Journal of Environmental Science International (한국환경과학회지)

The Korean Environmental Sciences Society (한국환경과학회)
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Comparative Study on the Accuracy of Surface Air Temperature Prediction based on selection of land use and initial meteorological data

토지이용도와 초기 기상 입력 자료의 선택에 따른 지상 기온 예측 정확도 비교 연구

  • Hae-Dong Kim (Department of Environmental Engineering, Keimyung University) ;
  • Ha-Young Kim (Department of Environmental Science, Keimyung University)
  • 김해동 (계명대학교 공과대학 환경공학과) ;
  • 김하영 (계명대학교 환경과학과)
  • Received : 2024.04.05
  • Accepted : 2024.06.03
  • Published : 2024.06.30
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Abstract

We investigated the accuracy of surface air temperature prediction according to the selection of land-use data and initial meteorological data using the Weather Research and Forecasting model-v4.2.1. A numerical experiment was conducted at the Daegu Dyeing Industrial Complex. We initially used meteorological input data from GFS (Global forecast system)and GDAPS (Global data assimilation and prediction system). High-resolution input data were generated and used as input data for the weather model using the land cover data of the Ministry of Environment and the digital elevation model of the Ministry of Land, Infrastructure, and Transport. The experiment was conducted by classifying the terrestrial and topographic data (land cover data) and meteorological data applied to the model. For simulations using high-resolution terrestrial data(10 m), global data assimilation, and prediction system data(CASE 3), the calculated surface temperature was much closer to the automatic weather station observations than for simulations using low-resolution terrestrial data(900 m) and GFS(CASE 1).

Keywords

Acknowledgement

이 논문은 2023년 대구녹색환경지원센터 연구개발사업의 지원을 받아 수행된 연구임(NO 23-04-03-40-42).

