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http://dx.doi.org/10.14191/Atmos.2022.32.3.179

Development and Wind Speed Evaluation of Ultra High Resolution KMAPP Using Urban Building Information Data  

Kim, Do-Hyoung (Research Applications Department, National Institute of Meteorological Sciences)
Lee, Seung-Wook (Research Applications Department, National Institute of Meteorological Sciences)
Jeong, Hyeong-Se (Research Applications Department, National Institute of Meteorological Sciences)
Park, Sung-Hwa (Research Applications Department, National Institute of Meteorological Sciences)
Kim, Yeon-Hee (Numerical Modeling Center, Korea Meteorological Administration)
Publication Information
Atmosphere / v.32, no.3, 2022 , pp. 179-189 More about this Journal
Abstract
The purpose of this study is to build and evaluate a high-resolution (50 m) KMAPP (Korea Meteorological Administration Post Processing) reflecting building data. KMAPP uses LDAPS (Local Data Assimilation and Prediction System) data to detail ground wind speed through surface roughness and elevation corrections. During the detailing process, we improved the vegetation roughness data to reflect the impact of city buildings. AWS (Automatic Weather Station) data from a total of 48 locations in the metropolitan area including Seoul in 2019 were used as the observation data used for verification. Sensitivity analysis was conducted by dividing the experiment according to the method of improving the vegetation roughness length. KMAPP has been shown to improve the tendency of LDAPS to over simulate surface wind speeds. Compared to LDAPS, Root Mean Square Error (RMSE) is improved by approximately 23% and Mean Bias Error (MBE) by about 47%. However, there is an error in the roughness length around the Han River or the coastline. Accordingly, the surface roughness length was improved in KMAPP and the building information was reflected. In the sensitivity experiment of improved KMAPP, RMSE was further improved to 6% and MBE to 3%. This study shows that high-resolution KMAPP reflecting building information can improve wind speed accuracy in urban areas.
Keywords
KMAPP; LDAPS; Urban; Wind speed; Building height;
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1 Adelekan, I. O., 2012: Vulnerability to wind hazards in the traditional city of Ibadan, Nigeria. Environ. Urban., 24, 597-617, doi:10.1177/0956247812454247.   DOI
2 Best, M. J., and Coauthors, 2011: The Joint UK Land Environment Simulator (JULES), model description - Part 1: Energy and water fluxes. Geosci. Model Dev., 4, 677-699, doi:10.5194/gmd-4-677-2011.   DOI
3 NIMS, 2020: Development of Production Techniques on User-Customized High Resolution Weather Information. National Institute of Meteorological Sciences, 72 pp (in Korean).
4 Paul, S., S. Ghosh, M. Mathew, A. Devanand, S. Karmakar, and D. Niyogi, 2018: Increased spatial variability and intensification of extreme monsoon rainfall due to urbanization. Scientific Reports, 8, 3918, doi:10.1038/s41598-018-22322-9.   DOI
5 Liu, J., Z. Gao, L. Wang, Y. Li, and C. Y. Gao, 2018: The impact of urbanization on wind speed and surface aerodynamic characteristics in Beijing during 1991-2011. Meteor. Atmos. Phys., 130, 311-324, doi:10.1007/s00703-017-0519-8.   DOI
6 Fuchs, R. J., E. Brennan, F.-C. Lo, J. I. Uitto, and J. Chamie, 1994: Mega-city Growth and the Future. United Nations University Press, 392 pp.
