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Korean Meteorological Society (한국기상학회)
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Surface Micro-Climate Analysis Based on Urban Morphological Characteristics: Temperature Deviation Estimation and Evaluation

도시의 지표형태학적 특성에 기반한 지면미기후 분석: 기온추정 및 평가

  • Yi, Chaeyeon (Weather Information Service Engine, Hankuk University of Foreign Studies) ;
  • An, Seung Man (Korea Research Institute for Human Settlements) ;
  • Kim, KyuRang (National Institute Meteorological Sciences) ;
  • Kwon, Hyuk-gi (Weather Information Service Engine, Hankuk University of Foreign Studies) ;
  • Min, Jae-Sik (Weather Information Service Engine, Hankuk University of Foreign Studies)
  • 이채연 (한국외국어대학교 차세대도시농림융합기상사업단) ;
  • 안승만 (국토연구원 주택.토지연구본부) ;
  • 김규랑 (국립기상과학원 응용기상연구과) ;
  • 권혁기 (한국외국어대학교 차세대도시농림융합기상사업단) ;
  • 민재식 (한국외국어대학교 차세대도시농림융합기상사업단)
  • Received : 2016.06.13
  • Accepted : 2016.09.19
  • Published : 2016.09.30
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Abstract

Air temperature deviation (ATD) is one of major indicators to represent spatial distribution of urban heat island (UHI), which is induced from the urbanization. The purpose of this study is to evaluate the accuracy of air temperature deviation about Climate Analysis Seoul (CAS) workbench, which had developed by National Institute Meteorological Science and TU Berlin. Comparison and correlation analysis for CAS ATD including meso-scale air temperature deviation, local-scale air temperature deviation, total air temperature deviation, surface heat flux deviation, cold air production deviation among meso-scale numerical modelling variable in 'Seoul Region', micro-scale numerical modelling in 'Detail Region', and CAS workbench variable using observation data in ground stations. Comparison between night time OBS ATD and CAS ATD show that have most close values. Most of observations ($dT_{max}$ and $dT_{min}$) have highly positive ($dT_{SHP}$, $dT_{CA}$, MD, TD, $f_{BS}$, $f_{US}$, $f_{WS}$, $h_B$) and negative ($f_{VS}$, $f_{TV}$, $h_V$, Z) correlations. However, CAS workbench needs further improvement of both observational framework and analytical framework to resolve the problems; (1) night time OBS ATD of has closer values in compare with at high rise mountain area and (2) correlations are very dependable to meteorological scale.

