Table of Contents Author Guidelines Submit a Manuscript
Advances in Meteorology
Volume 2017, Article ID 8601296, 15 pages
https://doi.org/10.1155/2017/8601296
Research Article

A Quality Control Method Based on an Improved Random Forest Algorithm for Surface Air Temperature Observations

1School of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, China
2Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China

Correspondence should be addressed to Xing Yang; moc.liamg@xgnayyrrah

Received 14 March 2017; Accepted 6 June 2017; Published 10 July 2017

Academic Editor: Peng Yu

Copyright © 2017 Xiaoling Ye 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. U. Schneider, A. Becker, P. Finger, A. Meyer-Christoffer, M. Ziese, and B. Rudolf, “GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle,” Theoretical and Applied Climatology, vol. 115, no. 1-2, pp. 15–40, 2014. View at Publisher · View at Google Scholar · View at Scopus
  2. M. Schindelegger and R. D. Ray, “Surface pressure tide climatologies deduced from a quality-controlled network of barometric observations,” Monthly Weather Review, vol. 142, no. 12, pp. 4872–4889, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. A. R. Cheng, T. H. Lee, H. I. Ku, and Y. W. Chen, “Quality control program for real-time hourly temperature observation in Taiwan,” Journal of Atmospheric and Oceanic Technology, vol. 33, no. 5, pp. 953–976, 2016. View at Publisher · View at Google Scholar · View at Scopus
  4. Z. Yan and P. D. Jones, “Detecting inhomogeneity in daily climate series using wavelet analysis,” Advances in Atmospheric Sciences, vol. 25, no. 2, pp. 157–163, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. C.-D. Xu, J.-F. Wang, M.-G. Hu, and Q-X. Li, “Estimation of uncertainty in temperature observations made at meteorological stations using a probabilistic spatiotemporal approach,” Journal of Applied Meteorology and Climatology, vol. 53, no. 6, pp. 1538–1546, 2014. View at Publisher · View at Google Scholar
  6. I. Durre, M. J. Menne, B. E. Gleason, T. G. Houston, and R. S. Vose, “Comprehensive automated quality assurance of daily surface observations,” Journal of Applied Meteorology and Climatology, vol. 49, no. 8, pp. 1615–1633, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. J. H. Lawrimore, M. J. Menne, B. E. Gleason et al., “An overview of the Global Historical Climatology Network monthly mean temperature data set, version 3,” Journal of Geophysical Research Atmospheres, vol. 116, no. 19, article D19, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Feng, Q. Hu, and W. H. Qian, “Quality control of daily meteorological data in China, 1951–2000: a new dataset,” International Journal of Climatology, vol. 24, no. 7, pp. 853–870, 2004. View at Publisher · View at Google Scholar · View at Scopus
  9. G. Sciuto, B. Bonaccorso, A. Cancelliere, and G. Rossi, “Quality control of daily rainfall data with neural networks,” Journal of Hydrology, vol. 364, no. 1-2, pp. 13–22, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. D. W. Meek and J. L. Hatfield, “Data quality checking for single station meteorological databases,” Agricultural and Forest Meteorology, vol. 69, no. 1-2, pp. 85–109, 1994. View at Publisher · View at Google Scholar · View at Scopus
  11. Y. Xiaoling, Z. Jianhua, and X. Xiong, “A GEP-based method for quality control of surface temperature observations,” Journal of Tropical Meteorology, vol. 30, no. 6, pp. 1196–1200, 2014. View at Publisher · View at Google Scholar
  12. X. Xiong, X. Ye, and Y. Zhang, “A quality control method for surface hourly temperature observations via gene-expression programming,” International Journal of Climatology, 2017. View at Publisher · View at Google Scholar
  13. S. Karatas and L. Yalcin, “Data Quality management,” in Proceedings of the TECO 2005, https://www.wmo.int/pages/prog/www/IMOP/publications/IOM-82-TECO_2005/Posters/P3%2833%29_Turkey_5_Karatas.pdf.
