• 한국작물학회지
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• 제59권1호
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• pp.89-96
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• 2014

1980년부터 2012년까지의 전국 92개소의 기상청 기상자료를 수집하고 전국의 감자 지역적응시험 성적을 수집하였다. 이 데이터들을 활용하여 기상요소가 감자수량에 미쳤던 영향을 평가하고 기상자료를 활용, 감자 수량을 예측해 보고자 하였다. 노지 무피복 재배 수미감자를 대상으로 전국 17지역의 86개 지역적응시험 성적을 추출하여 해당지역의 기상요소들간 상관계수를 조사한 결과, 감자의 상서수량은 파종일부터 50일간의 평균기온, 최고기온 및 일교차와 고도의 상관이 나타났고, 수확 50일전부터 수확일까지의 최고기온과도 고도의 상관이 있었으며, 수확 30일전부터 10일전까지의 강수량, 상대습도, 일조시간 및 강수일수도 높은 상관이 나타났다. 이들 시기별 기상요소들과 감자 상서수량간의 관계를 통계분석 프로그램 SAS를 이용하여 단계분석(Stepwise)한 결과, 다음과 같은 감자 수량예측 모형을 얻을 수 있었다. $$y=7.820{\times}Tmax_-1-6.315{\times}Prec_-4+128.214{\times}DR_-8+91.762{\times}DR_-3+643.965$$ 감자는 품종마다 기상에 대한 반응이 다르고, 기상 이외에도 토양, 비료, 재배방법 등 여러가지 가능한 요인들이 존재하므로 이 모형만으로 우리나라 지역별 감자수량을 정확히 예측할 수는 없겠으나, 기후변화에 적응하는 농업기술개발을 위한 지역별 감자 파종적기 재설정, 재배적지 탐색 등에 활용할 수 있을 것으로 판단된다.

• 설비공학논문집
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• 제24권8호
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• pp.599-604
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• 2012

Most air pollution and smog are a result of the burning of fossil fuels. The use of fossil fuels also causes acid rain and global warming. So the need for solar energy utilization is increased. It is essentially important to make efforts to reduce usage of fossil energy resources. In this study, we analyzed the correlation between climatic elements(Cloud cover, Duration of sunshine, Temperature) and the photovoltaic power generation. Cloud cover of the correlation coefficient was 0.87. And duration of sunshine of the correlation coefficient was 0.93. The order of the correlation coefficient was duration of sunshine, cloud cover, temperature. To accurately analyze of the degree of correlation for the photovoltaic power generation, additional research about climatic elements that show a high correlation is needed.

• 한국태양에너지학회 논문집
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• 제32권1호
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• pp.8-14
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• 2012

Concerning about global warming due to emission of greenhouse effect gas like C02 and depletion of fossil fuels have been spreading. So the need for solar energy utilization is increased. It is essentially important to make efforts to reduce usage of fossil energy resources. In this study, we analyzed the correlation between climatic elements and the photovoltaic power generation. Cloud cover of the correlation coefficient was 0.93. The order of the correlation coefficient was average temperatures, hours of sunshine duration of sunshine and the humidity. To accurately analyze of the degree of correlation for the photovoltaic power generation, additional research about climatic elements that show a high correlation is needed.

• Plant Resources
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• 제6권3호
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• pp.178-182
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• 2003

This study was conducted to investigate the relationship between yearly variations of climatic elements and yearly variations of productivity in perilla. In addition, correlation coefficients among yield and yield components were estimated. The data of yield and yield components were investigated for 10 years from 1991 to 2000. The meteorological data gathered at the Yeosu Weather Station for the same period were used to find out the relationships between climatic elements and productivity. Yearly variation of the amount of precipitation in September was large with coefficients of variation(c. v.) of 11.1%, but the coefficient of variance(c. v.) in July and August were relative small with 1.8, 2.1%, respectively. Number of cluster per hill and weight of 1,000 grains were greatly with c. v. of 76.1, 79.3%, respectively, but the coefficients of variance(c. v.) of plant height and seed yield were more less with 9.58, 10.60%, respectively. Correlation coefficients between precipitation of September and seed yield were positively significant correlation at the level of 5.1%, respectively, but the duration of sunshine in September and seed yield were negatively significant at the level of 5.1%, respectively. Correlation coefficients of these, the plant height, number of branches per plant, cluster length, number of cluster per hill, weight of 1,000 grains and seed yield were positively significant at the level of 5.1% respectively.

