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고해상도 수치예측자료 생산을 위한 경도-역거리 제곱법(GIDS) 기반의 공간 규모 상세화 기법 활용

Implementation of Spatial Downscaling Method Based on Gradient and Inverse Distance Squared (GIDS) for High-Resolution Numerical Weather Prediction Data

  • 양아련 (봄인 사이언스 컨설팅) ;
  • 오수빈 (봄인 사이언스 컨설팅) ;
  • 김주완 (공주대학교 대기과학과) ;
  • 이승우 (기상청 수치모델링센터 수치자료응용과) ;
  • 김춘지 (봄인 사이언스 컨설팅) ;
  • 박수현 (봄인 사이언스 컨설팅)
  • Yang, Ah-Ryeon (BomIn Science Consulting) ;
  • Oh, Su-Bin (BomIn Science Consulting) ;
  • Kim, Joowan (Department of Atmospheric Science, Kongju National University) ;
  • Lee, Seung-Woo (Numerical Data Application Division, Numerical Modeling Center, Korea Meteorological Administration) ;
  • Kim, Chun-Ji (BomIn Science Consulting) ;
  • Park, Soohyun (BomIn Science Consulting)
  • 투고 : 2021.02.01
  • 심사 : 2021.04.04
  • 발행 : 2021.06.30

초록

In this study, we examined a spatial downscaling method based on Gradient and Inverse Distance Squared (GIDS) weighting to produce high-resolution grid data from a numerical weather prediction model over Korean Peninsula with complex terrain. The GIDS is a simple and effective geostatistical downscaling method using horizontal distance gradients and an elevation. The predicted meteorological variables (e.g., temperature and 3-hr accumulated rainfall amount) from the Limited-area ENsemble prediction System (LENS; horizontal grid spacing of 3 km) are used for the GIDS to produce a higher horizontal resolution (1.5 km) data set. The obtained results were compared to those from the bilinear interpolation. The GIDS effectively produced high-resolution gridded data for temperature with the continuous spatial distribution and high dependence on topography. The results showed a better agreement with the observation by increasing a searching radius from 10 to 30 km. However, the GIDS showed relatively lower performance for the precipitation variable. Although the GIDS has a significant efficiency in producing a higher resolution gridded temperature data, it requires further study to be applied for rainfall events.

