The relationship between night cooling rate and meteorological elements was investigated over the past five years (2016-2020), using weather data from the new (Daegu(143)) and old (Shinam(860)) Daegu Regional Meteorological Agency located in the suburban and urban regions, respectively. There was a correlation between the total daily amount of solar radiation (Stot) and the night cooling rate in the both regions. However, a higher correlation was observed at the new Daegu Regional Meteorological Agency station (Daegu(143)). In particular, data from the new Daegu Regional Meteorological Administration's observatory, which experiences a low thermal storage effect caused by artificial structures, showed a higher correlation between nighttime cooling and weather factors. The reason for this is that the lesser the heat storage effect caused by the artificial structures, the better the effect of surface radiation cooling on temperature reduction. These findings confirm that the correlation between night cooling and weather factors can be used to assess the impact of artificial structures in cities.

This study investigates the temporal and spatial variations of marine meterological elements (air temperature (Temp), Sea Surface Temperature (SST), and Significant Wave Height (SWH)) in seven coastal waters of South Korea, using hourly data observed at marine meteorological buoys (10 sites), Automatic Weather System on lighthouse (lighthouse AWS) (9 sites), and AWS (20 sites) during 2013-2017. We also compared the characteristics of Temp, SST, and air-sea temperature difference (Temp-SST) between sea fog and non-sea-fog events. In general, annual mean values of Temp and SST in most of the coastal waters were highest (especially in the southern part of Jeju Island) in 2016, due to heat waves, and lowest (especially in the middle of the West Sea) in 2013 or 2014. The SWH did not vary significantly by year. Wind patterns varied according to coastal waters, but their yearly variations for each coastal water were similar. The maximum monthly/seasonal mean values of Temp and SST occurred in summer (especially in August), and the minimum values in winter (January for Temp and February for SST). Monthly/seasonal mean SWH was highest in winter (especially in December) and lowest in summer (June), while the monthly/seasonal variations in wind speed over most of the coastal waters (except for the southern part of Jeju Island) were similar to those of SWH. In addition, sea fog during spring and summer was likely to be in the form of advection fog, possibly because of the high Temp and low SST (especially clear SST cooling in the eastern part of South Sea in summer), while autumn sea fog varied between different coastal waters (either advection fog or steam fog). The SST (and Temp-SST) during sea fog events in all coastal waters was lower (and more variable) than during non-sea-fog events, and was up to 5.7℃ for SST (up to 5.8℃ for Temp-SST).

Recent climate change has led to fluctuations in agricultural production, and as a result national food supply has become an important strategic factor in economic policy. As such, in this study, panel data was collected to analyze the effects of seven meteorological elements on the production of five types of grain with error component panel data regression method following the test results of LM tests, Hausman test. The key factors affecting the production of rice were average temperature, average relative humidity and average ground surface temperature. The fluctuations in the other four grains types are not well explained by meterological elements. For other grains and beans, only average temperature and time (year) affect the production of other grains while average temperature, ground surface temperature, and time (year) influence the production of beans. For barley and millet, only average temperature positively affects the production of barley while ground surface temperature and time (year) negatively influence the production of millet. The implications of this study are as follow. First, it was confirmed that the meteorological elements have profound effects on the rice production. Second, when compared to existing studies, this study was not limited to rice but encompassed all five types of grains and went beyond other studies that were limited to temperature and rainfall to include various meteorological elements.

In order to clarify the characteristics of Photo-Volatic(PV) power generation over the Korean peninsula with complex terrain, special meteorological observation campaign was carried out for one year from 25 May 2011. Analysis is based on the comparison between observed meteorological elements and PV values generated at rated capacity 200 kW power plants. Solar radiation observed at 15° inclined surface is 11 % larger than that observed at horizontal surface due to low elevation angel of the sun during winter season. The PV power generation tend to be more similar the variation of inclined surface irradiance than horizontal surface irradiance. Increasing air temperature often induce disturbance of the PV power generation. However, the higher the air temperature in winter season, the higher PV power generation because the PV module may be more activated at higher air temperature. PV generating efficiency tends to be conversed the value of 15%.