Keywords

References

  1. An, S. M., K. S. Lee, and C. Yi, 2015: Building wind corridor network using roughness length. J. Korean Inst. Landsc. Archit., 43, 101-113. https://doi.org/10.9715/KILA.2015.43.3.101
  2. An, S. M., H. G. Son, K.-S. Lee, and C. Yi, 2016: A Study of the urban tree canopy mean radiant temperature mitigation estimation. J. Korean Inst. Landsc. Archit., 44, 93-106. https://doi.org/10.9715/KILA.2016.44.1.093
  3. Aoyagi, T., and N. Seino, 2011: A square prism urban canopy scheme for the NHM and its evaluation on summer conditions in the Tokyo metropolitan area, Japan. J. Appl. Meteor. Climatol., 50, 1476-1496. https://doi.org/10.1175/2011JAMC2489.1
  4. Bhang, K. J., and S.-S. Park, 2009: Evaluation of the surface temperature variation with surface settings on the urban heat island in Seoul, South Korea using Landsat-7 ETM+ and SPOT. IEEE Geosci. Remote Sens. Lett., 6, 708-712. https://doi.org/10.1109/LGRS.2009.2023825
  5. Chow, W. T., R. L. Pope, C. A. Martin, and A. J. Brazel, 2011: Observing and modeling the nocturnal park cool island of an arid city: horizontal and vertical impacts. Theor. Appl. Climatol., 103, 197-211. https://doi.org/10.1007/s00704-010-0293-8
  6. Christen, A., and R. Vogt, 2004: Energy and radiation balance of a Central European City. Int. J. Climatol., 24, 1395-1421. https://doi.org/10.1002/joc.1074
  7. Eum, J. H., D. Scherer, U. Fehrenbach, and J. H. Woo, 2011: Development of an urban landcover classification scheme suitable for representing climatic conditions in a densely built-up Asian megacity. Landscape. Urban Plan., 103, 362-371. https://doi.org/10.1016/j.landurbplan.2011.08.010
  8. Grimmond, C. S. B., and Coauthors, 2010: Climate and more sustainable cities: climate information for improved planning and management of cities (producers/ capabilities perspective). Environ. Sci., 1, 247-274.
  9. Hong, J. W., J. K. Hong, S. E. Lee, and J. Lee, 2013: Spatial distribution of Urban Heat Island based on Local Climate Zone of Automatic Weather Station in Seoul metropolitan area. Atmosphere, 23, 413-424 (in Korean with English abstract). https://doi.org/10.14191/Atmos.2013.23.4.413
  10. Ihaka, R., 1998: R: Past and future history. Comput. Sci. Stat., 392-396.
  11. Janicke, B., F. Meier, F. Lindberg, S. Schubert, and D. Scherer, 2016: Towards city-wide, building-resolving analysis of mean radiant temperature. Urban Climate, 15, 83-98. https://doi.org/10.1016/j.uclim.2015.11.003
  12. Jee, J.-B., and Y.-J. Choi, 2014: Conjugation of Landsat data for analysis of the land surface properties in capital Area. Korean Earth Sci. Soc., 35, 54-68. https://doi.org/10.5467/JKESS.2014.35.1.54
  13. Jee, J.-B., M. Jang, C. Yi, I. S. Zo, B. Y. Kim, M. S. Park, and Y. J. Choi, 2016: Sensitivity analysis of the highresolution WISE-WRF model with the use of surface roughness length in Seoul metropolitan areas. Atmosphere, 26, 111-126 (in Korean with English abstract). https://doi.org/10.14191/Atmos.2016.26.1.111
  14. Kim, K. R., C. Yi, J.-S. Lee, F. Meier, B. Jaenicke, U. Fehrenbach, and D. Scherer, 2014. BioCAS: Biometeorological climate impact assessment system for buildingscale impact assessment of heat-stress related mortality. Die Erde, 145, 62-79.
  15. Kim, Y. H., and J. J. Baik, 2002: Maximum urban heat island intensity in Seoul. J. Appl. Meteorol., 41, 651-659. https://doi.org/10.1175/1520-0450(2002)041<0651:MUHIII>2.0.CO;2
  16. Kusaka, H., H. Kondo, Y. Kikegawa, and F. Kimura, 2001: A simple single-layer urban canopy model for atmospheric models: Comparison with multi-layer and slab models. Bound.-Layer Meteor., 101, 329-358. https://doi.org/10.1023/A:1019207923078
  17. Kwon, H.-G., H.-J. Yang, C. Yi, Y.-H. Kim, and Y.-J. Choi, 2015: Urban climate impact assessment reflecting urban planning scenarios: connecting green network across the north and south in Seoul. J. Environ. Impact Assess., 24, 134-153. https://doi.org/10.14249/eia.2015.24.2.134
  18. Lee, C., C. Muller, D. Young, E. Warren, C. S. B. Grimmond, X. Cai, and E. Ferranti, 2015: Urban Meteorological Networks: The urban climatologists panacea? Proc. of the International Conference of Urban Climate.
  19. Lindberg, F., S. Onomura, and C. S. B. Grimmond, 2016: Influence of ground surface characteristics on the mean radiant temperature in urban areas. Int. J. Biometeorol., 60, 1439-1452, doi:10.1007/s00484-016-1135-x.
  20. Martilli, A., A. Clappier, and M. W. Rotach, 2002: An urban surface exchange parameterisation for mesoscale models. Bound.-Layer Meteor., 104, 261-304. https://doi.org/10.1023/A:1016099921195
  21. Masson, V., 2000: A physically-based scheme for the urban energy budget in atmospheric models. Bound.-Layer Meteor., 94, 357-397. https://doi.org/10.1023/A:1002463829265
