DOI QR코드

DOI QR Code

Effects of Network Density on Gridded Horizontal Distribution of Meteorological Variables in the Seoul Metropolitan Area

관측망 밀도가 기상 자료의 격자형 수평 분포에 미치는 영향

Kang, Minsoo;Park, Moon-Soo;Chae, Jung-Hoon;Min, Jae-Sik;Chung, Boo Yeon;Han, Seong Eui
강민수;박문수;채정훈;민재식;정보연;한성의

  • Received : 2019.03.31
  • Accepted : 2019.06.20
  • Published : 2019.06.30

Abstract

High-quality and high-resolution meteorological information is essential to reduce damages due to disastrous weather phenomena such as flash flood, strong wind, and heat/cold waves. There are many meteorological observation stations operated by Korea Meteorological Administration (KMA) in Seoul Metropolitan Area (SMA). Nonetheless, they are still not enough to represent small-scale weather phenomena like convective storm cells due to its poor resolution, especially over urban areas with high-rise buildings and complex land use. In this study, feasibilities to use additional pre-existing networks (e.g., operated by local government and private company) are tested by investigating the effects of network density on the gridded horizontal distribution of two meteorological variables (temperature and precipitation). Two heat wave event days and two precipitation events are chosen, respectively. And the automatic weather station (AWS) networks operated by KMA, local-government, and SKTechX in Incheon area are used. It is found that as network density increases, correlation coefficients between the interpolated values with a horizontal resolution of 350 m and observed data also become large. The range of correlation coefficients with respect to the network density shows large in nighttime rather than in daytime for temperature. While, the range does not depend on the time of day, but on the precipitation type and horizontal distribution of convection cells. This study suggests that temperature and precipitation sensors should be added at points with large horizontal inhomogeneity of land use or topography to represent the horizontal features with a resolution higher than 350 m.

Keywords

Automatic weather station (AWS);high-quality weather information;interpolation;network density;Seoul Metropolitan Area (SMA)