• Plant Resources
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• 제5권3호
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• pp.196-201
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• 2002

This study was conducted to investigate the relationships between yearly variations of climatic elements and yearly variations of productivity in sesame. In addition, correlation coefficients among yield and yield components were estimated. The data of yield and yield components were investigated for 10 years from 1992 to 2001. The meteorological data gathered at the Yeosu Weather Station for the same period were used to find out the relationships between climatic elements and productivity. Yearly variation of the amount of precipitation in July and September were large with coefficients of variation(c.v.) of 64.59, 92.47%, respectively, but the variation of the average temperature in June and August were relative small. Yield and plant height greatly with c. v. of 26.24, 23.41 %, respectively, 1, 000 grain weights show more or less c.v. of 3.83% and length capsule setting show still less variation. Correlation coefficients between maximun temperature in period of cultivation(from June to September) and yield are positively significant at the level of 5.1 %, respectively. Correlation coefficients amount the plant height, length capsule setting, number of capsules per plant, weight of 1, 000 grains and seed yield were positively significant at the level of 1 %, respectively. Simple linear regression equations by the least square method are estimated for number of capsules per plant(Y$_1$) and the maximun temperature in August(X) as $Y_1$=10.1255+0.1725X, and for yield(Y$_2$) and the maximun temperature in August(X) as $Y_2$=21.6151 + 1.3724X.

• 한국수자원학회논문집
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• 제47권11호
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• pp.1039-1050
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• 2014

본 연구에서는 전지구 기후모델의 성능을 평가함에 있어 기후 요소와 평가 지표에 따른 분석 결과의 다양성에 대해 살펴보고자 하였다. 미국 남동부 지역을 대상으로 17개의 CMIP5 GCM의 강우량, 일 최대 최저기온, 풍속에 대한 과거기간(1950~2000)의 모의 결과를 같은 기간의 관측치와 비교한 오차와 상관도를 이용하여 정량적으로 평가하였다. 기후 모델 산출물을 효과적으로 분석하기 위해 격자 단위 관측 자료를 평가기준으로 사용하였으며 다양한 형태의 기상 특성에 대한 모의 성능을 다각적으로 진단하기 위해 기후 정보(평균적 기후 통계량, 시간 변동성, 극한 사상 빈도 등)를 16개 지표로 정의하여 평가에 적용하였다. 또한 산정된 오차와 상관도를 기반으로 대상지역에 대한 기후요소별 GCM 성능 순위를 도출하여 비교하였다. 연구 결과, 기온에 대한 기후 특성에 대한 모델 재현성은 전반적으로 뛰어난 반면 강우량 및 풍속에 대한 모델 성능은 일 변동성을 제외한 대부분 지표들에 대해 비교적 낮은 것으로 나타났다. 더불어 모델의 정확도 순위는 기후 요소, 평가 지표, 그리고 오차 산정 방법에 따라 다양하게 나타남을 확인하였다. 특히 IPSL-CM5A-LR 모델은 대상지역에 대한 적용성이 현저히 낮은 것으로 나타났다. 본 연구는 다양한 기후변화 영향 연구에 적합한 모델 선정과 기후 모델의 불확실성을 고려한 합리적 미래 예측을 위해서는 다각적이고 면밀한 모델 평가가 선행되어야 함을 시사한다.

• 대한지리학회지
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• 제33권1호
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• pp.17-40
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• 1998

