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Advances in Meteorology
Volume 2017 (2017), Article ID 7265178, 11 pages
https://doi.org/10.1155/2017/7265178
Research Article

Statistical Downscaling of Temperature with the Random Forest Model

Bo Pang,1,2 Jiajia Yue,1,2 Gang Zhao,1,2 and Zongxue Xu1,2

1College of Water Sciences, Beijing Normal University, Beijing 100875, China
2Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China

Correspondence should be addressed to Gang Zhao

Received 20 December 2016; Revised 25 March 2017; Accepted 17 May 2017; Published 15 June 2017

Academic Editor: Jorge E. Gonzalez

Copyright © 2017 Bo Pang 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. IPCC, Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK, 2007.
  2. R. L. Wilby, S. P. Charles, E. Zorita, B. Timbal, P. Whetton, and L. O. Mearns, Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods, UEA, Norwich, UK, 2004.
  3. J. T. Chu, J. Xia, C.-Y. Xu, and V. P. Singh, “Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China,” Theoretical and Applied Climatology, vol. 99, no. 1-2, pp. 149–161, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. R. L. Wilby, L. E. Hay, and G. H. Leavesley, “A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River Basin, Colorado,” Journal of Hydrology, vol. 225, no. 1-2, pp. 67–91, 1999. View at Publisher · View at Google Scholar · View at Scopus
  5. M. K. Goyal and C. S. P. Ojha, “Downscaling of surface temperature for lake catchment in an arid region in India using linear multiple regression and neural networks,” International Journal of Climatology, vol. 32, no. 4, pp. 552–566, 2012. View at Publisher · View at Google Scholar · View at Scopus
  6. R. Huth, “Statistical downscaling in central Europe: evaluation of methods and potential predictors,” Climate Research, vol. 13, no. 2, pp. 91–101, 1999. View at Publisher · View at Google Scholar · View at Scopus
  7. D. Chen and Y. Chen, “Association between winter temperature in China and upper air circulation over East Asia revealed by canonical correlation analysis,” Global and Planetary Change, vol. 37, no. 3-4, pp. 315–325, 2003. View at Publisher · View at Google Scholar · View at Scopus
  8. P. Coulibaly, Y. B. Dibike, and F. Anctil, “Downscaling precipitation and temperature with temporal neural networks,” Journal of Hydrometeorology, vol. 6, no. 4, pp. 483–496, 2005. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Anandhi, V. V. Srinivas, D. N. Kumar, and R. S. Nanjundiah, “Role of predictors in downscaling surface temperature to river basin in India for IPCC SRES scenarios using support vector machine,” International Journal of Climatology, vol. 29, no. 4, pp. 583–603, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. J. T. Schoof and S. C. Pryor, “Downscaling temperature and precipitation: a comparison of regression-based methods and artificial neural networks,” International Journal of Climatology, vol. 21, no. 7, pp. 773–790, 2001. View at Publisher · View at Google Scholar · View at Scopus
  11. E. Kostopoulou, C. Giannakopoulos, C. Anagnostopoulou et al., “Simulating maximum and minimum temperature over Greece: a comparison of three downscaling techniques,” Theoretical and Applied Climatology, vol. 90, no. 1-2, pp. 65–82, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. D. Duhan and A. Pandey, “Statistical downscaling of temperature using three techniques in the Tons River basin in Central India,” Theoretical and Applied Climatology, vol. 121, no. 3-4, pp. 605–622, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. D. Maraun, H. W. Rust, and T. J. Osborn, “The annual cycle of heavy precipitation across the United Kingdom: a model based on extreme value statistics,” International Journal of Climatology, vol. 29, no. 12, pp. 1731–1744, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. M. Widmann, “One-dimensional CCA and SVD, and their relationship to regression maps,” Journal of Climate, vol. 18, no. 14, pp. 2785–2792, 2005. View at Publisher · View at Google Scholar · View at Scopus
