This study was conducted to determine the possibility of estimating the daily mean temperature for a specific location based on the climatic data collected from the nearby Automated Synoptic Observing System (ASOS) and Automated Weather System(AWS) to improve the accuracy of the climate data in forage yield prediction model. To perform this study, the annual mean temperature and monthly mean temperature were checked for normality, correlation with location information (Longitude, Latitude, and Altitude) and multiple regression analysis, respectively. The altitude was found to have a continuous effect on the annual mean temperature and the monthly mean temperature, while the latitude was found to have an effect on the monthly mean temperature excluding June. Longitude affected monthly mean temperature in June, July, August, September, October, and November. Based on the above results and years of experience with climate-related research, the daily mean temperature estimation was determined to be possible using longitude, latitude, and altitude. In this study, it is possible to estimate the daily mean temperature using climate data from all over the country, but in order to improve the accuracy of daily mean temperature, climatic data needs to applied to each city and province.

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.

Intergovernmental Panel on Climate Change (IPCC) provides various prospects of future climate change under the Representative Concentration Pathways (RCP) scenarios using General Circulation Models (GCMs) of Coupled Model Intercomparison Project (CMIP). This paper describes a modified application of Ensemble Bayesian Model Averaging (EBMA) to produce daily mean temperature ensembles using 19 GCMs provided by CMIP. We proposed two types of approach: (1) monthly weighting scheme for a whole area (EBMA.v1) and (2) monthly weighting for each grid point (EBMA.v2), which can take into account the spatially heterogeneous pattern of GCM. For the training period of 1979- 2005 for East Asia, 9,855 sets of daily temperature ensembles (27 years × 365 days) were produced and compared to the ERA-Interim reanalysis data of European Centre for Medium-Range Weather Forecasts (ECMWF), which showed better validation statistics than the general mean and median ensembles. In particular, EBMA.v2 outperformed EBMA.v1 by diminishing the large errors of inland areas where the surface heterogeneity is larger than the ocean. The EBMA.v2 was able to handle the problem of spatial variability by employing monthly and spatially varying weighting scheme. We finally produced daily mean temperature ensembles for the period of 2006-2100 by using the EBMA.v2 under the RCP 6.0 scenario, which are going to be provided on the web.

In this paper, for selected station of 8 clusters in East Asia (Park, 2017) more (less) warming periods than the wintertime mean warming of intra-seasonal fluctuation curves were taken and their means were computed. Long term trends and synoptic features of the mean temperature changes were examined. In most clusters, around the third of January there were less warming periods (LWP) than the mean wintertime warming. On the contrary, in February and the first and second of January there were more warming periods (MWP) than the winter mean or LWPs having a warming trend with statistical signicance. Time series of the daily Siberian High indices showed they had been weakening in February and being stagnant around late January. In most stations, the mean temperatures of MWP or LWP had large negative correlation coecients with the Siberian high intensity. is result explains the occurrences of MWPs in most clusters in February and LWPs in late January. In cluster B there were LWPs in early February due to the influence of the Aleutian Low which were strengthening in that periods. Cluster E showed different features without LWPs in late January. The cluster is considered to be affected by its plateau environment of West Yúnnán and the Tibet Plateau which prevent cold air of the lower atmosphere in Northern Asia flowing southward, and by the regional atmospheric circulation of 500hPa surface centered in this region.

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.

벼 건답직파재배의 파종 조한기를 전국의 기후자료 분석에 의해 구명하고자 기상청 56개 기상관측지점의 기온출현특성을 분석하였다. 출아소요일수가 짧으면서 출아일수의 변이도 적고, 출아립묘도 안정하게 확보할 수 있는 파종 조한기 결정의 유효기준온도인 일평균기온 13℃ 의 20년간 평균 출아초일과 80% 출현시기를 지역별로 분석한 결과, 가. 일평균기온 13℃ 의 출현초일의 일중기온변이는 낮의 18~19℃ , 밤의 6~10℃ 로서 발아 및 출아에 안전한 기온변이였으며, 나. 연차간('73~'92, 20년간) 변이는 일수로서 약 30~40일, 표준편차(SD)로는 약 8~10일의 차이가 있었고, '88년 이후는 평균 출현초일보다 빨라져 영농에 큰 관심이 되고 있으며, 다. 지역별 분포(기상청. 관측의 56개 지점 분석)는 평균 출현초일이 중북부의 대관령지역은 5월 19일이고 남부의 부산지역은 4월 12일 경이며, 80%출현시기는 중북부의 대관령지역이 5월 29일이고 남부의 합천지역이 4월 21일로서 지역간의 차이가 커서 위도 및 표고에 따른 세밀한 분석이 요구되며, 라. 파종 조한기는 일평균기온 13℃ 의 평균 출현초일부터 80%출현시기까지이며, 80%출아시기는 평균 출현초일보다 약 10일 늦게 나타났음. 마. 19개의 수도재배 농업기후지대별 평균 출현초일과 80% 출현시기의 유사성을 중심으로 다시 단순화시켜 구분하면 19개의 지대는 5개의 유형으로 구분됨.

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.