기후지역 구분(기후환경 유형화)의 가장 중요한 역할은 기후환경의 실태를 나타내 그 대세를 알기 쉽게 표현하는 귀납적 일반화를 행하기 위한 수단이다. 본 연구는 기후대표성과 그 범위의 체계적인 과학적 설정을 위해 한반도의 기후특성을 잘 반영하고 있는 13개 기후요서(강수일, 강수량, 일기엔트로피, 기온, 신적설, 상대습도 등)를 선정하고, 이에 관해 수집된 자료를 많은 변수 처리와 공간 및 속성자료를 동시에 분석해야 할 필요성으로, GIS 분석기능과 연계하여 다변량해석법의 일종인 주성분분석법, 정보이론에 의한 엔트로피아 정보비 개념을 사용해 기후요소별로 유형화를 시킨 후, 이들을 다시 GIS를 이용 중첩시켜 기후특성에 관한 속성값을 담고 있는 플라곤들을 대상으로 군집분석하여 한국 기후환경에 대한 종합적 유형화를 시도하였다. 이 결과 일기의 평균 정보량인 일기엔트로피와 그 정보비는 일기출현의 특성을 잘 나타내므로 그것을 측도로 하는 기후환경의 지역별 일기 대표성 및 범위의 설정이 가능하였고, 주성분분석법을 응용하여 추출된 기후요소들의 연변화형의 주성분 백터와 진폭계수는 실제의 기후요소들의 분포와 연변화 특징을 잘 나타내, 이를 기초로 하는 한국의 기후환경을 유형화하였으며, 위의 유형화된 결과물을 GIS 지도대수와 통계분석 프로그램인 S-Plus 및 SAS를 연계시켜 분석한 결과 형성된 997개 기후유형과 이 997개 유형에 포함된 109개 변량을 대상으로 군집분석을 하여, 남한의 기후환경을 3구분 즉, 대구분(8), 중구분(26), 소구분(48)으로 유형화시킨 결과를 얻어냈다.

• 대한지리학회지
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• 제36권
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• pp.13-25
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• 1987

이 글에서는 다음의 내용을 다루었다. 1. 주성분분석의 결과 및 고찰 2. 기후구분

• Plant Resources
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• 제6권3호
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• pp.159-164
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• 2003

This study was conducted to investigate the relationship between yearly variations of climatic elements and yearly variations of productivity in malting barley. In addition, correlation coefficients among yield and yield components were estimated. The data of yield and yield components were investigated for 10 years from 1991 to 2000. The meteorological data gathered at the Yeosu Weather Station for the same period were used to find out the relationships between climatic elements and productivity. Yearly varation of the amount of precipitation in December and January were large with coefficients of variation(c. v.) of 97.9, 51.3%, respectively, but the variation of the maximum temperature and minimum temperature in April were relative small. Yield, weight of 1,000 grains and culm length were greatly with c. v. of 37.3, 49.3 and 41.3%, respectively. spike length and number of spikes show more or less c. v. of 3.8, 24.7% respectively and number of grains per spike show still less variation with c. v. of 9.4%. Correlation coefficients between temperature of mean, maximum and minimum in February and seed yield and yield components were positively significant at level of 5.1%, respectively. Correlation coefficients between precipitation of April and seed yield were positively significant correlation at the level of 5.1 %, respectively, but the duration of sunshine in April and seed yield were negatively significant at the level of 5.1%, respectively. Correlation coefficients of those, yield components and yield, culm length, spike length, number of grains per spike, number of spikes per $m^2$, weight of 1,000 grains and seed yield were positively significant at the level of 5.1 % respectively.

• 한국자원식물학회:학술대회논문집
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• 한국자원식물학회 2000년도 춘계임시총회 및 학술발표대회
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• pp.26-33
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• 2000

This study was conducted to investigate the relationships between yearly variations of elimatic elements and yearly variations of productivity in potato. In addition, correlation coefficients among yield and yield components were estimated. The data of yield and yield components were investigated for 9 years from 1987 to 1995. The meteorological data what gathered at the Goheung Weather Station for the same period of crop growing season were used to find out the relationships between climatic elements and crop productivity. Yearly variation of the daily minimum temperature in March and April were large with coefficients of variation (C.V.) of 126.0%, 368%, respectively, but the variation of the daily mean and maximum temperature in May and June were relative small. Stem length and number of stem show more C.V. of 9.3%, 14.3%, respectively, but the variation of the yield was relative small with 3.7%. Correlation coefficients between the amount of precipitation in April and yield, yield and daily mean temperature in June were negatively significant at the level of 5, 1 %, respectively. Correlation coefficients between the growth habits and yield are positively significant at the level of 5, 1 %, respectively. Simple linear regression equations by the least square method are estimated for stem length (Yl) and the precipitation in April(X) as Y,=82.47-0.11x (R2=0.3959), and for yield(Y2) and the precipitation in April(X) as Y,=2003.61-0.94X (R2=0.5418).