Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation

Spatial Statistics

Volumn 14, Issue , 2015, Pages 22-38

  Kilibarda, Milan ^a   Tadić, Melita Perčec ^c   Hengl, Tomislav ^b   Luković, Jelena ^d   Bajat, Branislav ^a  

^a UNIVERSITY OF BELGRADE   (Serbia)

^b ISRIC WORLD SOIL INFORMATION   (Netherlands)

^c METEOROLOGICAL AND HYDROLOGICAL SERVICE   (Croatia)

^d University of Belgrade   (Serbia)

Author keywords

Daily temperature interpolation; Global space time kriging model; GSOD; MaxEnt; MODIS LST; Spatio temporal analysis

Indexed keywords

EID: 84947862977     PISSN: 22116753     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.spasta.2015.04.005     Document Type: Article

References (46)

1. Baddeley A., Turner R. Modelling spatial point patterns in R. Lecture Notes in Statistics 2006, vol. 185:23-74. Springer, New York. A. Baddeley, P. Gregori, J. Mateu, R. Stoica, D. Stoyan (Eds.).
2. Beniston M. Mountain weather and climate: A general overview and a focus on climatic change in the Alps. Hydrobiologia 2006, 562:3-16.
3. Bivand R., Pebesma E., Rubio V. Applied Spatial Data Analysis with R. Use R Series 2013, Springer, Heidelberg. second ed.
4. Caesar J., Alexander L., Vose R. Large-scale changes in observed daily maximum and minimum temperatures: Creation and analysis of a new gridded data set. J. Geophys. Res. D Atmos. 2006, 111(D5):D05101.
5. Chapman A.D. Principles of data quality, version 1.0. Report for the Global Biodiversity Information Facility 2005, Australian Biodiversity Information Services, Toowoomba South.
6. Coll C., Wan Z., Galve J.M. Temperature-based and radiance-based validations of the V5 MODIS land surface temperature product. J. Geophys. Res. 2009, 114(D20):D20102.
7. Cressie N., Wikle C.K. Statistics for Spatio-temporal Data 2011, Wiley.
8. Daly C., Halbleib M., Smith J.I., Gibson W.P., Doggett M.K., Taylor G.H., Curtis J., Pasteris P.P. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous united states. Int. J. Climatol. 2008, 28(15):2031-2064.
9. Di Luzio M., Johnson G.L., Daly C., Eischeid J.K., Arnold J.G. Constructing retrospective gridded daily precipitation and temperature datasets for the conterminous united states. J. Appl. Meterol. Climatol. 2008, 47(2):475-497.
10. Diggle P.J. Statistical Analysis of Spatial Point Patterns. Mathematics in Biology 2003, Edward Arnold, New York. second ed.
11. Elith J., H.Graham C., P.Anderson R., Dudk M., Ferrier S., Guisan A., J.Hijmans R., Huettmann F., R.Leathwick J., Lehmann A., Li J., G Lohmann L., A.Loiselle B., Manion G., Moritz C., Nakamura M., Nakazawa Y., McC M., Overton J., Townsend Peterson A., J.Phillips S., Richardson K., Scachetti-Pereira R., E.Schapire R., Sobern J., Williams S., S.Wisz M., E.Zimmermann N. Novel methods improve prediction of species distributions from occurrence data. Ecography 2006, 29(2):129-151.
12. Elith J., Phillips S.J., Hastie T., Dudík M., Chee Y.E., Yates C.J. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 2011, 17(1):43-57.
13. Feidas H., Makrogiannis T., Bora-Senta E. Trend analysis of air temperature time series in greece and their relationship with circulation using surface and satellite data: 1955-2001. Theor. Appl. Climatol. 2004, 79:185-208.
14. Hartmann D. Global Physical Climatology 1994, Academic Press, San Diego.
15. Haylock M.R., Hofstra N., Klein Tank A.M.G., Klok E.J., Jones P.D., New M. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. J. Geophys. Res. D Atmos. 2008, 113(D20):D20119.
16. Hengl T., Heuvelink G.B., Perčec Tadić M., Pebesma E.J. Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images. Theor. Appl. Climatol. 2012, 107:265-277.
17. Heuvelink G.B.M., Griffith D.A., Hengl T., Melles S.J. Sampling Design Optimization for Space-Time Kriging 2012, 207-230. John Wiley & Sons, Ltd.
18. Hijmans R.J., Cameron S.E., Parra J.L., Jones P.G., Jarvis A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 2005, 25:1965-1978.
19. Hofstra N., Haylock M., New M., Jones P., Frei C. Comparison of six methods for the interpolation of daily, European climate data. J. Geophys. Res. D Atmos. 2008, 113(21).
20. Kilibarda M., Bajat B. PlotGoogleMaps: The R-based web-mapping tool for thematic spatial data. Geomatica 2012, 66(1):37-49.
21. Kilibarda M., Hengl T., Heuvelink G.B.M., Gräler B., Pebesma E., Perčec Tadić M., Bajat B. Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution. J. Geophys. Res. Atmos. 2014, 119:2294-2313. 10.1002/2013JD020803.
