sexton, L. Alexander, and C. K. Folland (2003), Comparison of modeled and observed trends in indices of daily climate extremes, J. Clim., 16(22), 3560–3571.
21. Kilibarda, M., and B. Bajat (2012), Plotgooglemaps: The R-based web-mapping tool for thematic spatial data, Geomatica, 66(1), 37–49, doi:10.5623/cig2012-007.
22. Klein Tank, A. M. G., et al. (2002), Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment, Int. J. Climatol., 22(12), 1441–1453, doi:10.1002/joc.773.
23. Lawrimore, J. H., M. J. Menne, B. E. Gleason, C. N. Williams, D. B. Wuertz, R. S. Vose, and J. Rennie (2011), An overview of the Global Historical Climatology Network monthly mean temperature data set, version 3, J. Geophys. Res., 116, D19121, doi:10.1029/2011JD016187.
24. Leemans, R., and W. P. Cramer (1991), The IIASA database for mean monthly values of temperature, precipitation, and cloudiness on a global terrestrial grid, International Institute for Applied Systems Analysis, Laxenburg, Austria.
25. Legates, D., and C. Willmott (1990), Mean seasonal and spatial variability in global surface air temperature, Theor. Appl. Climatol., 41(1-2), 11–21, doi:10.1007/BF00866198.
26. Mitchell, T. D., and P. D. Jones (2005), An improved method of constructing a database of monthly climate observations and associated high-resolution grids, Int. J. Climatol., 25(6), 693–712, doi:10.1002/joc.1181.
27. Neteler, M. (2010), Estimating daily land surface temperatures in mountainous environments by reconstructed MODIS LST Data, Remote Sens., 2(1), 333–351, doi:10.3390/rs1020333.
28. New, M., M. Hulme, and P. Jones (1999), Representing twentieth-century space-time climate variability. Part I: Development of a 1961–90 mean monthly terrestrial climatology, J. Clim., 12, 829–856, doi:10.1175/1520-0442(1999)012<0829:RTCSTC>2.0.CO;2.
29. New, M., M. Hulme, and P. Jones (2000), Representing twentieth-century space-time climate variability. Part II: development of 1901-96 monthly grids of terrestrial surface climate, J. Clim., 13, 2217–2238, doi:10.1175/1520-0442(2000)013<2217:RTCSTC>2.0.CO;2.
30. Pebesma, E. (2012), Spacetime: Spatio-temporal data in R, J. Stat. Software, 51, 1–30.
31. Pebesma, E. J. (2004), Multivariable geostatistics in S: The gstat package, Comput. Geosci., 30(7), 683–691, doi:10.1016/j.cageo.2004.03.012.
32. Peterson, T. C., and R. S. Vose (1997), An overview of the Global Historical Climatology Network temperature database, Bull. Am. Meteorol. Soc., 78(12), 2837–2849, doi:10.1175/1520-0477(1997)078<2837:AOOTGH>2.0.CO;2.
33. Pinheiro, J., and D. Bates (2009), Mixed-Effects Models in S and S-PLUS, Statistics and Computing, Springer, New York.
34. Piper, S. C., and E. F. Stewart (1996), A gridded global data set of daily temperature and precipitation for terrestrial biospheric modeling, Global Biogeochem. Cycles, 10(4), 757–782, doi:10.1029/96GB01894.
35. Price, D. T., D. W. McKenney, I. A. Nalder, M. F. Hutchinson, and J. L. Kesteven (2000), A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data, Agric. For. Meteorol., 101(2), 81–94.
36. R Development Core Team (2012), R: A Language and Environment for Statistical Computing, 1731 pp., R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.
37. Sorensen, R., U. Zinko, and J. Seibert (2006), On the calculation of the topographic wetness index: Evaluation of different methods based on field observations, Hydrol. Earth Syst. Sci. Discuss., 10, 101–112.
38. Spadavecchia, L., and M. Williams (2009), Can spatio-temporal geostatistical methods improve high resolution regionalisation of meteorological variables? Agric. For. Meteorol., 149(6), 1105–1117.
39. Stahl, K., R. Moore, J. Floyer, M. Asplin, and I. McKendry (2006), Comparison of approaches for spatial interpolation of daily air temperature in a large region with complex topography and highly variable station density, Agric. For. Meteorol., 139(3), 224–236, doi:10.1016/j.agrformet.2006.07.004.
40. Tveito, O., M. Wegehenkel, F. van der Wel, and H. Dobesch (2006), Spatialisation of climatological and meteorological information with the support of GIS (Working Group 2), The Use of Geographic Information Systems in Climatology and Meteorology, Final Report. pp. 37–172.
41. Van De Kerchove, R., S. Lhermitte, S. Veraverbeke, and R. Goossens (2013), Spatio-temporal variability in remotely sensed land surface temperature, and its relationship with physiographic variables in the Russian Altay Mountains, Int. J. Appl. Earth Obs. Geoinf., 20, 4–19.
42. Van den Besselaar, E. J. M., M. R. Haylock, G. van der Schrier, and A. M. G. Klein Tank (2011), A European daily high-resolution observational gridded data set of sea level pressure, J. Geophys. Res., 116, D11110, doi:10.1029/2010JD015468.
43. Vose, R. S., O.R.N.L.E.S. Division., G. C. R. P. (U.S.), U. S. D. of Energy. Office of Health, E. Research., C. D. I. A. C. (U.S.), and I. Martin Marietta Energy Systems (1992), The Global Historical Climatology Network: Long-Term Monthly Temperature, Precipitation, Sea Level Pressure, and Station Pressure Data, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory; Available to the public from N.T.I.S., Oak Ridge, Tenn.; Springfield, VA.
44. Wan, Z., Y. Zhang, Q. Zhang, and Z.-L. Li (2004), Quality assessment and validation of the MODIS global land surface temperature, Int. J. Remote Sens., 25(1), 261–274, doi:10.1080/0143116031000116417.
45. Yoo, J.-M., Y.-I. Won, Y.-J. Cho, M.-J. Jeong, D.-B. Shin, S.-J. Lee, Y.-R. Lee, S.-M. Oh, and S.-J. Ban (2011), Temperature trends in the skin/surface, mid-troposphere and low stratosphere near Korea from satellite and ground measurements, Asia-Pac. J. Atmos. Sci., 47, 439–455, doi:10.1007/s13143-011-0029-4.