  15. M. K. Tippett, T. DelSole, S. J. Mason, and A. G. Barnston, “Regression-based methods for finding coupled patterns,” Journal of Climate, vol. 21, no. 17, pp. 4384–4398, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Hessami, P. Gachon, T. B. M. J. Ouarda, and A. St-Hilaire, “Automated regression-based statistical downscaling tool,” Environmental Modelling and Software, vol. 23, no. 6, pp. 813–834, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. Z. Liu, Z. Xu, S. P. Charles, G. Fu, and L. Liu, “Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China,” International Journal of Climatology, vol. 31, no. 13, pp. 2006–2020, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. C. Yang, N. Wang, S. Wang, and L. Zhou, “Performance comparison of three predictor selection methods for statistical downscaling of daily precipitation,” Theoretical and Applied Climatology, pp. 1–12, 2016. View at Publisher · View at Google Scholar · View at Scopus
  19. I. Hanssen-Bauer, E. J. Førland, J. E. Haugen, and O. E. Tveito, “Temperature and precipitation scenarios for Norway: comparison of results from dynamical and empirical downscaling,” Climate Research, vol. 25, no. 1, pp. 15–27, 2003. View at Publisher · View at Google Scholar · View at Scopus
  20. A. Hannachi, I. T. Jolliffe, and D. B. Stephenson, “Empirical orthogonal functions and related techniques in atmospheric science: a review,” International Journal of Climatology, vol. 27, no. 9, pp. 1119–1152, 2007. View at Publisher · View at Google Scholar · View at Scopus
  21. O. Fistikoglu and U. Okkan, “Statistical downscaling of monthly precipitation using NCEP/NCAR reanalysis data for tahtali river basin in Turkey,” Journal of Hydrologic Engineering, vol. 16, no. 2, pp. 157–164, 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. A. Sharma, “Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: part 1—A strategy for system predictor identification,” Journal of Hydrology, vol. 239, no. 1–4, pp. 232–239, 2000. View at Publisher · View at Google Scholar · View at Scopus
  23. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Malekipirbazari and V. Aksakalli, “Risk assessment in social lending via random forests,” Expert Systems with Applications, vol. 42, no. 10, pp. 4621–4631, 2015. View at Publisher · View at Google Scholar · View at Scopus
  25. P. Baudron, F. Alonso-Sarría, J. L. García-Aróstegui, F. Cánovas-García, D. Martínez-Vicente, and J. Moreno-Brotóns, “Identifying the origin of groundwater samples in a multi-layer aquifer system with Random Forest classification,” Journal of Hydrology, vol. 499, pp. 303–315, 2013. View at Publisher · View at Google Scholar · View at Scopus
  26. K. Tatsumi, Y. Yamashiki, M. A. Canales Torres, and C. L. R. Taipe, “Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data,” Computers and Electronics in Agriculture, vol. 115, pp. 171–179, 2015. View at Publisher · View at Google Scholar · View at Scopus
  27. Z. Wang, C. Lai, X. Chen, B. Yang, S. Zhao, and X. Bai, “Flood hazard risk assessment model based on random forest,” Journal of Hydrology, vol. 527, pp. 1130–1141, 2015. View at Publisher · View at Google Scholar · View at Scopus
  28. P. O. Gislason, J. A. Benediktsson, and J. R. Sveinsson, “Random forests for land cover classification,” Pattern Recognition Letters, vol. 27, no. 4, pp. 294–300, 2006. View at Publisher · View at Google Scholar · View at Scopus
  29. M. Pal, “Random forest classifier for remote sensing classification,” International Journal of Remote Sensing, vol. 26, no. 1, pp. 217–222, 2005. View at Publisher · View at Google Scholar · View at Scopus
  30. V. F. Rodriguez-Galiano, M. Chica-Olmo, F. Abarca-Hernandez, P. M. Atkinson, and C. Jeganathan, “Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture,” Remote Sensing of Environment, vol. 121, pp. 93–107, 2012. View at Publisher · View at Google Scholar · View at Scopus
