The Study on the Grid Size Regarding Spatial Interpolation for Local Climate Maps* Cha Yong Ku** Young Ho Shin*** Jae-Won Lee**** Hee-Soo Kim*****.,...,,,, Abstract : Recent global warming and abnormal weather have had negative impacts on human life, which has risen increasing attention of producing a local climate map. In this study, we review some issues to determine grid size regarding spatial interpolation and propose an appropriate spatial resolution for a local climate map. We utilized the climate data of Seoul and Kangwon Province. Point pattern analysis and accuracy assessment were applied to find an optimal spatial resolution in producing climate maps of the areas. It is anticipated that the spatial resolution would be most appropriate to result in local climate maps. Key Words : climate maps, spatial interpolation techniques, spatial resolution, point pattern analysis, accuracy assessment,... 73 30, (, 2011).. 73 * (CATER 2012-3120). ** (Professor, Department of Geography, Sangmyung University, koostar@smu.ac.kr) *** (Visiting Researcher, The Institute for Korean Regional Studies, Seoul ational University, syhgeo@snu.ac.kr) **** (Director, Meteorological Resources Division, Korea Meterological Administration, jlee@kma.go.kr) ***** (Deputy Director, Meteorological Resources Division, Korea Meterological Administration, khs@kma.go.kr)
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. Willmott(1982). (Mean Absolute Error; MAE).,.. MAE = _ 1 P i _ Oi... (3), Pi, O i. MAE. (Root Mean Square Errors; RMSE).,.. RMSE = _ 1 [(P i _ Oi ) 2 ] 1/2... (4) RMSE,. (Systematic Root Mean Square Errers; RMSEs). RMSEs. RMSE s = _ 1 S i = 1 [( ˆP i _ Oi ) 2 ] 1/2... (5) ˆP (O), (P).. (Unsystematic Root Mean Square Errers; RMSE u ). RMSE u. RMSE u = _ 1 S i = 1 S i = 1 S i = 1 [(P i _ ˆPi ) 2 ] 1/2... (6). (Index of Agreement; d). (RMSE) (Potential Error; PE),. d = 1 _ S PE = [ P i O i + O i O i ] 2 i = 1 SE 2 PE... (7) 0 1, 1.. Anderson(2001) IDW,,..,.. 16... 494
(a) (b),. 1.,. 38, 97.,,.,,,.,,,. 2. 2012 5 1 5 31, 495
A km 2 606.747 16,930.1 km 183.788 929.733 38 97 0.05 A m 199.794 660.562 0.1 A m 399.588 1,321.125 _ h ij km 2.046 5.0803 2.,. 1. GIS 606.747km 2, 38. 1 199m, 399m. 200m 400m.,, 4.092km. 2.046km. 2km., 200m 2km., GIS 16,930km 2, 97. 660m, 1,321m. 1km 1.3km.,, 10.1606km. 5.0803km. 5km., 1km 5km.. 200m 2km, 1km 5km.., 496
(a) 200m (b) 500m (c) 1000m (d) 1500m (e) 2000m (a) 1km (b) 2km (c) 3km (d) 4km (e) 5km 497
.. 200m, 500m, 1000m, 1500m, 2000m, 1km, 2km, 3km, 4km, 5km. 4, 5. 2., (index of degree).. 3,,.... MAE RMSE RMSE S RMSE U d 200m 27,935 0.308974 0.182417 0.370818 0.211923 0.938933 500m 4,575 0.321406 0.200492 0.383572 0.231008 0.932201 1000m 1,178 0.332457 0.19796 0.380988 0.229801 0.933091 1500m 500 0.343428 0.20645 0.382814 0.244753 0.930197 2000m 304 0.348846 0.237559 0.434816 0.220214 0.915317 MAE RMSE RMSE S RMSE U d 1 km 33,432 0.610189 0.802081 0.688597 0.411299 0.906102 2 km 8,500 0.758339 1.019247 0.861385 0.544866 0.83359 3 km 3,762 0.909517 1.202267 1.036552 0.609102 0.748158 4 km 2,100 1.012804 1.343414 1.141009 0.709127 0.674603 5 km 1,360 1.028065 1.343006 1.152991 0.688678 0.667822 (a) (b) 498
(d) 6., 500m 1000m 1500m. 500m 1km, 1km. 1km 4km 4km. 4km.,,,. 1km, 4km...,..,.,..,,...,,.,.,.,,,.,., 2000,, GIS 8(2): 243-255., 2004,, 499
38(4): 557-571., 2011, 1981~2010,., 2008,, 43(6): 1002-1015., 2008, GIS PRISM, 18(1): 71-81., 2011, GIS, 45(4): 613-623., 2011, GIS, 6(2): 159-170., 2006,, GIS 14(1): 29-56., 2007, GIS PRISM, 17(3): 255-268. Anderson, S. 2001. An evaluation of spatial interpolation methods on air temperature in Phoenix, AZ. paper from UCGIS student paper competitions. Dobesch, H., Dumolard, P. and Dyras, I. 2007. Spatial Interpolation for Climate Data. London: ISTE Ltd. Hengl, T. 2006. Finding the right pixel size. Computers & Geosciences 32: 1283-1298. Willmott, C.J. 1982. Some comments on the evaluation of model performance. Bulletin American Meteorology Society 63(11): 1309-1313. Woodcock, C.E. and Strahler, A.H. 1987. The factor of scale in remote sensing. Remote Sensing of Environment 21: 311-332. 500