Recent climate change has led to fluctuations in agricultural production, and as a result national food supply has become an important strategic factor in economic policy. As such, in this study, panel data was collected to analyze the effects of seven meteorological elements and using the Lagrange multipliers method, the fixed-effects model for the production of five types of food crop and the seven meteorological elements were analyzed. Results showed that the key factors effecting increases in production of rice grains were average temperature, average relative humidity and average ground surface temperature, while wheat and barley were found to have positive correlations with average temperature and average humidity. The implications of this study are as follow. First, it was confirmed that the meteorological elements have profound effects on the production of food crops. Second, when compared to existing studies, the study was not limited to one food crop but encompassed all five types, and went beyond other studies that were limited to temperature and rainfall to include various meterological elements.

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.

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 m2, weight of 1,000 grains and seed yield were positively significant at the level of 5.1 % respectively.

This study was conducted to investigate the relationships between yearly variations of meteorological elements and yearly variations of productivity in rice. In addition, correlation coefficients among yield and yield components were used to find out the relationships between meteorological elements and productivity. Yearly variation of the mean air temperature in May was large with coefficients of variation(C.V.) of 25.0%, but the variation of the duration of sunshine in May were relative small. No. of panicles per hill and 1,000 grains wt. of brown rice were great with C.V. of 21.1, 19.7%, respectively, brown rice yield show more or less C.V. of 5.5% and milled rice show still less variation. Correlation coefficients between temperatures in period of cultivation from May and yield were positive correlations. Correlation Coefficients between precipitation in period of cultivation from Sep. to Oct. and yield are positive correlations. Correlation coefficients amount the panicle length, no. of panicles, no. of spikelets, ratio of ripened grains, 1,000 grains wt. of brown rice, milled rice yield, brown rice yield and milled rice yield were positively significant at the level of 1 %, respectively.

This study was conducted to investigate the relationships between yearly variations of meteorological elements and yearly variations of productivity in hot pepper. In addition, correlation coefficients among the yields and yield components were used to find out the relationships between meteorological elements and productivity. Yearly variation of the mean air temperature in May and July showed large with coefficients of variation(C.V.) of 25.0,8.9%, respectively, but the variation of the duration of sunshine in May were relative small. Yield and plant height was greatly with C.V. of 7.14,11.6%, respectively, diameter of fruit showed more or less C.V. of 2.28% and length of fruit showed less variation. Correlation coefficients between maximum temperature in period of cultivation from May and yield are positively significant at the level of 1 %. Correlation Coefficients between precipitation in period of cultivation from May to August and yield are negative significant at the level of 5 and 1 %, respectively. Correlation coefficients amount the plant height, length of fruit, diameter of fruit per plant, and seed yield were positively significant at the level of 1 %, respectively.

1987년부터 1995년까지 전남 고흥군의 농가포장에서 감자의 주요특성과 재배기간중 관측된 기상자료를 이용하여 생육 및 수량과 기상요인과의 상관관계, 분석 및 수량추정식을 유도한 결과는 다음과 같다. 1.기상요인 중 변이가 큰 것은 3월과 4월의 최저기온으로서 변이계수는 각각 368.0%, 126.0%였으며 5, 6월의 평균기온, 최고기온 및 최저기온은 비교적 변이가 적 었다. 2. 생육 및 수량형질의 변이계수에서 수량은 3.7%로 매우 낮아 품종고유의 유전특성의 지배를 많이 받는 반면, 경장은 14.3%, 경수는 9.3%로 높아서 어느정도 환경요인에 영 향을 받는것으로 나타났다. 3. 4월의 강수량 및 6월의 평균기온과 수량간에는 부의 상관으로 나타났다. 4. 생육 및 수량간에 는 모두가 정의 상관으로 유의성이 인정되었으며 4월의 강수량을 이용하여 경장을 추정한 결과 Y1: 82.47-0.11X (R2=0.3959)의 직선 회귀식을 유도할 수 있었고, 역시 4월의 강수량을 이용하여 수량을 추정한 결과 Y2: 2003.61-0.94X (R2=0.5418)의 직선 회귀식을 얻을 수 있었으며 이들에 대한 분산분석에서도 유의성이 인정되었기에 수량에 대한 추정식을 이용하여 이론적 수량과 실제 수량의 오차를 구한 결과 값이 같아서 잘 적중되었다.