References

  1. Ahn, E., J., Kim, H. D., 2019, Analysis of the correlation between urban high temperature phenomenon and air pollution during summer in Daegu, J. Environ. Sci. Int., 28(10), 831-840. 
  2. Catalano, F., Moeng, C. H., 2010, Large-eddy simulation of the daytime boundary layer in an idealixed valley using the weather research and forecasting numerical model, Springer, 137, 49-75. 
  3. Chen, F., Dudhia, J., 2001, Coupling an advanced land surface/hydrology model with the penn State/NCAR MM5 modeling system. PartI: Model implementation and sensitivity, Monthly Weather Review, 129, 569-585. 
  4. DEGEC, 2019, A Study on the development of extreme climate adaptation measures in Daegu City (No.19-04-01-90-94), Daegu, 30-39. 
  5. Dudhia, J., 1989, Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model, J. Atmos. Sci., 46, 3077-3107. 
  6. Ferrier, B. S., 1994, A Double-moment multiple-phase fourclass bulk ice scheme. Part I: Description, J. Atmos. Sci., 51, 249-280. 
  7. Hong, S. O., Byon, J. Y., Park, H. S., Lee, Y. G., Kim, B. J., Ha, J. C., 2018, Sensitivity analysis of near surface air temperature to land cover change and urban parameterization scheme using unified model, Atmosphere, 28(4), 427-441. 
  8. Hujibe, H., 2012, Urban Climate Variation and Abnormal Weather, Asakura Press, Tokyo, Japan. 
  9. ICEC, 2022, A Study on climate, environment and energy based on Gwangju's big data platform system: Towards an improved application, ICEC, Gwangju, Korea. 
  10. Ichinose, T., 2003, Regional warming related to land-use change during recent 135 years in Japan, J. Global Environ. Eng., 9, 19-39. 
  11. Janjic, Z. I., 1994, The step-mountain eta coordinate model: further developments of the convection, viscous sublayer, and turbulence closure schemes, Monthly Weather Review, 122(5), 927-945. 
  12. Kain, J. S., 2004, The Kain-Fritsch convective parameterization: An Update, J. Appl. Meteor. Climatol., 43, 170-181. 
  13. Kang, M. S., Lim, Y. K., Cho, C. B., Kim, K. R., Park, J. S., Kim, B. J., 2015, The sensitivity analyses of initial condition and data assimilation for a fog event using the mesoscale meteorological model, J. Korean Earth Sci. Soc., 36(6), 567-579. 
  14. Kim, E. R., Choi, K. C., Park, S. J., Kim, M. H., Kim, S. W., Park, M. S., Ahn, M. H., Park, Y, S., Song, C. K., 2023, Turbulent characteristics in complex coastal areas assessed using BSWO observations and WRF-LES simulation results, Atmospheric Research, 289, 106756. 
  15. Kim, K. Y., Byon, J. Y., Kim, H. D., 2013, Heat island intensity in Seongseo, Daegu, South Korea - a Rural suburb containing large areas of water, J. of Environ. Sci. Int., 22(10), 1337-1344. 
  16. Kim, S. H., Cho, C. B., Kim, H. D., 2014, Investigation of urban high temperature phenomenon in summer using the high density ground monitoring system in Daegu metropolitan area, J. Environ. Sci. Int., 23(9), 1619-1626. 
  17. KMA, 2016, A Detailed analysis report on climate change by local governments in Korea (No.11-1360000-001312-01), Seoul, Korea. 
  18. KME, 2000, https://egis.me.go.kr. 
  19. Lee, S. H, Kim, H. D., 2008, Effects of regional warming due to urbanization on daytime local circulations in a complex basin of the Daegu metropolitan area, Korea, J. Appli. Meterol. and Climato., 47, 1427-1441. 
  20. Lee, S. H., Kim, H. D., 2010, Modification of nocturnal drainage flow due to urban surface heat flux, Asia-Pacif. J. Atmos. Sci., 46, 453-465. 
  21. Lim, K. S. S., Hong, S. Y., 2010, Development of effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models, Monthly Weather Review, 138, 1587-1612. 
  22. Liu, B. N., Bae, W. K., 2023, Thermal environment CFD simulation analysis in heat wave of low-rise residential area, 2023 Spring presentation proceeding of the Korean Housing Association, 35(1). 79-82. 
  23. Liu, Y. J., Liu, Y. B., Munoz-Esparza, D., Hu, F., Yan, C., Miao, S. G., 2020, Simulation of flow fields in complex terrain with WRF-LES: Sensitivity assessment of diferent PBL treatments, J. Appl. Meteorol. Climatol., 59, 1481-1501. 
  24. Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., clough, S. A., 1997, Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated .k model for the longwave, J. Geophys. Res., 102(D14), 16663-16682. 
  25. Na, H. N., Jung, W. S., 2020, A Study on improving the prediction accuracy of a typhoon disaster prevention model part II: Sensitivity of the WRF model to GDAPS, RDAPS, GFS data, J. Korean Soc. Atmos. Environ., 36(4), 522-532. 
  26. NIMS, 2014, Construction and Evaluation of WRF-LES, Seogwipo, Korea. 
  27. Park, S. H., Jee, J. B., Yi, C. Y., 2015, Sensitivity test of the numerical simulation with high resolution top-ographyand landuse over Seoul metropolitan and surrounding areas, Atmosphere, 25(2), 309-322. 
  28. Park, S. K., Kim, J. H., 2011, A Study on changes in local meteorological fields due to a change in land use in the lake Shihwa region using synthetic land cover data and high-resolution mesoscale model, Atmosphere, 21(4), 405-414. 
  29. Paulson, C. A., 1970, The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer, J. Appl. Meteor. Climatol., 9, 857-861. 
  30. Seo, B. K., Byon, J. Y., Lim, Y. J., Choi, B. C., 2015, Evaluation of surface wind forecast over the Gangwon province using the mesoscale WRF model, J. Korean Earth Sci. Soc., 36(2), 158-170. 
  31. Talbot, C., Bou-zeid, E., Smith, J., 2012, Nested mesoscale large-eddy simulations with WRF: Performance in real test cases, American Meteorological Society, 3, 1421-1440. 
  32. Ushiyama, M., Mikami, T. K., Kimura, K., 1999, On the high density urban climate observation network in Tokyo area - monitoring and management of urban heat island, Interm Report, 4-15.