Atmosphere (대기)

Korean Meteorological Society (한국기상학회)
권호 표지
DOI QR코드

DOI QR Code

Effects of Different Averaging Operators on the Urban Turbulent Fluxes

평균 방법이 도시 난류 플럭스에 미치는 영향

  • Kwon, Tae Heon (Weather Information Service Engine, Center for Atmospheric Science & Earthquake Research) ;
  • Park, Moon-Soo (Weather Information Service Engine, Center for Atmospheric Science & Earthquake Research) ;
  • Yi, Chaeyeon (Weather Information Service Engine, Center for Atmospheric Science & Earthquake Research) ;
  • Choi, Young Jean (Weather Information Service Engine, Center for Atmospheric Science & Earthquake Research)
  • 권태헌 (기상기술개발원 차세대도시농림융합기상사업단) ;
  • 박문수 (기상기술개발원 차세대도시농림융합기상사업단) ;
  • 이채연 (기상기술개발원 차세대도시농림융합기상사업단) ;
  • 최영진 (기상기술개발원 차세대도시농림융합기상사업단)
  • Received : 2014.01.17
  • Accepted : 2014.03.18
  • Published : 2014.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

The effects of different averaging operators and atmospheric stability on the turbulent fluxes are investigated using the vertical velocity, air temperature, carbon dioxide concentration, and absolute humidity data measured at 10 Hz by a 3-dimensional sonic anemometer and an open-path $CO_2/H_2O$ infrared gas analyzer installed at a height of 18.5 m on the rooftop of the Jungnang KT building located at a typical residential area in Seoul, Korea. For this purpose, 7 different averaging operators including block average, linear regression, and moving averages during 100 s, 300 s, 600 s, 900 s, and 1800 s are considered and the data quality control procedure such as physical limit check and spike removal is also applied. It is found that as the averaging interval becomes shorter, turbulent fluxes computed by the moving average become smaller and the ratios of turbulent fluxes computed by the 100 s moving average to the fluxes by the 1800 s moving average under unstable stability are smaller than those under neutral stability. The turbulent fluxes computed by the linear regression are 85~92% of those computed by the 1800 s moving average and nearly the same as those computed by 900 s moving average, implying that the adequate selection of an averaging operator and its interval will be very important to estimate more accurate turbulent fluxes at urban area.

