Atmosphere (대기)

Korean Meteorological Society (한국기상학회)
권호 표지
DOI QR코드

DOI QR Code

Validation of Quality Control Algorithms for Temperature Data of the Republic of Korea

한국의 기온자료 품질관리 알고리즘의 검증

  • Received : 2012.02.24
  • Accepted : 2012.04.10
  • Published : 2012.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

This study is aimed to validate errors for detected suspicious temperature data using various quality control procedures for 61 weather stations in the Republic of Korea. The quality control algorithms for temperature data consist of four main procedures (high-low extreme check, internal consistency check, temporal outlier check, and spatial outlier check). Errors of detected suspicious temperature data are judged by examining temperature data of nearby stations, surface weather charts, hourly temperature data, daily precipitation, and daily maximum wind direction. The number of detected errors in internal consistency check and spatial outlier check showed 4 days (3 stations) and 7 days (5 stations), respectively. Effective and objective methods for validation errors through this study will help to reduce manpower and time for conduct of quality management for temperature data.

Keywords

References

  1. 이정덕, 최영은, 박창용, 2010: 한국의 기온자료 알고리즘 개발. 기후연구, 5(2), 162-174.
  2. 박창용, 최영은, 2011: 고품질의 기온자료를 이용한 연.계절 평균기온의 변화에 관한 연구, 대한지리학회지, 46(1), 20-35.
  3. Brunet, M., Saladie, O., Jones, P. D., Sigro, J., Aguilar, E., Moberg, A., Lister, D., Walther, A., Lopez, D., and C. Almarza, 2006: The development of a new dataset of Spanish Daily Adjusted Temperature Series (SDATS) (1850-2003), Int. J. Climatol., 26, 1777-1802. https://doi.org/10.1002/joc.1338
  4. Brunet, M., Saladie, O., Jones, P., Sigro, J., Aguilar, E., Moberg, A., Lister, D., Walther, A., and C. Almarza, 2008: A case-study/guidance on the development of long-term daily adjusted temperature datasets, Climate Data and Monitoring WCDMP-No. 66, World Meteorological Organization WMO-TD 1425, WMO, Geneva.
  5. Feng, S., and W. Qian, 2004: Quality control of daily meteorological data in China, 1951-2000: A new dataset, Int. J. Climatol., 24, 853-870. https://doi.org/10.1002/joc.1047
  6. Karl, T. R., Nicholls, N., and A. Ghazi, 1999: CLIVAR/GCOS/WMO Workshop on indices and indicators for climate extremes, Climatic Change, 42, 3-7. https://doi.org/10.1023/A:1005491526870
  7. Kenneth G. Hubbard and M.V.K. Sivakumar(Eds.). 2001. Automated Weather Stations for Applications in Agriculture and Water Resources Management: Current Use and Future Perspectives. Proceedings of an International Workshop[ held in Lincoln, Nebraska, USA, 6-10 March 2000. Lincoln, Nebraska: High Plains Climate Center and Geneva, Switzerland: World Meteorological Organization.
  8. Klein-Tank, A. M. G., Wijngaard, J. B., Koonnen, G. P., Böohm, R., Demareee, G., Gocheva, A., Mileta, M., Pashiardis, S., Hejkrlik, L., Kern-Hansen, C., Heino, R., Bessemoulin, P., Muuller-Westermeier, G., Tzanakou, M., Salíia, S., Paalsdoottir, T., Fitzgerald, D., Rubin, S., Capaldo, M., Maugeri, M., Leitass, A., Bukantis, A., Aberfeld, R., Van Encelen, A. F. V., Forland, E., Mietus, M., Coelho, F., Mares, C., Razuvaev, V., Nieplova, E., Cegnar, T., Loopez, J. A., Dahlstroom, B., Moberg, A., Kirchhofer, W., Ceylan, A., Pachaliuk, O., Alexander, L. V., and P. Petrovic, 2002: Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment, Int. J. Climatol., 22, 1441-1453. https://doi.org/10.1002/joc.773
  9. Lanzante, J. R., 1996: Resistant, robust and nonparametric techniques for the analysis of climate data: theory and examples, including applications to historical radiosonde station data, Int. J. Climatol., 16, 1197-1226. https://doi.org/10.1002/(SICI)1097-0088(199611)16:11<1197::AID-JOC89>3.0.CO;2-L
  10. NCDC, 2002: United States climate normals, 1971-2000 inhomogeneity adjustment methodology, Asheville, NC, USA.
  11. Nicholls, N., and W. Murray, 1999: Workshop on indices and indicators for climate extremes, Climatic Change, 42, 23-29. https://doi.org/10.1023/A:1005495627778
  12. Peterson, T. C., and R. S. Vose, 1997: An overview of the global historical climatology network temperature database, Bull. Amer. Meteor. Soc., 78, 2837-2849. https://doi.org/10.1175/1520-0477(1997)078<2837:AOOTGH>2.0.CO;2
  13. Peterson, T. C., Easterling, D. R., Karl, T. R., Groisman, P., Nicholls, N., Plummer, N., Torok, S. , Auer, I., Boehm, R., Gullett, D., Vincent, L., Heino, R., Tuomenvirta, H., Mestre, O., Szentimrey, T., Salinger, J., Forland, E. J., Hanssen-Bauer, I., Alexandersson, H., Jones, P., and D. Parker, 1998a: Homogeneity adjustments of IN SITU atmospheric climate data: A Review, Int. J. Climatol., 18, 1493-1517. https://doi.org/10.1002/(SICI)1097-0088(19981115)18:13<1493::AID-JOC329>3.0.CO;2-T
  14. Peterson, T. C., Vose, R., Schmoyer R., and V. Razuvaev, 1998b: Global historical climatology network (GHCN) quality control of monthly temperature data, Int. J. Climatol., 18, 1169-1179. https://doi.org/10.1002/(SICI)1097-0088(199809)18:11<1169::AID-JOC309>3.0.CO;2-U
  15. Reek, T., Doty, S. T., and T. W. Owen, 1992: A deterministic approach to the validation of historical daily temperature and precipitation data from the cooperative network, Bull. Amer. Meteor. Soc., 73, 753-762. https://doi.org/10.1175/1520-0477(1992)073<0753:ADATTV>2.0.CO;2
  16. Vincent, L. A., Zhang, X., Bonsal, B. R., and W. D. Hogg, 2002: Homogenization of daily temperatures over Canada, J. Climate, 15, 1322-1334. https://doi.org/10.1175/1520-0442(2002)015<1322:HODTOC>2.0.CO;2
  17. WMO, 2003, Guidance on metadata and homogenization, WMO-TD 1186, WMO, Geneva, Switzerland.
  18. WMO, 2008, A case-study/guidance on the development of long-term daily adjusted temperature datasets, Climate Data and Monitoring WCDMP-No. 66, World Meteorological Organization WMO-TD 1425, WMO, Geneva, Switzerland.

Cited by

  1. Large-Scale Circulation Anomalies Associated with Extreme Heat in South Korea and Southern–Central Japan vol.32, pp.10, 2019, https://doi.org/10.1175/JCLI-D-18-0485.1