경기북부지역 정밀 수치기후도 제작 및 활용 - I. 수치기후도 제작

Development and Use of Digital Climate Models in Northern Gyunggi Province - I. Derivation of DCMs from Historical Climate Data and Local Land Surface Features

  • 김성기 (경기도 농업기술원 북부농업시험장) ;
  • 박중수 (경기도 농업기술원 북부농업시험장) ;
  • 이은섭 (경기도 농업기술원 북부농업시험장) ;
  • 장정희 (경기도 농업기술원 북부농업시험장) ;
  • 정유란 (경희대학교 생태시스템공학과/생명자원과학연구원) ;
  • 윤진일 (경희대학교 생태시스템공학과/생명자원과학연구원)
  • 발행 : 2004.03.01

초록

Northern Gyeonggi Province(NGP), consisting of 3 counties, is the northernmost region in South Korea adjacent to the de-militarized zone with North Korea. To supplement insufficient spatial coverage of official climate data and climate atlases based on those data, high-resolution digital climate models(DCM) were prepared to support weather- related activities of residents in NGP Monthly climate data from 51 synoptic stations across both North and South Korea were collected for 1981-2000. A digital elevation model(DEM) for this region with 30m cell spacing was used with the climate data for spatially interpolating daily maximum and minimum temperatures, solar irradiance, and precipitation based on relevant topoclimatological models. For daily minimum temperature, a spatial interpolation scheme accommodating the potential influences of cold air accumulation and the temperature inversion was used. For daily maximum temperature estimation, a spatial interpolation model loaded with the overheating index was used. Daily solar irradiances over sloping surfaces were estimated from nearby synoptic station data weighted by potential relative radiation, which is the hourly sum of relative solar intensity. Precipitation was assumed to increase with the difference between virtual terrain elevation and the DEM multiplied by an observed rate. Validations were carried out by installing an observation network specifically for making comparisons with the spatially estimated temperature pattern. Freezing risk in January was estimated for major fruit tree species based on the DCMs under the recurrence intervals of 10, 30, and 100 years, respectively. Frost risks at bud-burst and blossom of tree flowers were also estimated for the same resolution as the DCMs.

키워드

참고문헌

  1. Choi, J., U. Chung, and J. I. Yun 2003: Urban effect correction to improve accuracy of spatially interpolated temperature estimates in Korea. Journal of Applied Meteorology 42: 1711-1719
  2. Chung, U., and J. I. Yun, 2002: Spatial interpolation of hourly air temperature over sloping surfaces based on a solar irradiance correction. Korean Journal of Agricultural and Forest Meteorology, 4(2), 95-103
  3. Chung, U., H. H. Seo, K. H. Hwang, B. S. Hwang, and J. I. Yun., 2002: Minimum temperature mapping in complex terrain considering cold air drainage. Korean Journal of Agricultural and Forest Meteorology, 4(3), 133-140
  4. Chung, U., H. C. Seo, J. I. Yun, and K. H. Lee, 2003: An optimum scale for topoclimatic interpolation of daily minimum temperature in complex terrain. Korean Journal of Agricultural and Forest Meteorology, 5(4), 261-265
  5. Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A statistical - topographical model for mapping climatological precipitation over mountainous terrain. Journal of Applied Meteorology 33, 140-158
  6. Dodson, R. and D. Marks, 1997: Daily temperature interpolated at high spatial resolution over a large mountainous region. Climate Research 8(1), 1-20
  7. Dodson, R. and D. Marks, 1997: Daily temperature interpolated at high spatial resolution over a large mountainous region. Climate Research 8(1), 1-20
  8. Gates, D. M., 1980: Biophysical Ecology. Springer-Verlag, New York
  9. Holdaway, M. R., 1996: Spatial modeling and interpolation of monthly temperature using kriging. Climate Research 6, 215-225
  10. Nakai, K., 1990. Japanese system of the meteorological information service to user communities including education and training. In A. Price-Budgen(ed.) Using Meteorological Information and Products. Ellis Horwood, UK. 257-274
  11. Nalder, I. A., and R. W. Wein, 1998: Spatial interpolation of climatic normals: test of a new method in the Canadian boreal forest. Agricultural and Forest Meteorology 92, 211-225
  12. Phillips, D. L., J. Dolph, and D. Marks, 1992: A comparison of geostatistical procedures for spatial analysis of precipitation in mountainous terrain. Agricultural and Forest Meteorology 58, 119-141199
  13. Regniere, J., 1996: Generalized approach to landscapewide seasonal forecasting with temperature-driven simulation models. Environmental Entomology 25(5), 896-881
  14. Regniere, J., B. Cooke, and V. Bergeron, 1996: BioSIM: A Computer-Based Decision Support Tool for Seasonal Planning of Pest Management Activities. User's Manual. Canadian Forest Service Info. Rep. LAU-X-116. 50p
  15. Seino, H., 1993: An estimation of distribution of meteorological elements using GIS and AMeDAS data. Journal of Agricultural Meteorology (Japan) 48(4), 379- 383
  16. Shin, M. Y., and J. I. Yun, 1992: Estimation of monthly temperature distribution in Cheju Island by topoclimatological relationships. Journal of Korean Forestry Society 81, 40-52
  17. un, J. I., 2000: Estimation of climatological precipitation of North Korea by using a spatial interpolation scheme. Korean Journal of Agricultural and Forest Meteorology 2(1),16-23
  18. Yun, J. I., and S. E. Taylor, 1998: Modelling soil temperature of sloped surfaces by using a GIS technology. Korean J. Crop Science 43(2), 113-119