Table of Contents Author Guidelines Submit a Manuscript
Advances in Meteorology
Volume 2015, Article ID 325718, 15 pages
http://dx.doi.org/10.1155/2015/325718
Research Article

How Well Do Gridded Datasets of Observed Daily Precipitation Compare over Australia?

Climate Change Research Centre and ARC Centre of Excellence for Climate System Science, UNSW Australia, Level 4, Mathews Building, Sydney, NSW 2052, Australia

Received 31 May 2015; Revised 10 August 2015; Accepted 11 August 2015

Academic Editor: Charles Jones

Copyright © 2015 Steefan Contractor et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. Aghakouchak, A. Mehran, H. Norouzi, and A. Behrangi, “Systematic and random error components in satellite precipitation data sets,” Geophysical Research Letters, vol. 39, no. 9, pp. 3–6, 2012. View at Publisher · View at Google Scholar · View at Scopus
  2. H. Yin, M. G. Donat, L. V. Alexander, and Y. Sun, “Multi-dataset comparison of gridded observed temperature and precipitation extremes over China,” International Journal of Climatology, vol. 35, no. 10, pp. 2809–2827, 2015. View at Publisher · View at Google Scholar
  3. A. Aghakouchak, A. Behrangi, S. Sorooshian, K. Hsu, and E. Amitai, “Evaluation of satellite-retrieved extreme precipitation rates across the central United States,” Journal of Geophysical Research: Atmospheres, vol. 116, no. 2, Article ID D02115, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. A. D. King, L. V. Alexander, and M. G. Donat, “The efficacy of using gridded data to examine extreme rainfall characteristics: a case study for Australia,” International Journal of Climatology, vol. 33, no. 10, pp. 2376–2387, 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. N. Hofstra, M. New, and C. McSweeney, “The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data,” Climate Dynamics, vol. 35, no. 5, pp. 841–858, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. N. Hofstra, M. Haylock, M. New, P. Jones, and C. Frei, “Comparison of six methods for the interpolation of daily, European climate data,” Journal of Geophysical Research, vol. 113, no. 21, Article ID D21110, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Yatagai, K. Kamiguchi, O. Arakawa, A. Hamada, N. Yasutomi, and A. Kitoh, “Aphrodite constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges,” Bulletin of the American Meteorological Society, vol. 93, no. 9, pp. 1401–1415, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. D. A. Jones, W. Wang, and R. Fawcett, “High-quality spatial climate data-sets for Australia,” Australian Meteorological and Oceanographic Journal, vol. 58, no. 4, pp. 233–248, 2009. View at Google Scholar · View at Scopus
  9. C. G. Menéndez, M. De Castro, A. Sörensson, and J.-P. Boulanger, “CLARIS project: towards climate downscaling in South America,” Meteorologische Zeitschrift, vol. 19, no. 4, pp. 357–362, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. E. J. Klok and A. M. G. K. Tank, “Updated and extended European dataset of daily climate observations,” International Journal of Climatology, vol. 29, no. 8, pp. 1182–1191, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. B. Mueller, S. I. Seneviratne, C. Jimenez et al., “Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations,” Geophysical Research Letters, vol. 38, no. 6, 2011. View at Publisher · View at Google Scholar
  12. C.-T. Chen and T. Knutson, “On the verification and comparison of extreme rainfall indices from climate models,” Journal of Climate, vol. 21, no. 7, pp. 1605–1621, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. R. J. H. Dunn, M. G. Donat, and L. V. Alexander, “Investigating uncertainties in global gridded datasets of climate extremes,” Climate of the Past, vol. 10, no. 6, pp. 2171–2199, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. M. New, M. Todd, M. Hulme, and P. Jones, “Precipitation measurements and trends in the twentieth century,” International Journal of Climatology, vol. 21, no. 15, pp. 1899–1922, 2001. View at Google Scholar · View at Scopus
  15. R. J. Joyce, J. E. Janowiak, P. A. Arkin, and P. Xie, “CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution,” Journal of Hydrometeorology, vol. 5, no. 3, pp. 487–503, 2004. View at Google Scholar · View at Scopus
  16. S. Sorooshian, K.-L. Hsu, X. Gao, H. V. Gupta, B. Imam, and D. Braithwaite, “Evaluation of PERSIANN system satellite-based estimates of tropical rainfall,” Bulletin of the American Meteorological Society, vol. 81, no. 9, pp. 2035–2046, 2000. View at Publisher · View at Google Scholar · View at Scopus