References

  1. Arnfield, A. J., 2003: Two decades of urban climate research: a review of turbulence, exchange of energy and water, and urban heat island. Int. J. Climatol., 23, 1-26. https://doi.org/10.1002/joc.859
  2. Arsenault, R., and F. Brissette, 2014: Determining the optimal spatial distribution of weather station networks for hydrological modeling purposes using RCM Datasets: an experimental approach. J. Hydrometeor., 15, 517-526, doi:10.1175/JHM-D-13-088.1. https://doi.org/10.1175/JHM-D-13-088.1
  3. Blumenfeld, K. A., and R. H. Skaggs, 2011: Using a high-density rain gauge network to estimate extreme rainfall frequencies in Minnesota. Applied Geography, 31, 5-11, doi:10.1016/j.apgeog.2010.03.013. https://doi.org/10.1016/j.apgeog.2010.03.013
  4. Chae, J.-H., M.-S. Park, and Y.-J. Choi, 2014: The WISE quality control system for integrated meteorological sensor data. Atmosphere, 24, 445-456, doi:10.14191/Atmos.2014.24.3.445 (in Korean with English abstract). https://doi.org/10.14191/Atmos.2014.24.3.445
  5. Demographia, 2018: Demographia world urban areas, Built up urban areas or world agglomerations. 14th Annual edition, demographia, 118 pp.
  6. Grimmond, C. S. B., 2006: Progress in measuring and observing the urban atmosphere. Theor. Appl. Climatol., 84, 3-22. https://doi.org/10.1007/s00704-005-0140-5
  7. Grimmond, C. S. B., and Coauthors, 2009: Urban surface energy balance models: Model characteristics and methodology for a comparison study. In A. Baklanov et al. Eds., Meteorological and Air Quality Models for Urban Areas, Springer, 97-123.
  8. Gubler, S., S. Hunziker, M. Begert, M. Croci-Maspoli, T. Konzelmann, S. Bronnimnann, C. Schwierz, C. Oria, and G. Rosas, 2017: The influence of station density on climate data homogenization. Int. J. Climatol., 37, 4670-4683, doi:10.1002/joc.5114. https://doi.org/10.1002/joc.5114
  9. Janis, M. J., K. G. Hubbard, and K. T. Redmond, 2002: Determining the Optimal Number of Stations for the United States Climate Reference Network Final Report. Southeast Regional Climate Center, Research Paper Series, 21 pp.
  10. Kim, E., I. Ra, K. H. Rhee, and C. S. Kim, 2014: Estimation of real-time flood risk on roads based on rainfall calculated by the revised method of missing rainfall. Sustainability, 6, 6418-6431, doi:10.3390/su6096418. https://doi.org/10.3390/su6096418
  11. Kim, M.-K., M.-S. Han, D.-H. Jang, S.-G. Baek, W.-S. Lee, Y.-H. Kim, and S. Kim, 2012: Production technique of observation grid data of 1km resolution. J. Climate Res., 7, 55-68 (in Korean with English abstract).
  12. Kim, M.-K., D.-H. Lee, and J. Kim, 2013: Production and validation of daily grid data with 1km resolution in South Korea. J. Climate Res., 8, 13-25 (in Korean with English abstract).
  13. KMA, 2006: Real-time quality control system for meteorological observation data (I) Application. 11-1360000-000206-01 Tech. Note 2006-2, 157 pp.
  14. Kuo, Y.-H., M. Skumanich, P. L. Haagenson, and J. S. Chang, 1985: The accuracy of trajectory models as revealed by the observing system simulation experiments. Mon. Wea. Rev., 113, 1852-1867. https://doi.org/10.1175/1520-0493(1985)113<1852:TAOTMA>2.0.CO;2
  15. Kuo, Y.-H., X. Zou, and W. Huang, 1998: The impact of Global Positioning System data on the prediction of an extratropical cyclone: an observing system simulation experiment. Dyn. Atmos. Oceans, 27, 439-470. https://doi.org/10.1016/S0377-0265(97)00023-7
  16. Lee, S.-H., K.-S. Lee, W.-C. Jin, and H.-K. Song, 2009: Effect of an urban park on air temperature differences in a central business district area. Landscape Ecol. Eng., 5, 183-191. https://doi.org/10.1007/s11355-009-0067-6
  17. Martinelli, L., T. P. Lin, and A. Matzarakis, 2015: Assessment of the influence of daily shadings pattern on human thermal comfort and attendance in Rome during summer period. Build. Environ., 92, 30-38, doi:10.1016/j.buildenv.2015.04.013. https://doi.org/10.1016/j.buildenv.2015.04.013
  18. Mehrjardi, R. T., M. Z. Jahromi, Sh. Mahmodi, and A. Heidari, 2008: Spatial Distribution of Groundwater Quality with Geostatistics (Case Study: Yazd-Ardakan Plain). World Appl. Sci. J., 4, 9-17.
  19. Oliver, M. A., and R. Webster, 1990: Kriging: a method of interpolation for geographical information systems. Int. J. Geogr. Inf. Syst., 4, 313-332. https://doi.org/10.1080/02693799008941549
