• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2003.09a
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• pp.31-34
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• 2003

월별 최고기온 및 최저기온 분포도를 작성하였다. 그림은 4월의 기온으로서, 기온의 변이폭은 최고기온이 9 ~ 19$^{\circ}C$, 최저기온이 -1 ~ 6$^{\circ}C$ 이다. 사과원 0 주변은 최고기온 17$^{\circ}C$, 최저기온 4$^{\circ}C$로 추정된다. 후지품종의 개화는 남서쪽에서 4월 26일 처음 시작되어 서부지역이 4월말, 동부지역이 5월 초에 만개 한다.(중략)

• Journal of the Korean Geographical Society
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• v.39 no.1
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• pp.13-26
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• 2004

This paper examined the possibility of NET application for a relative weather stress index in Korea. The characteristic of NET distribution used temperature, relative humidity, wind speed which forecasting at Korean Meteorological Administration were analyzed. Regional critical values of daily maximum NET of stress index for summer resembled the distribution of daily maximum temperature because were not impacted wind and humidity but temperature. Regional critical values of daily minimum NET of stress index for winter distributed variously compared with summer. The highland region and the northern region of Seoul were impacted of low temperature and coastal region which strong wind. The occurrences of stressful days did not vary in summer, but obviously increased in winter after mid-1990s.

• Journal of Korean Society of Steel Construction
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• v.23 no.3
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• pp.283-294
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• 2011

The variation of temperature in the steel girder bridge by air temperature is measured. A correlation between the daily temperature range, the maximum and minimum temperatures of the day, and the temperature of the bridge are analyzed. With the statistical data from the Korea Meteorological Administration, the temperature correlations analyzed in this study is able to predict temperature variations between the upper flange and the lower flange which calculates the realistic displacement values of a movable support and an expansion joint in design.

• Journal of the Korean Geographical Society
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• v.48 no.1
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• pp.37-55
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• 2013

Previous studies have shown that Foehn was mainly observed in Young-seo area in Korea. However, they have failed to indicate the area where Foehn can be observed most frequently in Young-seo area and how Foehn is distributed in that area. This study targets HongCheon area in Young-seo province and examines the frequency and extent of Foehn in local scale through documenting a daily maximum air temperature map of Foehn. The period examined in this study is the months between March and June from 2003 to 2012. CoKriging method, which uses temperature and the altitude above sea, generates a higher level of accuracy in making daily maximum air temperature map of Foehn occurring days. We have found that Foehn is observed in certain areas, not all areas of HongCheon region, by compiling the daily maximum air temperature map. In particular, Foehn was found to be frequent and strong in the downstream of HongCheon river. In addition, we surveyed the residents of HongCheon about their perception of Foehn. They did not know whether high temperature and dryness in spring are caused by Foehn. The methods and techniques used to examine Foehn in local climate scale by this study will enhance the understanding of regional climate and contribute towards the research in this area. In particular, they can be applied to high temperature that recently occurred between spring and summer, excessive hotness in summers, agricultural plant growth in springs and etc.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2002.11a
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• pp.77-80
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• 2002

기온은 생물의 대사과정에 직접적인 영향을 끼침으로서 생장과 발육을 결정하는 중요한 환경요인이며, 특히 식물은 개체 및 군락 수준에서 기온의 일 변화, 계절변화, 혹은 영년 변화에 반응한다. 최근의 농업 및 삼림 생태계 연구는 기온을 비롯한 환경요인의 영향을 생리과정의 정량적 모의를 근거로 이해하고, 이를 넓은 지역으로 확대하여 다양한 시간적 주기로 예측하는 방향으로 나아가고 있다 (Chung et al., 2002).(중략)

• Korean Journal of Remote Sensing
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• v.15 no.1
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• pp.9-20
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• 1999

The use of climatic information is essential in the industial society. More specialized weather servies are required to perform better industrial acivities including agriculture. Especially, crop models require daily weather data of crop growing area or cropping zones, where routine weather observations are rare. Estimates of the spatial distribution of daily climates might complement the low density of standard weather observation stations. This study was conducted to estimate the spatial distribution of daily minimum and maximum temperatures in Korean Peninsula. A topoclimatological technique was first applied to produce reasonable estimates of monthly climatic normals based on 1km $\times$ 1km grid cell over study area. Harmonic analysis method was then adopted to convert the monthly climatic normals into daily climatic normals. The daily temperatures for each grid cell were derived from a spatial interpolation procedure based on inverse-distance weighting of the observed deviation from the climatic normals at the nearest 4 standard weather stations. Data collected from more than 300 automatic weather systems were then used to validate the final estimates on several dates in 1997. Final step to confirm accuracy of the estimated temperature fields was comparing the distribution pattern with the brightness temperature fields derived from NOAA/AVHRR. Results show that differences between the estimated and the observed temperatures at 20 randomly selected automatic weather systems(AWS) range from -3.$0^{\circ}C$ to + 2.5$^{\circ}C$ in daily maximum, and from -1.8$^{\circ}C$ to + 2.2$^{\circ}C$ in daily minimum temperature. The estimation errors, RMSE, calculated from the data collected at about 300 AWS range from $1.5^{\circ}C$ to 2.5$^{\circ}C$ for daily maximum/minimum temperatures.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.3
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• pp.252-261
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• 2018