  17. G. J. Huffman, R. F. Adler, D. T. Bolvin et al., “The TRMM Multisatellite Precipitation Analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales,” Journal of Hydrometeorology, vol. 8, no. 1, pp. 38–55, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. M. B. Sylla, F. Giorgi, E. Coppola, and L. Mariotti, “Uncertainties in daily rainfall over Africa: assessment of gridded observation products and evaluation of a regional climate model simulation,” International Journal of Climatology, vol. 33, no. 7, pp. 1805–1817, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. D. A. Jones, W. Wang, and R. Fawcett, Australian Water Availability Project Daily 672 Gridded Rainfall, 2014, http://www.bom.gov.au/jsp/awap/rain/index.jsp.
  20. G. J. Huffman, E. F. Stocker, D. T. Bolvin, E. J. Nelkin, and R. F. Adler, “TRMM Version 7 3B42,” NASA/GSFC, Greenbelt, Md, USA, 2014, http://mirador.gsfc.nasa.gov/cgi-bin/mirador/presentNavigation.pl?tree=project&dataset=TRMM_3B42_daily.007&project=TRMM&dataGroup=Gridded&version=007.
  21. G. J. Huffman, R. F. Adler, M. M. Morrissey et al., “Global precipitation at one-degree daily resolution from multisatellite observations,” Journal of Hydrometeorology, vol. 2, no. 1, pp. 36–50, 2001. View at Publisher · View at Google Scholar · View at Scopus
  22. G. J. Huffman, R. F. Adler, M. M. Morrissey et al., Global Precipitation One-Degree Daily Data Set, NASA/GSFC, Greenbelt, Md, USA, 2014, ftp://ftp.cgd.ucar.edu/archive/PRECIP/.
  23. P. W. Jones, “First- and second-order conservative remapping schemes for grids in spherical coordinates,” Monthly Weather Review, vol. 127, no. 9, pp. 2204–2210, 1999. View at Google Scholar · View at Scopus
  24. M. J. Menne, I. Durre, R. S. Vose, B. E. Gleason, and T. G. Houston, “An overview of the global historical climatology network-daily database,” Journal of Atmospheric and Oceanic Technology, vol. 29, no. 7, pp. 897–910, 2012. View at Publisher · View at Google Scholar · View at Scopus
  25. M. J. Menne, I. Durre, B. Korzeniewski et al., Global Historical Climatology Network—Daily (GHCN-Daily), Version 3.20-upd-2015011506, NOAA National Climatic Data Center, 2012, ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily.
  26. D. A. Jones, W. Wang, and R. Fawcett, “Climate Data for the Australian Water Availability Project,” 2007.
  27. G. J. Huffman, R. F. Adler, P. Arkin et al., “The global precipitation climatology project (GPCP) combined precipitation dataset,” Bulletin of the American Meteorological Society, vol. 78, no. 1, pp. 5–20, 1997. View at Publisher · View at Google Scholar · View at Scopus
  28. O. Tveito, “Spatialisation of climatological and meteorological information with the support of GIS (Working Group 2),” in The Use of Geographic Information Systems in Climatology and Meteorology, 2006. View at Google Scholar
  29. D. Kurtzman and R. Kadmon, “Mapping of temperature variables in Israel: a comparison of different interpolation methods,” Climate Research, vol. 13, no. 1, pp. 33–43, 1999. View at Publisher · View at Google Scholar · View at Scopus
  30. C. Daly, “Guidelines for assessing the suitability of spatial climate data sets,” International Journal of Climatology, vol. 26, no. 6, pp. 707–721, 2006. View at Publisher · View at Google Scholar · View at Scopus
  31. K. Stahl, R. D. Moore, J. A. Floyer, M. G. Asplin, and I. G. McKendry, “Comparison of approaches for spatial interpolation of daily air temperature in a large region with complex topography and highly variable station density,” Agricultural and Forest Meteorology, vol. 139, no. 3-4, pp. 224–236, 2006. View at Publisher · View at Google Scholar · View at Scopus
  32. S. S. P. Shen, P. Dzikowski, G. Li, and D. Griffith, “Interpolation of 1961–97 daily temperature and precipitation data onto alberta polygons of ecodistrict and soil landscapes of Canada,” Journal of Applied Meteorology, vol. 40, no. 12, pp. 2162–2177, 2001. View at Publisher · View at Google Scholar · View at Scopus
  33. H. Akima, “Algorithm 761: scattered-data surface fitting that has the accuracy of a cubic polynomial,” ACM Transactions on Mathematical Software, vol. 22, no. 3, pp. 362–371, 1996. View at Publisher · View at Google Scholar · View at Scopus
  34. H. Akima, “A new method of interpolation and smooth curve fitting based on local procedures,” Journal of the ACM, vol. 17, no. 4, pp. 589–602, 1970. View at Publisher · View at Google Scholar
  35. C. H. Jarvis and N. Stuart, “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,” Journal of Applied Meteorology, vol. 40, no. 6, pp. 1075–1084, 2001. View at Google Scholar · View at Scopus
