Extended Abstract

2.1 MethodologyIn the EOF analysis, sp ace-time data D(r,t) are represented in te rms of the loadingvectors (L) and their principal component (PC) time series: D(r,t)=∑PC n (t)L n (r), whereL n (r) and PC n (t) are specific spatial patterns and their temporal ev olutionsrespectively.The CSEOF analysis is described in det ail by Kim and North (1997). In the CSEOFanalysis, a sp ace-time datum are D(r,t) represented as D(r,t)=∑PC n (t)CL n (r,t),CL n (r,t)=LV n (r,t+d), where CL n (r,t) are cyclost ationary loading vectors (CSL Vs) andPC n (t) are their corresponding princip al com ponent (PC) time series, The sp atialpatterns is further constrained to be periodic with d.Canonical correlation analysis seeks vectors a and b such that the random variablesa'X and b'Y maximize the correlation ρ=cor(a'X,b'Y). The random variables U=a'X andV=b'Y are the first pair of canonical variables.2.2. Data descriptionThere are two p arts dat a in this study , Korea precipit ation and sea surfacetemperature, which respectively obtained from Korea Water Management InformationSystem Dat a Set and Internati onal Comprehensive Ocean-Atmosphere Dat a Set(ICOADS).These SST dat a come from ICOADS, and t he sp atial covera ge is glo bal grid 2.0degree latitude×2.0 degree longitude, the grid 0°N-45°N, 75°E-150°E over Ease Asiawhich have 456 months (1972-2009) records of data are used in this research. And 61stations in Korea which have 444 months (1973-2009) records of data are used for thecurrent study . The available dat a main tained with extens ive quality control andcalculation of average of 3 months dat a, which are the most impor tant processes forstudying the frequency and intensity.3. Analysis and ResultsThe EOF technique and CSEOF technique have been applied to the average of 3months precipitation data of 61 stations during 36 years in Korea. The twelve leadingEOF and CSEOF modes of SST data respectively account for 96.84% and 99.96% ofthe total variance; and the twelv e leading EOF and CSEOF modes of precipitationdata respectively acc ount for 97.12% and 97.62% of the total variance. The mainmotivation for employing this technique in the present study is to investigate thephysical processes associated with the ev olution of the precipitation from theobservation data. The first leading princi pal component time series of EOF andCSEOF analysis are shown in figure 2.-461-