  14. C. G. Wade, “A quality control program for surface mesometeorological data,” Journal of Atmospheric and Oceanic Technology, vol. 4, no. 3, pp. 435–453, 1987. View at Publisher · View at Google Scholar
  15. S. L. Barnes, “A technique for maximizing details in numerical weather map analysis,” Journal of Applied Meteorology, vol. 3, no. 4, pp. 396–409, 1964. View at Publisher · View at Google Scholar
  16. K. G. Hubbard, N. B. Guttman, J. You, and Z. Chen, “An improved QC process for temperature in the daily cooperative weather observations,” Journal of Atmospheric and Oceanic Technology, vol. 24, no. 2, pp. 206–213, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. J. You and K. G. Hubbard, “Quality control of weather data during extreme events,” Journal of Atmospheric and Oceanic Technology, vol. 23, no. 2, pp. 184–197, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. H. Wang and Y. Liu, “Comprehensive consistency method of data quality controlling with its application to daily temperature,” Journal of Applied Meteorological Science, vol. 23, no. 1, pp. 69–76, 2012. View at Google Scholar
  19. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Neshat, G. Sepidnam, M. Sargolzaei, and A. N. Toosi, “Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications,” Artificial Intelligence Review, vol. 42, no. 4, pp. 965–997, 2014. View at Publisher · View at Google Scholar · View at Scopus
  21. K. G. Hubbard and J. You, “Sensitivity analysis of quality assurance using the spatial regression approach—a case study of the maximum/minimum air temperature,” Journal of Atmospheric and Oceanic Technology, vol. 22, no. 10, pp. 1520–1530, 2005. View at Publisher · View at Google Scholar · View at Scopus
  22. K. G. Hubbard, S. Goddard, W. D. Sorensen, N. Wells, and T. T. Osugi, “Performance of quality assurance procedures for an applied climate information system,” Journal of Atmospheric and Oceanic Technology, vol. 22, no. 1, pp. 105–112, 2005. View at Publisher · View at Google Scholar · View at Scopus
  23. R. E. Schapire, “The strength of weak learnability,” Machine Learning, vol. 5, no. 2, pp. 197–227, 1990. View at Publisher · View at Google Scholar · View at Scopus
  24. C. Strobl, A.-L. Boulesteix, T. Kneib, T. Augustin, and A. Zeileis, “Conditional variable importance for random forests,” BMC Bioinformatics, vol. 9, no. 1, article 307, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. J. E. Nash and J. V. Sutcliffe, “River flow forecasting through conceptual models part I—a discussion of principles,” Journal of Hydrology, vol. 10, no. 3, pp. 282–290, 1970. View at Publisher · View at Google Scholar · View at Scopus
  26. C. J. Willmott, “Some comments on the evaluation of model performance,” Bulletin of the American Meteorological Society, vol. 63, no. 11, pp. 1309–1313, 1982. View at Publisher · View at Google Scholar
  27. I. Chollett, F. E. Müller-Karger, S. F. Heron, W. Skirving, and P. J. Mumby, “Seasonal and spatial heterogeneity of recent sea surface temperature trends in the Caribbean Sea and southeast Gulf of Mexico,” Marine Pollution Bulletin, vol. 64, no. 5, pp. 956–965, 2012. View at Publisher · View at Google Scholar · View at Scopus
  28. R. Hill, U. Schreiber, R. Gademann, A. W. D. Larkum, M. Kühl, and P. J. Ralph, “Spatial heterogeneity of photosynthesis and the effect of temperature-induced bleaching conditions in three species of corals,” Marine Biology, vol. 144, no. 4, pp. 633–640, 2004. View at Publisher · View at Google Scholar · View at Scopus
  29. W. R. Tobler, “A computer movie simulating urban growth in the detroit region,” Economic Geography, vol. 46, supplement 1, pp. 234–240, 1970. View at Publisher · View at Google Scholar
  30. M. Tiefelsdorf and B. Boots, “The exact distribution of Moran’s I,” Environment and Planning A, vol. 27, no. 6, pp. 985–999, 1995. View at Publisher · View at Google Scholar
  31. T. V. Perneger, “What’s wrong with Bonferroni adjustments,” British Medical Journal, vol. 316, no. 7139, article 1236, 1998. View at Publisher · View at Google Scholar · View at Scopus