대기 (Atmosphere)

  • 제26권3호
  • /
  • Pages.483-492
  • /
  • 2016
  • /
  • 1598-3560(pISSN)
  • /
  • 2288-3266(eISSN)
한국기상학회 (Korean Meteorological Society)
권호 표지
DOI QR코드

DOI QR Code

주간에 두 타워로부터 관측된 에디 공분산 자료의 확률 오차의 추정

Estimation of the Random Error of Eddy Covariance Data from Two Towers during Daytime

  • 임희정 (경북대학교 천문대기과학과) ;
  • 이영희 (경북대학교 천문대기과학과) ;
  • 조창범 (국립기상과학원 응용기상연구과) ;
  • 김규랑 (국립기상과학원 응용기상연구과) ;
  • 김백조 (국립기상과학원 응용기상연구과)
  • Lim, Hee-Jeong (Department of Astronomy and Atmospheric Sciences, Kyungpook National University) ;
  • Lee, Young-Hee (Department of Astronomy and Atmospheric Sciences, Kyungpook National University) ;
  • Cho, Changbum (National Institute of Meteorological Sciences) ;
  • Kim, Kyu Rang (National Institute of Meteorological Sciences) ;
  • Kim, Baek-Jo (National Institute of Meteorological Sciences)
  • 투고 : 2016.05.09
  • 심사 : 2016.07.08
  • 발행 : 2016.09.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

We have examined the random error of eddy covariance (EC) measurements on the basis of two-tower approach during daytime. Two EC towers were placed on the grassland with different vegetation density near Gumi-weir. We calculated the random error using three different methods. The first method (M1) is two-tower method suggested by Hollinger and Richardson (2005) where random error is based on differences between simultaneous flux measurements from two towers in very similar environmental conditions. The second one (M2) is suggested by Kessomkiat et al. (2013), which is extended procedure to estimate random error of EC data for two towers in more heterogeneous environmental conditions. They removed systematic flux difference due to the energy balance deficit and evaporative fraction difference between two sites before determining the random error of fluxes using M1 method. Here, we introduce the third method (M3) where we additionally removed systematic flux difference due to available energy difference between two sites. Compared to M1 and M2 methods, application of M3 method results in more symmetric random error distribution. The magnitude of estimated random error is smallest when using M3 method because application of M3 method results in the least systematic flux difference between two sites among three methods. An empirical formula of random error is developed as a function of flux magnitude, wind speed and measurement height for use in single tower sites near Nakdong River. This study suggests that correcting available energy difference between two sites is also required for calculating the random error of EC data from two towers at heterogeneous site where vegetation density is low.