  36. H. Akima, “A method of bivariate interpolation and smooth surface fitting for irregularly distributed data points,” ACM Transactions on Mathematical Software, vol. 4, no. 2, pp. 148–159, 1978. View at Publisher · View at Google Scholar
  37. F. J. Moral, “Comparison of different geostatistical approaches to map climate variables: application to precipitation,” International Journal of Climatology, vol. 30, no. 4, pp. 620–631, 2010. View at Publisher · View at Google Scholar · View at Scopus
  38. R. Sibson, “A brief description of natural neighbour interpolation,” in Interpreting Multivariate Data, vol. 21, pp. 21–36, 1981. View at Google Scholar
  39. S. E. Koch, M. Desjardins, and P. J. Kocin, “An interactive Barnes objective map analysis scheme for use with satellite and conventional data.,” Journal of Climate & Applied Meteorology, vol. 22, no. 9, pp. 1487–1503, 1983. View at Publisher · View at Google Scholar · View at Scopus
  40. S. K. Sinha and S. G. Narkhedkar, “Barnes objective analysis scheme of daily rainfall over Maharashtra ( India ) on a mesoscale grid,” Atmósfera, vol. 19, no. 2, pp. 109–126, 2006. View at Google Scholar
  41. G. Weymouth, G. A. Mills, D. Jones, E. E. Ebert, and M. J. Manton, “A continental-scale daily rainfall analysis system,” The Australian Meteorological Magazine, vol. 48, pp. 169–179, 1999. View at Google Scholar
  42. S. L. Barnes, Mesoscale Objective Map Analysis Using Weighted Time-series Observations, 1973.
  43. C. V. Deutsch, “Correcting for negative weights in ordinary kriging,” Computers and Geosciences, vol. 22, no. 7, pp. 765–773, 1996. View at Publisher · View at Google Scholar · View at Scopus
  44. P. Holper, “Climate Change, Science Information Paper: Australian Rainfall: Past, Present and Future,” 2011.
  45. A. Behrangi, M. Lebsock, S. Wong, and B. Lambrigtsen, “On the quantification of oceanic rainfall using spaceborne sensors,” Journal of Geophysical Research: Atmospheres, vol. 117, no. 20, Article ID D20105, 2012. View at Publisher · View at Google Scholar · View at Scopus
  46. A. Behrangi, G. Stephens, R. F. Adler, G. J. Huffman, B. Lambrigtsen, and M. Lebsock, “An update on the oceanic precipitation rate and its zonal distribution in light of advanced observations from space,” Journal of Climate, vol. 27, no. 11, pp. 3957–3965, 2014. View at Publisher · View at Google Scholar · View at Scopus
  47. L. Alexander and C. Tebaldi, “Climate and weather extremes: observations, modelling and projections,” in The Future of the World's Climate, pp. 253–288, Elsevier, Amsterdam, The Netherlands, 2012. View at Google Scholar
  48. G. J. Huffman, A. Pendergrass, and National Center for Atmospheric Research Staff, Eds., TRMM: Tropical Rainfall Measuring Mission, The Climate Data Guide, 2015, https://climatedataguide.ucar.edu/climate-data/trmm-tropical-rainfall-measuring-mission.
  49. D. W. Pierce, T. P. Barnett, B. D. Santer, and P. J. Gleckler, “Selecting global climate models for regional climate change studies,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 21, pp. 8441–8446, 2009. View at Publisher · View at Google Scholar · View at Scopus
  50. D. Krige, “A statistical approach to some basic mine valuation problems on the Witwatersrand,” Journal of the Chemical, Metallurgical and Mining Society of South Africa, vol. 52, no. 6, pp. 119–139, 1951. View at Google Scholar
  51. Z. Kebaili Bargaoui and A. Chebbi, “Comparison of two kriging interpolation methods applied to spatiotemporal rainfall,” Journal of Hydrology, vol. 365, no. 1-2, pp. 56–73, 2009. View at Publisher · View at Google Scholar · View at Scopus
  52. U. Haberlandt, “Geostatistical interpolation of hourly precipitation from rain gauges and radar for a large-scale extreme rainfall event,” Journal of Hydrology, vol. 332, no. 1-2, pp. 144–157, 2007. View at Publisher · View at Google Scholar · View at Scopus
  53. P. Goovaerts, “Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall,” Journal of Hydrology, vol. 228, no. 1-2, pp. 113–129, 2000. View at Publisher · View at Google Scholar · View at Scopus
  54. K. N. Dirks, J. E. Hay, C. D. Stow, and D. Harris, “High-resolution studies of rainfall on Norfolk Island Part II: interpolation of rainfall data,” Journal of Hydrology, vol. 208, no. 3-4, pp. 187–193, 1998. View at Publisher · View at Google Scholar · View at Scopus
  55. R. D. C. Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2008.
  56. Mini-Language Reference, “NCAR Command Language,” National Center for Atmospheric Research, 2013, http://www.ncl.ucar.edu/Document/Manuals/Ref_Manual/.