  22. Mochida, A., and I. Y. Lun, 2008: Prediction of wind environment and thermal comfort at pedestrian level in urban area. J. Wind Eng. Ind. Aerod., 96, 1498-1527. https://doi.org/10.1016/j.jweia.2008.02.033
  23. Mosimann, T., T. Frey, and P. Trute, 1999: Schutzgut Klima/Luft in der Landschaftsplanung. Inf. Dienst Nat. Schutz Niedersachs, 19, 201-276.
  24. Oke, T. R., 1982: The energetic basis of the urban heat island. Quart. J. Roy. Meteor. Soc., 108, 1-24.
  25. Oke, T. R., 1988: Street design and urban canopy layer climate. Energ. Buildings, 11, 103-113. https://doi.org/10.1016/0378-7788(88)90026-6
  26. Oke, T. R., 2002: Boundary layer climates. Routledge, 435 pp.
  27. Parlow, E., D. Scherer, and U. Fehrenbach, 2006: Regionale klimaanalyse der region Sudlicher Oberrhein (REKLISO). Wissenschaftlicher Abschlussbericht, erstellt im Auftrag des Regionalverbandes Sudlicher Oberrhein, Freiburg.
  28. Parlow, E., D. Scherer, and U. Fehrenbach , 2010: Klimaanalyse der Stadt Zurich (KLAZ). Umwelt und Gesundheitsschutz der Stadt Zurich. Abschlussbericht. 76S.
  29. Perego, S., 1999: Metphomod - a numerical mesoscalemodel for the simulation of regional photosmog in complex terrain: model description and application during Pollumet 1993 (Switzerland). Meterol. Atmos. Phys., 70, 43-69. https://doi.org/10.1007/s007030050024
  30. Ryu, Y. H., and J. J. Baik, 2012: Daytime local circulations and their interactions in the Seoul metropolitan area. J. Appl. Meteorol. Climatol., 52, 784-801.
  31. Salamanca, F., and A. Martilli, 2010: A new Building Energy Model coupled with an Urban Canopy Parameterization for urban climate simulations—part II. Validation with one dimension off-line simulations. Theor. Appl. Climatol., 99, 345-356. https://doi.org/10.1007/s00704-009-0143-8
  32. Scherer, D., U. Fehrenbach, H.-D. Beha, and E. Parlow, 1999: Improved concepts and methods in analysis and evaluation of the urban climate for optimizing urban planning processes. Atmos. Environ., 33, 4185-4193. https://doi.org/10.1016/S1352-2310(99)00161-2
  33. Scherer, D., U. Fehrenbach, T. Lakes, S. Lauf, F. Meier, and C. Schuster, 2013: Quantification of heat-stress related mortality hazard, vulnerability and risk in Berlin, Germany. Die Erde, 144, 238-259.
  34. Seoul Metropolitan Government (SMG), 2014: 2030 Seoul plan. Seoul Metropolitan Government, 225 pp.
  35. Seto, K. C., B. Guneralp, and L. R. Hutyra, 2012: Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. of the National Academy of Sciences, 109, 16083-16088.
  36. Stathopoulos, T., 2006: Pedestrian level winds and outdoor human comfort. J. Wind Eng. Ind. Aerod., 94 769-780. https://doi.org/10.1016/j.jweia.2006.06.011
  37. Stewart, I. D., and T. R. Oke, 2012: Local climate zones for urban temperature studies. Bull. Amer. Meteor. Soc., 93, 1879-1900. https://doi.org/10.1175/BAMS-D-11-00019.1
  38. Sukopp, H., R. Wittig, and H. P. Blume, 1993: Stadtokologie. Fischer, 402 pp.
  39. Voogt, J. A., and T. R. Oke, 2003: Thermal remote sensing of urban climates. Remote Sens. Environ., 86, 370-384. https://doi.org/10.1016/S0034-4257(03)00079-8
  40. Yi, C., J.-H. Eum, Y.-J. Choi, K. R. Kim, D. Scherer, U. Fehrenbach, and G.-H. Kim, 2011: Development of Climate Analysis Seoul (CAS) maps based on landuse and meteorological model. J. Korean Assoc. Geogr. Inf. Stud., 14, 12-25. https://doi.org/10.11108/kagis.2011.14.1.012
  41. Yi, C., S. M. An, K. R. Kim, Y.-J. Choi, and D. Scherer, 2012: Improvement of air temperature analysis by precise spatial data on a local-scale: a case study of Eunpyeong New-town in Seoul. J. Korean Assoc. Geogr. Inf. Stud., 15, 144-158. https://doi.org/10.11108/kagis.2012.15.1.144
  42. Yi, C., H.-G. Kwon, and Y.-J. Choi, 2014a: Utilizing climate information for the improvement of urban planning and environmental management. Proc. of the autumn meeting of J. Korean Meteor. Soc., 1-2.
  43. Yi, C., K. R. Kim, S. M. An, and Y.-J. Choi, 2014b: Impact of the local surface characteristics and the distance from the center of heat island to suburban areas on the night temperature distribution over the Seoul metropolitan area. J. Korean Assoc. Geogr. Inf. Stud., 17, 35-49. https://doi.org/10.11108/kagis.2014.17.1.035
  44. Yi, C., T. H. Kwon, M. S. Park, Y.-J. Choi, and S. M. An, 2015: A study on the roughness length spatial distribution in relation to the Seoul building morphology. J. Korean Meteor. Soc., 25, 235-247.
  45. Yi, C., K. R. Kim, S. M. An, Y.-J. Choi, A. Holtmann, B. Jaenicke, U. Fehrenbach, and D. Scherer, 2016: Estimating spatial patterns of air temperature at building resolving spatial resolution in Seoul, Korea. Int. J. Climatol., 36, 533-549. https://doi.org/10.1002/joc.4363
  46. Yu, C., and W. N. Hien, 2006: Thermal benefits of city parks. Energy Build., 38, 105-120. https://doi.org/10.1016/j.enbuild.2005.04.003

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