Meteorological measurements were carried out in the basin of Cheong-Kwan located Yang-San near Pusan city, from Oct. 30 to Nov. 1, 1988. According to the measured data, we verified the close relationship between the variation of nocturnal inversion layer and the meteorological elements. The nocturnal inversion layer by radiative cooling in this site extends up to 130 meters or so. And the nocturnal jet appears just above or at the top of the inversion layer, and the stronger of the prevailing wind blows, and the lower of the jet level appears. Some meteorological features such as heating, cooling etc., began to change in or in the slightly higher level of the inversion layer, when they are formed, reinforced and disappeared. And the air in the basin preserves its character because it is not affected by local scale air flow.

본 연구는 호프 주산지인 강원도 횡성에서 호프 발육기 동안의 기상요소 변화에 따른 α -acid 함량 예측모형을 작성하고, α -acid 생성에 관여하는 기상요소를 구명하고자 1978년부터 1986년까지 9개년 간의 연평균 α -acid 함량과 발육단계별 기상요소를 분석 검토한 결과는 다음과 같다. 1. α -acid 함량 예측을 위하여 선택된 기상요소는 화아분화기(5월 21일~6월 20일)의 최고기온, 개화기(6월 11일~7월 10일)의 최고기온, 일조시수 그리고 강수량, 구화형성기(7월 21일~8월 20일) 최고기온이었다. 2. 개화기의 일조시수(X1 ), 개화기의 최고기온(X3 ), 개화분화기의 최고기온(X4 ), 개화기의 강수량(X5), 그리고 구화성숙기의 최고기온(X6)의 α -acid 함량 증가에 영향을 주었다. 3. α -acid 함량 예측의 중선형 회귀모형은 Y=28.369-0.003 X1 ＋1.508 X2 -1.953X3 -0.335X4 -0.003X5-0.119X6로 MSEp=0.004, Rp2 =0.9987, Rap2 =0.9949, Cp=7.00이었다.

본 연구는 호프 주산지인 강원도 횡성에서 기상요소의 변화에 따른 수량 예측 모형식을 작성하고, 수량에 관여하는 주요 기상요소와 호프 생리적 작용관계를 구명하고자 1978년부터 1986년까지 9개년간의 년도별 호프 각 생육단계의 기상요소와 4년생 이상 호프의 년평균 수량을 농촌진흥청 VAX 11/785 전산기 AGRISP에 입력 분석하여 검토한 결과는 다음과 같다. 1. 수량 예측을 위하여 선택된 기상요소는 초기생장기(4월 21일~5월 20일)의 최저기온과 평균기온, 화아분화기(5월 21일~6월 20일)의 최고기온, 개화기(6월 11일~7월 10일)의 최고기온, 구화형성기(7월 1일~7월 31일)의 일조시수, 구솨 성숙기(7월 21일~8월 20일)의 강수량이었다. 2. 초기 생장기의 최고기온( X3 )과 개화기의 최고기온( X4 )은 수량 감소에 영향을 주었고, 초기 생장기의 평균기온( X5 )과 구화형성기의 일조시수( X6)는 수량 증가에 영향을 주었다. 3. 수량예측의 중선형 회귀모형식은 Y=6042.846-17.665 X1 -0.919 X2 -96.538 X3 -138.105 X4 ＋86.910 X5 ＋ X6로 MS Ep=25.258, Rp2=0.9991, Rap 2 =0.9962, Cp=7.00이었다.00이었다.