This study was conducted to develop a method to generate daily maximum and minimum temperatures using monthly data. We analyzed 30-year daily weather data of the 23 meteorological stations in South Korea and elucidated the parameters for predicting annual trend (center value ($\hat{U}$), amplitude (C), deviation (T)) and daily fluctuation (A, B) of daily maximum and minimum temperature. We use national average values for C, T, A and B parameters, but the center value is derived from the annual average data on each stations. First, daily weather data were generated according to the occurrence of rainfall, then calibrated using monthly data, and finally, daily maximum and minimum daily temperatures were generated. With this method, we could generate daily weather data with more than 95% similar distribution to recorded data for all 23 stations. In addition, this method was able to generate Growing Degree Day(GDD) similar to the past data, and it could be applied to areas not subject to survey. This method is useful for generating daily data in case of having monthly data such as climate change scenarios.

• Journal of the Korean Geographical Society
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• v.42 no.3 s.120
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• pp.332-343
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• 2007

This study aims to examine the distribution of heat waves and to understand its cause for 33 years$(1973\sim2005)$ from 60 weather stations in Korea. Heat wave is defined as a period of 3 or more days with a daily maximum temperature exceeds the 95th percentile. In the inland of Chungcheong region, the Chungcheong western costal region, the inland of Jeolla region, the inland of Gyeongsang region and the southern region of Jeju island, heat wave days appeared more than 160 days. In the middle region of eastern costal and the northern region of Jeju island, heat wave days were less than 110 days. In regions that were heavily influenced by southwesterly winds during the occurrence of heat waves, such as the inland of Chungcheong region, the Chungcheong western costal region, the inland of Jeolla region and the inland of Gyeongsang region, heat waves continued for the longer term.

• Korean Journal of Agricultural and Forest Meteorology
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• v.10 no.1
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• pp.25-31
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• 2008

Information on daily maximum air temperature is important in predicting the status of plants and insects, but the uneven and sparse distribution of weather stations prohibits timely access to the data in regions with complex topography. Since cumulative solar irradiance plays a critical role in determining daily maximum temperature on any sloping surfaces, derivation of a quantitative relationship between cumulative solar irradiance and the resultant daily maximum temperature is a prerequisite to development of such estimation models. Air temperatures at 8 sideslope locations with similar elevation and slope angle but aspect, circumventing a cone-shaped, grass-covered parasitic volcano (c.a., 570 m diameter for the bottom circle and 90m bottom-to-top height), were measured from June to December in 2007. Daily maximum temperatures from each location were compared with the average of 8 locations (assumed to be the temperature measured at a "horizontal reference" position). The temperature deviation at all locations increased with the day of year (or sun elevation) from summer solstice to winter solstice. Averaged over the entire period, the south facing location was warmer by $1^{\circ}C$ in daily maximum temperature than "horizontal reference" and the north facing location was cooler by $0.8^{\circ}C$ than the reference, resulting in the year round average south-north temperature difference of $1.8^{\circ}C$. In November, both south and north facing slopes showed the greatest deviation of $+2.0^{\circ}C$ and $-1.3^{\circ}C$, respectively in daily maximum temperature at monthly scale. On a daily scale, the greatest deviation was +3.8 and $2.7^{\circ}C$ at the south and north slope, respectively. The cumulative solar irradiance (on the slope for 4 hours from 11:00 to 15:00 TST) explained >60% of the variance in daily maximum temperature deviations among 8 locations, suggesting a feasibility of developing an estimation model for daily maximum temperature over complex topography at landscape scales.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.201-217
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• 2022

The United States is one of the largest producers of major crops such as wheat, maize, and soybeans, and is a major exporter of these crops. Therefore, it is important to estimate the crop production of the country in advance based on reliable long- term weather forecast information for stable crops supply and demand in Korea. The purpose of this study is to improve the seasonal predictability of the agro-climatic indices over the United States by using regional-scale daily temperature. For long-term numerical weather prediction, a dynamical downscaling is performed using Weather Research and Forecasting (WRF) model, a regional climate model. As the initial and lateral boundary conditions of WRF, the global hourly prediction data obtained from the Pusan National University Coupled General Circulation Model (PNU CGCM) are used. The integration of WRF is performed for 22 years (2000-2021) for period from June to December of each year. The empirical quantile mapping, one of the bias correction methods, is applied to the timeseries of downscaled daily mean, minimum, and maximum temperature to correct the model biases. The uncorrected and corrected datasets are referred WRF_UC and WRF_C, respectively in this study. The daily minimum (maximum) temperature obtained from WRF_UC presents warm (cold) biases over most of the United States, which can be attributed to the underestimated the low (high) temperature range. The results show that WRF_C simulates closer to the observed temperature than WRF_UC, which lead to improve the long- term predictability of the temperature- based agro-climatic indices.