키워드

참고문헌

  1. Ahn, J.-B., J. Lee, and E.-S. Im, 2012: The reproducibility of surface air temperature over South Korea using dynamical downscaling and statistical correction. J. Meteor. Soc. Japan Ser. II, 90, 493-507, doi:10.2151/jmsj.2012-404.
  2. Ahrens, C. D., 2003: Meteorology today: An introduction to weather, climate, and the environment. 7th ed. Brooks Cole, 624 pp.
  3. Barnes, S. L., 1964: A technique for maximizing details in numerical weather map analysis. J. Appl. Meteor. Climatol., 3, 396-409. https://doi.org/10.1175/1520-0450(1964)003<0396:ATFMDI>2.0.CO;2
  4. Cardoso, R. M., P. M. M. Soares, P. M. A. Miranda, and M. Belo-Pereira, 2012: WRF high resolution simulation of Iberian mean and extreme precipitation climate. Int. J. Climatol., 33, 2591-2608, doi:10.1002/joc.3616.
  5. Case, J. L., J. Manobianco, J. E. Lane, C. D. Immer, and F. J. Merceret, 2004: An objective technique for verifying sea breezes in high-resolution numerical weather prediction models. Wea. Forecasting, 19, 690-705. https://doi.org/10.1175/1520-0434(2004)019<0690:AOTFVS>2.0.CO;2
  6. Cressman, G. P., 1959: An operational objective analysis system. Mon. Wea. Rev., 87, 367-374. https://doi.org/10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2
  7. Daly, C., 2002: Variable influence of terrain on precipitation patterns: Delineation and use of effective terrain height in PRISM. Oregon State University, 1-7.
  8. Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor. Climatol., 33, 140-158. https://doi.org/10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2
  9. Di Piazza, A., F. Lo Conti, L. V. Noto, F. Viola, and G. La Loggia, 2011: Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy. Int. J. Appl. Earth Obs. Geoinf., 13, 396-408, doi:10.1016/j.jag.2011.01.005.
  10. Flint, L. E, and A. L. Flint, 2012: Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis. Ecol. Process., 1, 2, doi:10.1186/2192-1709-1-2.
  11. Guan, H., X. Zhang, O. Makhnin, and Z. Sun, 2013: Mapping mean monthly temperatures over a coastal hilly area incorporating terrain aspect effects. J. Hydrometeor., 14, 233-250, doi:10.1175/JHM-D-12-014.1.
  12. Heikkila, U., A. Sandvik, and A. Sorterberg, 2011: Dynamical downscaling of ERA-40 in complex terrain using the WRF regional climate model. Climate Dyn., 37, 1551-1564, doi:10.1007/s00382-010-0928-6.
  13. Johnson, G. L., C. Daly, G. H. Taylor, and C. L. Hanson, 2000: Spatial variability and interpolation of stochastic weather simulation model parameters. J. Appl. Meteor. Climatol., 39, 778-796. https://doi.org/10.1175/1520-0450(2000)039<0778:SVAIOS>2.0.CO;2
  14. Journel, A. G., and C. J. Huijbregts, 1978: Mining Geostatistics. Academic Press, 600 pp.
  15. Kim, D.-K., and H.-Y. Chun, 2000: A numerical study of the orographic effects associated with a heavy rainfall event. J. Korean Meteor. Soc., 36, 441-454 (in Korean with English abstract).
  16. 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. Clim. Res., 7, 55-68 (in Korean with English abstract).
  17. Kim, T.-J., H.-H. Kwon, D.-R. Lee, and S.-K. Yoon, 2014: Development of stochastic downscaling method for rainfall data using GCM. J. Korea Water Resour. Assoc., 47, 825-838, doi:10.3741/JKWRA.2014.47.9.825 (in Korean with English abstract).
  18. Kravchenko, A. N., 2003: Influence of spatial structure on accuracy of interpolation methods. Soil. Sci. Soc. Amer. J., 67, 1564-1571. https://doi.org/10.2136/sssaj2003.1564
  19. Kwon, H.-H., T. J. Kim, S.-H. Hwang, and T.-W. Kim, 2013: Development of daily rainfall simulation model based on homogeneous hidden markov chain. J. Korean Soc. Civ. Eng., 33, 1861-1870, doi:10.12652/Ksce.2013.33.5.1861 (in Korean with English abstract).
  20. Lee, J., J.-B. Ahn, M.-P. Jung, and K.-M. Shim, 2017: A study on the method of producing the 1 km resolution seasonal prediction of temperature over South Korea for boreal winter using genetic algorithm and global elevation data based on remote sensing. Korean J. Remote Sens., 33, 661-676, doi:10.7780/kjrs.2017.33.5.2.6 (in Korean with English abstract).
  21. Lim, Y.-K., D. W. Shin, S. Cocke, T. E. LaRow, J. T. Schoof, J. J. O'Brien, and E. P. Chassignet, 2007: Dynamically and statistically downscaled seasonal simulations of maximum surface air temperature over the southeastern United States. J. Geophys Res. Atmos., 112, D24102. doi:10.1029/2007JD008764.
  22. Lin, Z.-H., X.-G. Mo, H.-X. Li, and H.-B. Li, 2002: Comparison of three spatial interpolation methods for climate variables in China. Acta Geogr. Sin., 57, 47-56.
  23. Lo, J. C.-F., Z.-L. Yang, and R. A. Pielke Sr., 2008: Assessment of three dynamical climate downscaling methods using the weather research and forecasting (WRF) model. J. Geophys. Res. Atmos., 113, D09112.
  24. Mohammadi, S. A., M. Azadi, and M. Rahmani, 2017: Comparison of spatial interpolation methods for gridded bias removal in surface temperature forecasts. J. Meteor. Res., 31, 791-799, doi:10.1007/s13351-017-6135-1.
  25. Myers, D. E., 1982: Matrix formulation of co-kriging. J. Int. Ass. Math. Geol., 14, 249-257. https://doi.org/10.1007/BF01032887
  26. Nalder, I. A., and R. W. Wein, 1998: Spatial interpolation of climatic normals: test of a new method in the Canadian boreal forest. Agr. Forest Meteorol., 92, 211-225. https://doi.org/10.1016/S0168-1923(98)00102-6
  27. Reinstorf, F., M. Binder, M. Schirmer, J. Grimm-Strele, and W. Walther, 2005: Comparative assessment of regionalisation methods of monitored atmospheric deposition loads. Atmos. Environ., 39, 3661-3674. https://doi.org/10.1016/j.atmosenv.2005.03.006
  28. Shepard, D., 1968: A two-dimensional interpolation function for irregularly-spaced data. Proc. The 23rd ACM National Conference. New York, Association for Computing Machinery, 517-524.
  29. Stahl, K., R. D. Moore, J. A. Floyer, M. G. Asplin, and I. G. McKendry, 2006: Comparison of approaches for spatial interpolation of daily air temperature in a large region with complex topography and highly variable station density. Agric. Forest Meteorol., 139, 224-236. https://doi.org/10.1016/j.agrformet.2006.07.004
  30. Tang, L., X. Su, G. Shao, H. Zhang, and J. Zhao, 2012: A Clustering-Assisted Regression (CAR) approach for developing spatial climate data sets in China. Environ. Modell. Softw., 38, 122-128, doi:10.1016/j.envsoft.2012.05.008.
  31. Thiessen, A. H., 1911: Precipitation averages for large areas. Mon. Wea. Rev., 39, 1082-1089.
  32. von Storch, H., 1995: Spatial patterns: EOFs and CCA. In H. von Storch et al. Eds., Analysis of Climate Variability: Applications of Statistical Techniques, Springer, 231-263.