22. Klein Tank A.M.G., Wijngaard J.B., Können G.P., Böhm R., Demarée G., Gocheva A., Mileta M., Pashiardis S., Hejkrlik L., Kern-Hansen C., Heino R., Bessemoulin P., Müller-Westermeier G., Tzanakou M., Szalai S., Pálsdóttir T., Fitzgerald D., Rubin S., Capaldo M., Maugeri M., Leitass A., Bukantis A., Aberfeld R., van Engelen A.F.V., Forland E., Mietus M., Coelho F., Mares C., Razuvaev V., Nieplova E., Cegnar T., Antonio López J., Dahlström B., Moberg A., Kirchhofer W., Ceylan A., Pachaliuk O., Alexander L.V., Petrovic P. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int. J. Climatol. 2002, 22(12):1441-1453.
23. Kleiner K. Data on demand. Nature Clim. Change 2011, 1:10-12.
24. Kottek M., Rubel F. Global daily precipitation fields form bias-corrected rain gauge and satelite observations. part i: Design and development. Meteorol. Z. 2007, 16(5):525-539.
25. Lawrimore J.H., Menne M.J., Gleason B.E., Williams C.N., Wuertz D.B., Vose R.S., Rennie J. An overview of the Global Historical Climatology Network monthly mean temperature data set, version 3. J. Geophys. Res. 2011, 116(D19):D19121.
26. Löwe, P., Neteler, M., 2014. Data science: history repeated?: the heritage of the free and open source gis community. In: EGU General Assembly 2014.
27. Metz M., Rocchini D., Neteler M. Surface temperatures at the continental scale: Tracking changes with remote sensing at unprecedented detail. Remote Sens. 2014, 6(5):3822-3840. URL http://www.mdpi.com/2072-4292/6/5/3822.
28. Mitchell T.D., Jones P.D. An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. J. Climatol. 2005, 25(6):693-712.
29. Neteler M. Estimating daily land surface temperatures in mountainous environments by reconstructed modis lst data. Remote Sens. 2010, 2(1):333-351.
30. New M., Hulme M., Jones P. Representing twentieth-century space-time climate variability. Part I: Development of a 1961-90 mean monthly terrestrial climatology. J. Clim. 1999, 12:829-856.
31. New M., Hulme M., Jones P. Representing twentieth-century space-time climate variability. Part II: development of 1901-96 monthly grids of terrestrial surface climate. J. Clim. 2000, 13:2217-2238.
32. Pebesma E. spacetime: spatio-temporal data in R. J. Stat. Softw. 2012, 51:1-30.
33. Pebesma E.J. Multivariable geostatistics in s: the gstat package. Comput. Geosci. 2004, 30(7):683-691.
34. Perčec Tadić M. Gridded Croatian climatology for 1961-1990. Theor. Appl. Climatol. 2010, (January):1434-4483.
35. Peterson T.C., Vose R.S. An overview of the global historical climatology network temperature database. Bull. Amer. Math. Soc. 1997, 78(12):2837-2849.
36. Phillips S.J., Anderson R.P., Schapire R.E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 2006, 190(3-4):231-259.
37. Proedrou M., Theoharatos G., Cartalis C. Variations and trends in annual and seasonal air temperatures in greece determined from ground and satellite measurements. Theor. Appl. Climatol. 1997, 57:65-78.
38. R: A Language and Environment for Statistical Computing 2012, R Development Core Team, R Foundation for Statistical Computing, Vienna, Austria.
39. Rohde R., Muller R.A., Jacobsen R., Muller E., Perlmutter S., et al. A new estimate of the average earth surface land temperature spanning 1753 to 2011. Geoinfor. Geostat.: An Overview 2013, 1(1). 10.4172/2327-4581.1000101.
40. Santer B.D., Wigley T.M.L., Gaffen D.J., Bengtsson L., Doutriaux C., Boyle J.S., Esch M., Hnilo J.J., Jones P.D., Meehl G.A., Roeckner E., Taylor K.E., Wehner M.F. Interpreting differential temperature trends at the surface and in the lower troposphere. Science 2000, 287(5456):1227-1232.
41. Smith T.M., Arkin P.A., Bates J.J., Huffman G.J. Estimating bias of satellite-based precipitation estimates. J. Hydrometeorol. 2006, 7(5):841-856.
42. Van den Besselaar E.J.M., Haylock M.R., van der Schrier G., Klein Tank A.M.G. A European daily high-resolution observational gridded data set of sea level pressure. J. Geophys. Res. D Atmos. 2011, 116(D11).
43. Vose R.S., Marietta M. The Global Historical Climatology Network: Long-term Monthly Temperature, Precipitation, Sea Level Pressure, and Station Pressure Data 1992, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, Tenn. Springfield, VA.
44. Wan Z., Zhang Y., Zhang Q., Li Z.-L. Quality assessment and validation of the modis global land surface temperature. Int. J. Remote Sens. 2004, 25(1):261-274.
45. Wessel P., Smith W.H. A global, self-consistent, hierarchical, high-resolution shoreline database. J. Geophys. Res. Solid Earth 1996, 101(B4):8741-8743.
46. Yoo J.-M., Won Y.-I., Cho Y.-J., Jeong M.-J., Shin D.-B., Lee S.-J., Lee Y.-R., Oh S.-M., Ban S.-J. Temperature trends in the skin/surface, mid-troposphere and low stratosphere near Korea from satellite and ground measurements. Asia-Pac. J. Atmos. Sci. 2011, 47:439-455.