7 Hong, S.-O., J.-Y. Byon, H. Park, Y.-G. Lee, B.-J. Kim, and J.-H. Ha, 2018: Sensitivity analysis of near surface air temperature to land cover change and urban parameterization scheme using Unified Model. Atmosphere, 28, 427-441, doi:10.14191/Atmos.2018.28.4.427 (in Korean with English abstract).   DOI
8 Kim, D. H., S. O. Hong, J. Y. Byon, H. S. Park, and J. C. Ha, 2019: Development and evaluation of urban canopy model based on unified model input data using urban building information data in Seoul. Atmosphere, 29, 417-427, doi:10.14191/Atmos.2019.29.4.417 (in Korean with English abstract).   DOI
9 Taubenbock, H., T. Esch, A. Felbier, M. Wiesner, A. Roth, and S. Dech, 2012: Monitoring urbanization in mega cities from space. Remote. Sens. Environ., 117, 162-176, doi:10.1016/j.rse.2011.09.015.   DOI
10 Yun, J., Y. H. Kim, and H. W. Choi, 2021: Analyses of the meteorological characteristics over South Korea for wind power applications using KMAPP. Atmosphere, 31, 1-15, doi:10.14191/Atmos.2021.31.1.001 (in Korean with English abstract).   DOI
11 Kim, D. J., G. Kang, D. Y. Kim, and J. J. Kim, 2020: Characteristics of LDAPS-predicted surface wind speed and temperature at Automated Weather Stations with different surrounding land cover and topography in Korea. Atmosphere, 11, 1224, doi:10.3390/atmos11111224 (in Korean with English abstract).   DOI
12 Masson, V., 2000: A physically-based scheme for the urban energy budget in atmospheric models, Bound.-Layer. Meteor., 94, 357-397, doi:10.1023/A:1002463829265.   DOI
13 Oke, T. R., 1995: The Heat Island of the Urban Boundary Layer: Characteristics, Causes and Effects. In J. E. Cermak, A. G. Davenport, E. J. Plate, and D. X. Viegas (Eds.), Wind Climate in Cities, Springer Netherlands, 81-107 pp.
14 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.   DOI
15 Byon, J.-Y., Y.-J. Choi, and B.-G. Seo, 2010: Evaluation of urban weather forecast using WRF-UCM (Urban Canopy Model) over Seoul. Atmosphere, 20, 13-26 (in Korean with English abstract).
16 Unnikrishnan, C. K., B. Gharai, S. Mohandas, A. Mamgain, E. N. Rajagopal, G. R. Iyenger, and P. V. N. Rao, 2016: Recent changes on land use/land cover over Indian region and its impact on the weather prediction using Unified model. Atmos. Sci. Lett., 17, 294-300, doi:10.1002/asl.658.   DOI
17 Shepherd, J. M., 2013: Impacts of urbanization on precipitation and storms: Physical insights and vulnerabilities. Climate Vulnerability, 5, 109-125 pp.   DOI
18 Statistics Korea, 2019: Household projections by province 2017-basaed: 2017-2047 [Available online at https://kosis.kr/publication/publicationThema.do] (in Korean).
19 Varquez, A. C. G., M. Nakayoshi, and M. Kanda, 2015: The effects of highly detailed urban roughness parameters on a sea-breeze numerical simulation. Bound.- Layer Meteor., 154, 449-469, doi:10.1007/s10546-014-9985-4.   DOI
20 Zhang, W., G. Villarini, G. A. Vecchi, and J. A. Smith, 2018: Urbanization exacerbated the rainfall and flooding caused by hurricane Harvey in Houston. Nature, 563, 384-388, doi:10.1038/s41586-018-0676-z.   DOI
21 Howard, T., and P. Clack, 2007: Correction and downscaling of NWP wind speed forecasts. Meteor. Appl., 14, 105-116.   DOI
22 Cardona, O. D., and Coauthors, 2012: Determinants of risk: Exposure and vulnerability. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change. 65-108pp.
23 Farr, T. G., and Coauthors, 2007: The shuttle radar topography mission, Rev. Geophys., 45, RG2004, doi:10.1029/2005RG000183.   DOI
24 Garschagen, M., and P. Romero-Lankao, 2015: Exploring the relationships between urbanization trends and climate change vulnerability. Climatic Change, 133, 37-52, doi:10.1007/s10584-013-0812-6.   DOI
25 Kalnay, E., and M. Cai, 2003: Impact of urbanization and land-use change on climate. Nature, 423, 528-531, doi:10.1038/nature01675.   DOI
26 Keum, W. H., S. H. Lee, D. I. Lee, S. S. Lee, and Y. H. Kim, 2021: Evaluation and improvement of the KMAPP surface wind speed prediction over complex terrain areas. Atmosphere, 31, 85-100, doi:10.14191/Atmos.2021.31.1.085 (in Korean with English abstract).   DOI
27 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, doi:10.1023/A:1019207923078.   DOI
28 Lu, X., D. Yuan, Y. Chen, and J. C. H. Fung, 2021: Impacts of urbanization and long-term meteorological variations on global PM2.5 and its associated health burden. Environ. Pollut., 270, 116003, doi:10.1016/j.envpol.2020.116003.   DOI