Browse > Article
http://dx.doi.org/10.7780/kjrs.2022.38.5.1.3

Evaluation of the Air Temperature and Wind Observation Environments Around Automated Synoptic Observing Systems in Summer Using a CFD Model  

Kang, Jung-Eun (Major of Environmental Atmospheric Sciences, Division of Earth Environmental System Science, Pukyong National University)
Rho, Ju-Hwan (Major of Environmental Atmospheric Sciences, Division of Earth Environmental System Science, Pukyong National University)
Kim, Jae-Jin (Major of Environmental Atmospheric Sciences, Division of Earth Environmental System Science, Pukyong National University)
Publication Information
Korean Journal of Remote Sensing / v.38, no.5_1, 2022 , pp. 471-484 More about this Journal
Abstract
This study examined the effects of topography and buildings around the automated synoptic observing system (ASOS) on the observation environment of air temperatures and wind speeds and directions using a computational fluid dynamics(CFD) model. For this, we selected 10 ASOSs operated by the Korea Meteorological Administration. Based on the data observed at the ASOSs in August during the recent ten years, we established the initial and boundary conditions of the CFD model. We analyzed the temperature observation environment by comparing the temperature change ratios in the case considering the actual land-cover types with those assuming all land-cover types as grassland. The land-cover types around the ASOSs significantly affected the air temperature observation environment. The temperature change ratios were large at the ASOSs around which buildings and roads were dense. On the other hand, when all land covers were assumed as grassland, the temperature change ratios were small. Wind speeds and directions at the ASOSs were also significantly influenced by topography and buildings when their heights were higher or similar to the observation heights. Obstacles even located at a long distance affected the wind observation environments. The results in this study would be utilized for evaluating ASOS observation environments in the relocating or newly organizing steps.
Keywords
CFD model; Geographic information system; Automated synoptic observing system; Environmental geographic information service; Meteorological observation environment;
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
연도 인용수 순위
1 Blocken, B., W.D. Janssen, and T. van Hooff, 2012. CFD simulation for pedestrian wind comfort and wind safety in urban areas: General decision framework and case study for the Eindhoven University campus, Environmental Modelling & Software, 30: 15-34. https://doi.org/10.1016/j.envsoft.2011.11.009   DOI
2 Bourbia, F. and H.B. Awbi, 2004. Building cluster and shading in urban canyon for hot dry climate, Part 1: Air and surface temperature measurements, Renewable Energy, 29(2): 249-262. https://doi.org/10.1016/S0960-1481(03)00170-8   DOI
3 Brozovsky, J., A. Simonsen, and N. Gaitani, 2021. Validation of a CFD model for the evaluation of urban microclimate at high latitudes: A case study in Trondheim, Norway, Building and Environment, 205: 108175. https://doi.org/10.1016/j.buildenv.2021.108175   DOI
4 Castro, I.P. and D.D. Apsley, 1997. Flow and dispersion over topography: a comparison between numerical and laboratory data for two-dimensional flows, Atmospheric Environment, 31(6): 839-850. https://doi.org/10.1016/S1352-2310(96)00248-8   DOI
5 Jarraud, M., 2018. Guide to Instruments and Methods of Observation (2018 edition - Volume I: Measurement of Meteorological Variables), World Meteorological Organization, Geneva, Switzerland.
6 Kang, J.-E. and J.-J. Kim, 2020. Assessment of Observation Environments of Automated Synoptic Observing Systems Using GIS and WMO Meteorological Observation Guidelines, Korean Journal of Remote Sensing, 36(5-1): 693-706 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2020.36.5.1.4   DOI
7 Kim, J.-J., E. Pardyjak, D.-Y. Kim, K.-S. Han, and B.- H. Kwon, 2014. Effects of Building-Roof Cooling on Flow and Air Temperature in Urban Street Canyons, Asia-Pacific Journal of Atmospheric Sciences, 50(3): 365-375. https://doi.org/10.1007/s13143-014-0023-8   DOI
8 Ku, C.A. and H.K. Tsai, 2020. Evaluating the influence of urban morphology on urban wind environment based on computational fluid dynamics simulation, ISPRS International Journal of Geo-Information, 9(6): 399. https://doi.org/10.3390/ijgi9060399   DOI
9 Lee, H., J.-J. Kim, and Y.-G. Lee, 2015. A Study on the Characteristics of Flows around Building Groups Using a CFD Model, Atmosphere, 25(3): 501-510 (in Korean with English abstract). https://doi.org/10.14191/Atmos.2015.25.3.501   DOI
10 Yang, H.-J. and J.-J. Kim, 2015a. Assessment of Observation Environment for Surface Wind in Urban Areas Using a CFD model, Atmosphere, 25(3): 449-459 (in Korean with English abstract). https://doi.org/10.14191/Atmos.2015.25.3.449   DOI
11 Kwon, A.-R. and J.-J. Kim, 2015. Analysis on the Observation Environment of Surface Wind Using GIS data, Korean Journal of Remote Sensing, 31(2): 65-75 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2015.31.2.2   DOI
12 Zhang, S., K.C. Kwok, H. Liu, Y. Jiang, K. Dong, and B. Wang, 2021. A CFD study of wind assessment in urban topology with complex wind flow, Sustainable Cities and Society, 71: 103006. https://doi.org/10.1016/j.scs.2021.103006   DOI
13 Liu, S., W. Pan, H. Zhang, X. Cheng, Z. Long, and Q. Chen, 2017. CFD simulations of wind distribution in an urban community with a full-scale geometrical model, Building and Environment, 117: 11-23. https://doi.org/10.1016/j.buildenv.2017.02.021   DOI
14 Park, S.-J., S.-H. Choi, J.-E. Kang, D.-J. Kim, D.-S. Mun, W.-S. Choi, J.-J. Kim, and Y.-G. Lee, 2016. Effects of Differential Heating by LandUse types on flow and air temperature in an urban area, Korean Journal of Remote Sensing, 32(6): 603-616 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2016.32.6.5   DOI
15 Yang, H.-J. and J.-J. Kim, 2015b. Evaluation of Observation Environment for Weather Stations Located in Metropolitan Areas, Korean Journal of Remote Sensing, 31(2): 193-203 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2015.31.2.13   DOI