Keywords

References

  1. Aubinet, M., T. Vesala, and D. Papale, 2012: Eddy covariance A practical guide to measurement and data analysis. Springer, 438 pp.
  2. Baldocchi, D., and Coauthors, 2001: Fluxnet: A new tool to study the temporal and spatial variability of ecosystem- scale carbon dioxide, water vapor, and energy flux densities. Bull. Amer. Meteor. Soc., 82, 2415-2434. https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2
  3. Batchvarova, E., S.-E. Gryning, and C. B. Hasager, 2001: Regional fluxes of momentum and sensible heat over a sub-arctic landscape during late winter. Bound.- Layer Meteor., 99, 489-507. https://doi.org/10.1023/A:1018982711470
  4. Berger, B. W., J. D. Kenneth, C. Yi, P. S. Bakwin, and C. L. Zhao, 2001: Long-term carbon dioxide fluxes from a very tall tower in a northern forest: flux measurement methodology. J. Atmos. Oceanic Technol., 18, 529-542. https://doi.org/10.1175/1520-0426(2001)018<0529:LTCDFF>2.0.CO;2
  5. Clement, R. J., 2004: Mass and energy exchange of a plantation forest in Scotland using micrometeorological methods (PhD Thesis). University of Edinburgh, Edinburgh, 597 pp.
  6. Culf, A. D., 2000: Examples of the effects of different averaging methods on carbon dioxide fluxes calculated using the eddy correlation method. Hydrol. Earth Syst. Sci., 4, 193-198. https://doi.org/10.5194/hess-4-193-2000
  7. Finnigan, J., R. Clement, Y. Malhi, R. Leuning, and H. A. Cleugh, 2003: A re-evaluation of long-term flux measurement techniques Part I: Averaging and coordinate rotation. Bound.-Layer Meteor., 107, 1-48. https://doi.org/10.1023/A:1021554900225
  8. Foken, T., and B. Wichura, 1996: Tools for quality assessment of surface-based flux measurements. Agri. Forest Meteor., 78, 83-105. https://doi.org/10.1016/0168-1923(95)02248-1
  9. Gash, J. H. C., and A. D. Culf, 1996: Applying a linear detrend to eddy correlation data in real time. Bound.- Layer Meteor., 107, 1-48.
  10. Gockede, M., and Coauthors, 2008: Quality control of CarboEurope flux data - Part 1: coupling footprint analyses with flux data quality assessment to evaluate sites in forest ecosystems. Biogeosciences, 5, 433-450. https://doi.org/10.5194/bg-5-433-2008
  11. Grimmond, C. S. B., and T. R. Oke, 1999: Aerodynamic properties of urban areas derived from analysis of surface form. J. Appl. Meteorol., 38, 1262-1292. https://doi.org/10.1175/1520-0450(1999)038<1262:APOUAD>2.0.CO;2
  12. Gryning, S.-E., and E. Batchvarova, 2009: Measuring meteorology in urban areas - some progress and many problems. In Meteorological and air quality models for urban areas (eds. by Baklanov et al.), 125-131.
  13. Hong, J., H. Kwon, J.-H. Lim, Y.-H. Byun, J. Lee, and J. Kim, 2009: Standardization of KoFlux eddy-covariance data processing. Korean J. Agri. Forest Meteor., 11, 19-26. https://doi.org/10.5532/KJAFM.2009.11.1.019
  14. Kaimal, J. C., and J. J. Finnigan, 1994: Atmospheric boundary layer flows Their structure and Measurement. Oxford University Press, 289 pp.
  15. Kanda, M., A. Inagaki, M. O., Letzel, S. Raasch, and T. Watanabe, 2004: LES study of the energy imbalance problem with eddy covariance fluxes. Bound.-Layer Meteor., 110, 381-404. https://doi.org/10.1023/B:BOUN.0000007225.45548.7a
  16. Kwon, H., and J. Kim, 2010: KoFlux's progress: background, status and direction. Korean J. Agri. Forest Meteor., 12, 241-263. https://doi.org/10.5532/KJAFM.2010.12.4.241
  17. Lee, X., W. Massman, and B. Law, 2004: Handbook of micrometeorology A guide for surface flux measurement and analysis. Kluwer Academic Publishers, 250pp.
  18. 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. https://doi.org/10.14191/Atmos.2013.23.3.307
  19. Lim, H.-J., and Y.-H. Lee, 2008: Processing and quality control of flux data at Gwangneung forest. Korean J. Agri. Forest Meteor., 10, 82-93. https://doi.org/10.5532/KJAFM.2008.10.3.082
  20. Liu, H. Z., J. W. Feng, L. Jarvi, and T. Vesala, 2012: Eddy covariance measurements of $CO_{2}$ and energy fluxes in the city of Beijing. Atmos. Chem. Phys. Discuss, 12, 7677-7704. https://doi.org/10.5194/acpd-12-7677-2012
  21. Macdonald, R. W., R. F. Griffiths, and D. J. Hall, 1998: An improved method for estimation of surface roughness of obstacle arrays. Atmos. Environ., 32, 1857-1864. https://doi.org/10.1016/S1352-2310(97)00403-2
  22. Nordbo, A., L. Jarvi, and T. Vesala, 2012: Revised covariance flux calculation methodologies - effect on urban energy balance. Tellus B, 64, 18184. https://doi.org/10.3402/tellusb.v64i0.18184
  23. Panofsky, H. A., and J. A. Dutton, 1984: Atmospheric turbulence Models and Methods for Engineering Applications. John Wiley & Sons, 397 pp.
  24. Park, M.-S., S. J. Joo, and C. S. Lee, 2013: Effects of an urban park and residential area on the atmospheric $CO_{2}$ concentration and flux in Seoul, Korea. Adv. Atmos. Sci., 30, 503-514. https://doi.org/10.1007/s00376-012-2079-7
  25. Park, M.-S., S. J. Joo, and S.-U. Park, 2014: Carbon dioxide concentration and flux at the urban residential area in Seoul, Korea. Adv. Atmos. Sci. (in press).
  26. Rannik, U, and T. Vesala, 1999: Autoregressive filtering versus linear detrending in estimation of fluxes by the eddy covariance method. Bound.-Layer Meteor., 91, 259-280. https://doi.org/10.1023/A:1001840416858
  27. Sakai, R. K., D. R. Fitzjarrald, and K. E. Moore, 2001: Importance of low-frequency contributions to eddy fluxes observed over rough surfaces. J. Appl. Meteor., 40, 2178-2192. https://doi.org/10.1175/1520-0450(2001)040<2178:IOLFCT>2.0.CO;2
  28. Song, T., Y. Sun, and Y. Wang, 2013: Multilevel measurements of fluxes and turbulence over an urban landscape in Beijing. Tellus B, 65, 20421. https://doi.org/10.3402/tellusb.v65i0.20421
  29. Tennekes, H., and J. L. Lumley, 1972: A first course in turbulence. The MIT Press, 300 pp.
  30. Vickers, D., and L. Mahrt, 1997: Quality control and flux sampling problems for tower and aircraft data. J. Atmos. Oceanic Technol., 14, 512-526. https://doi.org/10.1175/1520-0426(1997)014<0512:QCAFSP>2.0.CO;2
  31. Yuan, R., M. Kang, S. Park, J. Hong, D. Lee, and J. Kim, 2007: The effects of coordinate rotation on the eddy covariance flux estimation in a hilly KoFlux forest catchment. Korean J. Agri. Forest Meteor., 9, 100-108. https://doi.org/10.5532/KJAFM.2007.9.2.100

Cited by

  1. Development of a Quality Check Algorithm for the WISE Pulsed Doppler Wind Lidar vol.26, pp.3, 2016, https://doi.org/10.14191/Atmos.2016.26.3.461
  2. Analysis of Meteorological and Radiation Characteristics using WISE Observation Data vol.39, pp.1, 2018, https://doi.org/10.5467/JKESS.2018.39.1.89
  3. High-resolution urban observation network for user-specific meteorological information service in the Seoul Metropolitan Area, South Korea vol.10, pp.4, 2017, https://doi.org/10.5194/amt-10-1575-2017
  4. Meteorological Characteristics and Assessment of the Effect of Local Emissions during High PM10 Concentration in the Seoul Metropolitan Area vol.13, pp.2, 2019, https://doi.org/10.5572/ajae.2019.13.2.117