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Estimation of Monthly Precipitation in North Korea Using PRISM and Digital Elevation Model

PRISM과 상세 지형정보에 근거한 북한지역 강수량 분포 추정

  • Kim, Dae-Jun (National Center for Agro-Meteorology, Seoul National University) ;
  • Yun, Jin-I. (Department of Ecosystem Engineering, Kyung Hee University)
  • Received : 2011.03.14
  • Accepted : 2011.03.28
  • Published : 2011.03.30

Abstract

While high-definition precipitation maps with a 270 m spatial resolution are available for South Korea, there is little information on geospatial availability of precipitation water for the famine - plagued North Korea. The restricted data access and sparse observations prohibit application of the widely used PRISM (Parameter-elevation Regressions on Independent Slopes Model) to North Korea for fine-resolution mapping of precipitation. A hybrid method which complements the PRISM grid with a sub-grid scale elevation function is suggested to estimate precipitation for remote areas with little data such as North Korea. The fine scale elevation - precipitation regressions for four sloping aspects were derived from 546 observation points in South Korea. A 'virtual' elevation surface at a 270 m grid spacing was generated by inverse distance weighed averaging of the station elevations of 78 KMA (Korea Meteorological Administration) synoptic stations. A 'real' elevation surface made up from both 78 synoptic and 468 automated weather stations (AWS) was also generated and subtracted from the virtual surface to get elevation difference at each point. The same procedure was done for monthly precipitation to get the precipitation difference at each point. A regression analysis was applied to derive the aspect - specific coefficient of precipitation change with a unit increase in elevation. The elevation difference between 'virtual' and 'real' surface was calculated for each 270m grid points across North Korea and the regression coefficients were applied to obtain the precipitation corrections for the PRISM grid. The correction terms are now added to the PRISM generated low resolution (~2.4 km) precipitation map to produce the 270 m high resolution map compatible with those available for South Korea. According to the final product, the spatial average precipitation for entire territory of North Korea is 1,196 mm for a climatological normal year (1971-2000) with standard deviation of 298 mm.

현재 남한에서는 270m 해상도의 강수분포도가 제작되어 활용되고 있지만, 북한지역에는 강수관측점의 수가 남한에 비하여 매우 적어서 남한과 같은 방법으로 강수분포를 추정하기는 어렵다. 자료가 불충분한 북한지방의 강수추정을 위해 우선 낮은 해상도의 강수기후도를 PRISM을 이용하여 제작하고 격자 내 지형특성을 반영하기 위해 여기에 상대적으로 자료가 풍부한 남한의 '지형-강수 관계'에 근거한 보정값을 더하는 방법을 모색하였다. 남한 지역 270m 해상도의 DEM에서 자동기상관측소와 표준기상관측소 위치의 격자값을 추출하고 이들을 이용하여 AWS+KMA 및 KMA에 해당하는 가상지형을 만든 다음, 둘 간의 편차를 얻었다. 강수량에 대해서도 동일한 작업을 하여 둘 간의 편차를 얻어 경사향별로 고도편차-강수편차 간 회귀식을 도출하였다. 북한 지역의 270m 해상도의 DEM과 27개 기상대 고도 값으로 IDW한 가상지형 간의 편차를 구한 다음, 남한에서 얻은 회귀식을 적용하여 보정값을 계산하였다. 북한지역에 대해 2,430m 해상도로 PRISM모형을 구동하고 보정값을 적용하여 최종강수량을 얻었다. 제작된 강수기후도에 따르면 북한지방의 연간 총 강수량은 지역평균이 1,196mm이며 표준편차는 298mm인 것으로 추정된다.

Keywords

References

  1. Chung, U., K. Yun, K. S. Cho, J. H. Yi, and J. I. Yun, 2009:The PRISM-based rainfall mapping at an enhanced gridcell resolution in complex terrain. Korean Journal ofAgricultural and Forest Meteorology 11, 72-78. (InKorean with English abstract) https://doi.org/10.5532/KJAFM.2009.11.2.072
  2. Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A statistical-topographicmodel for mapping climatological precipitationover mountainous terrain. Journal of Applied Meteorology33, 140-158. https://doi.org/10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2
  3. Johnson, G. L., P. A. Pasteries, G. H. Taylor, and C. Daly,1999: Spatial climate products – a new dimension for climate applications. Preprints, 11th Conference on AppliedClimatology, Dallas, Texas, American Meteorological Society,107-113.
  4. Nalder, I. A., and R.W. Wein, 1998: Spatial interpolation ofclimatic normals: test of a new method in the Canadianboreal forest. Agricultural and Forest Meteorology 92,211-225. https://doi.org/10.1016/S0168-1923(98)00102-6
  5. Phillips, D. L., J. Dolph, and D. Marks, 1992: A comparison ofgeostatistical procedures for spatial analysis of precipitationin mountainous terrain. Agricultural and Forest Meteorology58, 119-141. https://doi.org/10.1016/0168-1923(92)90114-J
  6. Seino, H., 1993: An estimation of distribution of meteorologicalelements using GIS and AMeDAS data. Journal ofAgricultural Meteorology(Japan) 48, 379-383. https://doi.org/10.2480/agrmet.48.379
  7. Shin, S. C., M. G. Kim, M. S. Suh, D. K. Rha, D. H. Jang,C. S. Kim, W. S. Lee, and Y. H. Kim, 2008: Estimationof high resolution gridded precipitation using GIS andPRISM. Atmosphere 18, 71-81. (In Korean with Englishabstract)
  8. Thiessen, A. H., 1991: Precipitation averages for large areas.Monthly Weather Review 39, 1082-1084.
  9. Yun, J. I., 2000: Estimation of climatological precipitationof North Korea by using a spatial interpolation scheme.Korean Journal of Agricultural and Forest Meteorology2, 16-23. (In Korean with English abstract)
  10. Yun, J. I., 2010: Agroclimatic maps augmented by a GIStechnology. Korean Journal of Agricultural and ForestMeteorology 12, 63-73. (In Korean with English abstract) https://doi.org/10.5532/KJAFM.2010.12.1.063
  11. http://www.kma.go.kr/(2011.3.1)

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