  20. Park, D. W., N. V. Nikhil, and S. R. Lee, 2013: Landslide and debris flow susceptibility zonation using TRIGRS for the 2011 Seoul landslide event. Nat. Hazards Earth Syst. Sci., 13, 2833-2849, doi:10.5194/nhess-13-2833-2013. https://doi.org/10.5194/nhess-13-2833-2013
  21. Park, M.-S., and J.-H. Chae, 2018: Features of sea-land-breeze circulation over the Seoul Metropolitan Area. Geosci. Lett., 5, 28, doi:10.1186/s40562-018-0127-6. https://doi.org/10.1186/s40562-018-0127-6
  22. Park, M.-S., S.-H. Park, J.-H. Chae, M.-H. Choi, Y. Song, M. Kang, and J.-W. Roh, 2017: High-resolution urban observation network for user-specific meteorological information service in the Seoul Metropolitan Area, South Korea. Atmos. Meas. Tech., 10, 1575-1594, doi:10.5194/amt-10-1575-2017. https://doi.org/10.5194/amt-10-1575-2017
  23. Roth, M., 2000: Review of atmospheric turbulence over cities. Q. J. Roy. Meteor. Soc., 126, 941-990. https://doi.org/10.1002/qj.49712656409
  24. Setianto, A., and T. Triandini, 2013: Comparison of Kriging and inverse distance weighted (IDW) interpolation methods in lineament extraction and analysis. J. SE. Asian Appl., Geol., 5, 21-29.
  25. Shin, S.-C., M.-K. Kim, M.-S. Suh, D.-K. Rha, D.-H. Jang, C.-S. Kim, W.-S. Lee, and Y.-H. Kim, 2008: Estimation of high resolution gridded precipitation using GIS and PRISM. Atmosphere, 18, 71-81 (in Korean with English abstract).
  26. Shiode, N., and S. Shiode, 2011: Street-level Spatial Interpolation Using Network-based IDW and Ordinary Kriging. T. GIS, 15, 457-477, doi:10.1111/j.1467-9671.2011.01278.x. https://doi.org/10.1111/j.1467-9671.2011.01278.x
  27. Song, Y., J.-H. Chae, M.-H. Choi, M.-S. Park, and Y. J. Choi, 2014: Standardization of metadata for urban meteorological observations. J. Kor. Soc. Atmos. Environ., 30, 600-618, doi:10.5572/KOSAE.2014.30.6.600 (in Korean with English abstract). https://doi.org/10.5572/KOSAE.2014.30.6.600
  28. Stewart, I. D., and T. R. Oke, 2012: Local climate zones for urban temperature studies. Bull. Amer. Meteor. Soc., 93, 1879-1900, doi:10.1175/BAMS-D-11-00019.1. https://doi.org/10.1175/BAMS-D-11-00019.1
  29. Toparlar, Y., B. Blocken, B. Maiheu, and G. J. F. van Heijst, 2018: The effect of an urban park on the microclimate in its vicinity: a case study for Antwerp, Belgium, Int. J. Climatol., 38, e303-e322, doi:10.1002/joc.5371. https://doi.org/10.1002/joc.5371
  30. Um, M.-J., and C.-S. Jeong, 2011: Spatial analysis of precipitation with PRISM in Gangwondo. J. Kor. Water Resour. Assoc., 44, 179-188 (in Korean with English abstract). https://doi.org/10.3741/JKWRA.2011.44.3.179
  31. Um, M.-J., W. Cho, and H.-W. Rim, 2007: Rainfall adjustment on duration and topographic elevation. J. Kor. Water Resour. Assoc., 40, 511-521 (in Korean with English abstract). https://doi.org/10.3741/JKWRA.2007.40.7.511
  32. Um, M.-J., C.-S. Jeong, and W. Cho, 2009: Analysis of precipitation distribution in the region of Gangwon with spatial analysis (I): classification of precipitation zones and analysis for seasonal and annual precipitation. J. Kor. Soc. Hazard Mitig., 9, 103-113 (in Korean with English abstract).
  33. Yoon, S.-S., and B. Lee, 2017: Effects of Using High-Density Rain Gauge Networks and Weather Radar Data on Urban Hydrological Analyses. Water, 9, 931, doi:10.3390/w9120931. https://doi.org/10.3390/w9120931
  34. Yoon, S.-S., B. Lee, and Y. Choi, 2015: Quantitative Precipitation Estimation using High Density Rain Gauge Network in Seoul Area. Atmosphere, 25, 283-294, doi:10.14191/Atmos.2015.25.2.283 (in Korean with English abstract). https://doi.org/10.14191/Atmos.2015.25.2.283
  35. Yu, K., Y. Chen, D. Wang, Z. Chen, A. Gong, and J. Li, 2019: Study of the seasonal effect of building shadows on urban land surface temperatures based on remote sensing data. Remote Sens., 11, 497, doi:10.3390/rs11050497. https://doi.org/10.3390/rs11050497
  36. Yun, H.-S., M.-J. Um, W.-C. Cho, and J.-H. Heo, 2009: Orographic precipitation analysis with regional frequency analysis and multiple linear regression. J. Korea Water Resour. Assoc., 42, 465-480 (in Korean with English abstract). https://doi.org/10.3741/JKWRA.2009.42.6.465

Acknowledgement

Supported by : 기상청