A power spectral analysis is made seasonally for the data of daily mean temperature and pressure at Seoul(37°34'N, 126°58'E), Chupungnyong(36°13'N, 128°00'E), Kwangju(35°08'N, 126°55'E) from March 1961 to February 1986. The time sequences of the power spectra for the daily mean pressure show that power spectral density is generally high at the period of 20-30days and 10 days in winter, 15-20 days and 8.6 days in spring, summer and autumn. For the daily mean temperature, the power spectral density is generally lower than that of pressure and changes largely following the seasons, high in winter and low in summer. The time sequences of the' power spectra are much the same pattern as that of pressure in spring and autumn, but in winter show high power spectral density at the period of 5.5-7.5 days, much the same period as '3 cold days and 4 warm days' which is the popular weather lore on the winter temperature fluctuations in Korea. Judging from the phase differences between observation stations of temperature and pressure changes, which is less than 20 degrees, the phase changes of temperature occurs in sequence of Seoul, Chupungnyong, Kwangju and in case of pressure much the same as in the temperature at the period of above 15 days, but below the period of about 15 days in the opposite sequence. The correlations between the interannual changes of seasonal mean of weather elements and the power spectral density at each period are investigated to show that the positive correlation is between the power spectral density at the period of above 20 days and temperature in summer, that at below 4.5 days and precipitation in spring, that at below 6.7 days and temperature in winter.

In this study, the intra-seasonal fluctuation (ISF) of wintertime temperature change in East Asia was classified by a cluster analysis of complete linkage. A ISF of temperature change was defined as a difference of synthesized harmonics (1 to 36 harmonic) of daily temperature averaged for 30 years (1951~1980, 1981~2010). Eight clusters were gained from the ISF curves of 96 stations in East Asia. Regions of the cluster C, G and A1 seem to be affected by the Siberian High (SH) center, whereas the cluster A1, A2, D, B and F by the SH main pathways. Regions of the cluster E are apart from the SH main pathways and appear to be in the area of influence of other factors. Wintertime temperatures in Northwest China (clusters C, G) and Northeast China (cluster A1) were increased very largely. In most clusters, around late January there were less warming periods than the winter mean of the mean ISF of the clusters, before and after this time there were more warming periods than the winter mean.

In this study, the yearly mean normalized difference vegetation index(YMNDVI) in Chungcheongnam-do was calculated using S10 NDVI data of the vegetation sensor for SPOT 4 and 5. Based on this calculation, statistical values such as mean value, standard deviation and coefficient of variation were determined. In addition, a comparative analysis was performed by calculating YMNDVI for cities and counties of Chungcheongnam-do. The YMNDVI of Chungcheongnam-do revealed a slight increase during 14 years between 1999 to 2012. However, it showed only a slight change within the range of 0.476 to 0.553, and no significant increase or decrease was noted. As a result, the highest YMNDVI was 0.553 at 2009, the lowest YMNDVI was 0.476 at 2001 and 2006. The mean value of YMNDVI in Chungcheongnam-do for 14 years turned out to be 0.502. As a result of the regional YMNDVI analysis, the highest YMNDVI region was Geumsan-gun, followed by Geryong-si, Cheongyang-gun and Gongjusi. The lowest YMNDVI region was Taean-gun, followed by Dangjin-si, Seosan-si and Seocheon-gun. An analysis of coefficient of variation in the research area showed that the mean value of Chungcheongnamdo was 4.2%, while the overall value was also not that high.

벼의 생산비 절감을 위한 성력재배의 측면에서 전국적으로 확대 실시 보급되고 있는 건답 직파재배 안전성을 기후적으로 검토하고자 출아 조한의 파종기 결정에 대한 유효기준온도인 일평균기온 10℃ 출현초일과 80% 출현시기를 지역별로 분석한 결과, 가. 연차간('73~'92, 20년간) 변이는 일수로서는 약 20~30일, 표준편차(SD)로는 약 5~7일의 차이가 있었고, '88년 이후는 평균 출현초일보다 빨라져 영농면에서 큰 관심이 되고 있음. 나. 지역별 분포(기상청 관측의 56개 지점 분석)는 북부(대관령, 5월 1일)와 남부(부산, 3월 30일)간에는 약 30일 이상의 출현시기에 차이가 있어 우리나라의 기후자원량 분석의 필요성을 느낄 수 있음. 다. 일평균기온 10℃ 평균 출현초일은 80% 출현 시기보다 약 10일 정도 빠른 경향이며 라. 19개의 수도재배농업기후지대별 평균 출현초일과 80% 출현시기의 유사성을 중심으로 다시 단순화시켜 구분하면 19개 지대는 7개의 유형으로 구분할 수 있었음.