키워드

참고문헌

  1. Aubinet, M., T. Vesala, and D. Papale, 2012: Eddy covariance. A practical guide to measurement and data analysis. Springer, 438 pp.
  2. Choi, S.-Y., Y. Lee, C. Cho, and K.-R. Kim, 2015: Analysis of local wind induced by surface heterogeneity and sloping terrain near nakdong river. Asia-Pac. J. Atmos. Sci., 51, 245-257.
  3. Finkelstein, P. L., and P. F. Sims, 2001: Sampling error in eddy correlation flux measurements. J. Gephys. Res., 106, 3503-3509. https://doi.org/10.1029/2000JD900731
  4. Foken, T., 2008: The energy balance closure problem: an overview. Ecol. Appl., 18, 1351-1367. https://doi.org/10.1890/06-0922.1
  5. Hollinger, D. Y., and A. D. Richardson, 2005: Uncertainty in eddy covariance measurements and its application to physiological models. Tree Physio., 25, 873-885. https://doi.org/10.1093/treephys/25.7.873
  6. Hu, Q., S. Feng, and G. Schaefer, 2002: Quality control for USDA NRCS SM-ST Network soil temperatures: A method and a dataset. J. Appl. Meteor., 41, 607-619. https://doi.org/10.1175/1520-0450(2002)041<0607:QCFUNS>2.0.CO;2
  7. Kahng, K., H.-J. Koo, J.-Y. Byon, Y.-S. Park, and H.-S. Jung, 2013: Comparison of surface fluxes based on landuse characteristics near Gangjeong-Goryeong Wier of the Nakdong River. J. Korean Earth Sci. Soc., 34, 561-574. https://doi.org/10.5467/JKESS.2013.34.6.561
  8. Kessomkiat, W., H.-J. Hendricks-Franssen, A. Graf, and H. Vereecken, 2013: Estimating random errors of eddy covariance data: An extended two-tower approach. Agric. Forest Meteorol., 171, 203-219.
  9. Lee, Y.-H., B. Lee, K. Kahng, S.-J. Kim, and S.-O. Hong, 2013: Quality control and characteristic of eddy covariance data in the region of Nakdong River. Atmosphere, 23, 307-320 (in Korean with English abstract). https://doi.org/10.14191/Atmos.2013.23.3.307
  10. Lenschow, D. H., J. Mann, and L. Kristensen, 1994: How long is long enough when measuring fluxes and other turbulence statistics? J. Atmos. Oceanic. Technol., 11, 661-673. https://doi.org/10.1175/1520-0426(1994)011<0661:HLILEW>2.0.CO;2
  11. Mahrt, L., 1998: Flux sampling errors from aircraft and towers. J. Atmos. Oceanic Technol., 11, 661-673.
  12. Mann, J., and D. H. Lenschow, 1994: Errors in airborne flux measurements. J. Geophys. Res., 99, 14519-14526. https://doi.org/10.1029/94JD00737
  13. Massman, W. J., and X. Lee, 2002: Eddy covariance flux corrections and uncertainties in long term studies of carbon and energy. Agric. Forest Meteorol., 113, 121-144. https://doi.org/10.1016/S0168-1923(02)00105-3
  14. Mauder, M., M. Cuntz, C. Drue, A. Graf, C. Rebmann, H. P. Schmid, M. Schmidt, and R. Steinbrecher, 2013: A strategy for quality and uncertainty assessment of long-term eddy-covariance measurements. Agric. Forest Meteorol., 169, 122-135. https://doi.org/10.1016/j.agrformet.2012.09.006
  15. National Institute of Meteorological Research, 2011, Hydrometeorology research for the test-bed region (I). National Institute of Meteorological Research, 625 pp. (in Korean).
  16. National Institute of Meteorological Research, 2012: Hydrometeorology research for the test-bed region (II). National Institute of Meteorological Research, 690 pp (in Korean).
  17. Oleson, K. W., and Coauthors, 2004: Technical Description of the Community Land Model (CLM). NCAR/ TN-461+STR, 173 pp.
  18. Richardson, A. D., and Coauthors, 2006: A multi-site analysis of random error in tower-based measurements of carbon and energy fluxes. Agric. Forest Meteorol., 136, 1-18. https://doi.org/10.1016/j.agrformet.2006.01.007
  19. Schmid, H. P., 1994: Source areas for scalar and scalar fluxes. Bound.-Layer Meteor., 67, 293-318. https://doi.org/10.1007/BF00713146
  20. Taylor, J. R., 1997: An introduction to error analysis. 2nd ed. University Science Books, 327 pp.
  21. Twine, T. E., W. P. Kustas, J. M. Norman, D. R. Cook, P. R. Houser, T. P. Meyers, J. H. Prueger, P. J. Starks, and M. L. Wesely, 2000: Correcting eddy-covariance flux underestimates over a glassland. Agric. Forest Meteorol., 103, 279-300. https://doi.org/10.1016/S0168-1923(00)00123-4
  22. Webb, E. K., G. I. Pearman, and R. Leuning, 1980: Correction of flux measurements for density effects due to heat and water vapor transfer. Quart. J. Roy. Meteor. Soc., 106, 85-100. https://doi.org/10.1002/qj.49710644707
  23. Wilks, D. S., 1995: Statistical methods in the atmospheric sciences. Academic Press, 467 pp.