  31. V. F. Rodriguez-Galiano, B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez, “An assessment of the effectiveness of a random forest classifier for land-cover classification,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 67, no. 1, pp. 93–104, 2012. View at Publisher · View at Google Scholar · View at Scopus
  32. C. Strobl and A. Zeileis, “Danger: high power! — Exploring the statistical properties of a test for random forest variable importance,” Tech. Rep. 17, University of Munich, 2008. View at Google Scholar
  33. V. Svetnik, A. Liaw, C. Tong, J. Christopher Culberson, R. P. Sheridan, and B. P. Feuston, “Random forest: a classification and regression tool for compound classification and QSAR modeling,” Journal of Chemical Information and Computer Sciences, vol. 43, no. 6, pp. 1947–1958, 2003. View at Publisher · View at Google Scholar · View at Scopus
  34. E. Eccel, L. Ghielmi, P. Granitto, R. Barbiero, F. Grazzini, and D. Cesari, “Prediction of minimum temperatures in an alpine region by linear and non-linear post-processing of meteorological models,” Nonlinear Processes in Geophysics, vol. 14, no. 3, pp. 211–222, 2007. View at Publisher · View at Google Scholar · View at Scopus
  35. Pearl River Water Resources Committee (PRWRC), The Zhujiang Archive, vol. 1, Guangdong Science and Technology Press, Guangzhou, China, 1991, (in Chinese).
  36. Q. Zhang, C.-Y. Xu, and Z. Zhang, “Observed changes of drought/wetness episodes in the Pearl River basin, China, using the standardized precipitation index and aridity index,” Theoretical and Applied Climatology, vol. 98, no. 1-2, pp. 89–99, 2009. View at Publisher · View at Google Scholar · View at Scopus
  37. E. V. Dmitriev, I. V. Nogotkov, V. S. Rogutov, G. Komenko, and A. Chavro, “Temporal error estimate for statistical downscaling regional meteorological models,” Física de la Tierra, vol. 19, pp. 219–241, 2007. View at Google Scholar
  38. C. Lavaysse, M. Vrac, P. Drobinski, M. Lengaigne, and T. Vischel, “Statistical downscaling of the French Mediterranean climate: assessment for present and projection in an anthropogenic scenario,” Natural Hazards and Earth System Science, vol. 12, no. 3, pp. 651–670, 2012. View at Publisher · View at Google Scholar · View at Scopus
  39. L. Liu, Z. Liu, X. Ren, T. Fischer, and Y. Xu, “Hydrological impacts of climate change in the Yellow River Basin for the 21st century using hydrological model and statistical downscaling model,” Quaternary International, vol. 244, no. 2, pp. 211–220, 2011. View at Publisher · View at Google Scholar · View at Scopus
  40. F.-F. Ai, J. Bin, Z.-M. Zhang et al., “Application of random forests to select premium quality vegetable oils by their fatty acid composition,” Food Chemistry, vol. 143, pp. 472–478, 2014. View at Publisher · View at Google Scholar · View at Scopus
  41. P. Gislason, J. Benediktsson, and J. Sveinsson, “Random forest classification of multisource remote sensing and geographic data,” in Proceedings of the Geoscience and Remote Sensing Symposium, vol. 2, pp. 1049–1052, IEEE, Anchorage, Alaska, USA, 2004. View at Publisher · View at Google Scholar
  42. B. C. Hewitson and R. G. Crane, “Climate downscaling: techniques and application,” Climate Research, vol. 7, no. 2, pp. 85–95, 1996. View at Publisher · View at Google Scholar · View at Scopus
  43. B. Timbal, A. Dufour, and B. McAvaney, “An estimate of future climate change for western France using a statistical downscaling technique,” Climate Dynamics, vol. 20, no. 7-8, pp. 807–823, 2003. View at Google Scholar · View at Scopus
  44. K. Tatsumi, T. Oizumi, and Y. Yamashiki, “Effects of climate change on daily minimum and maximum temperatures and cloudiness in the Shikoku region: a statistical downscaling model approach,” Theoretical and Applied Climatology, vol. 120, no. 1-2, pp. 87–98, 2015. View at Publisher · View at Google Scholar · View at Scopus
  45. Z. A. Holden, J. T. Abatzoglou, C. H. Luce, and L. S. Baggett, “Empirical downscaling of daily minimum air temperature at very fine resolutions in complex terrain,” Agricultural and Forest Meteorology, vol. 151, no. 8, pp. 1066–1073, 2011. View at Publisher · View at Google Scholar · View at Scopus