1991-2020년의 30년 동안 봄철(3-5월)에 북극-동아시아 지역의 지표면 부근 대기 온난화가 북극 진동에 따라 한국의 서울에서 발생하는 황사 사례일의 종관 기상 특성에 미치는 영향을 분석하였다. 북극-동아시아 지역의 봄철 온 난화 증가는 한국의 서울에서 황사 사례일을 6일을 감소시켰고, 황사 사례일의 PM10 질량 농도도 –1.6 g m3 yr1으로 강도를 약화시키는데 기여하고 있었다. 2010년대 한국에서 감소하고 있는 황사 사례일에 대한 동아시아 지역의 종관 기상 특성은 음()의 잠재소용돌이도(Potential Vorticity Unit; PVU)로 나타나는 고기압성 활동이 증가하고 있었다. 또한, 한국에서는 음()의 북극진동지수(Arctic Oscillation Index; AOI)에서 황사 사례일이 증가하고 양(+)에서는 감소하는 정적 편포를 보였다. AOI가 음()인 황사 사례일에서는 중국 대륙에 온난한 고기압이 강화되고 있었다. 더불어 한대 제트의 중심 위치가 북쪽으로 이동하면서 몽골과 중국 북부에서는 한대 기단의 남하에 의한 저기압성 활동이 약해지고 있었다. 황사의 발생이 감소하였을 뿐 아니라 발원지로부터 한국으로 황사를 수송하는 풍속이 감소하고 있었다. 반면, AOI가 양(+)인 황사 사례일에서는 중국 대륙에 광역적으로 온난하고 정체적인 고기압이 위치하고 있었으며, 한대 제트 의 북쪽이 더욱 냉각되어 있었다. 몽골-중국 북부-한국에 이르는 지역에서 하층 대류권의 현저한 풍속 감소가 황사 발 생을 감소시킬 뿐 아니라 장거리 수송을 약화시키는 원인이 되는 것으로 보인다.

겨울철 국내 도로 결빙으로 인한 교통사고가 증가하는 추세를 보이고 있으며 2018년~2022년까지 총 4,609건의 결빙 교통사고가 발 생하였다. 결빙 교통사고의 치사율은 2.3으로 일반적인 교통사고와 비교하여 높은 치사율을 보이며 최근 5년(2018~2022)동안 결빙 교 통사고로 인하여 107명이 사망자와 7,728명의 부상자가 발생하였다. 현재 국토교통부에서 제시한 결빙 취약구간 평가기준표에 따라 결 빙 위험 구간을 지정하고 있으나, 해당 기준은 결빙의 주요 요인으로 고려되는 기상조건을 충분히 반영하지 못하고 있다. 도로 결빙은 노면온도가 0℃ 이하이며 노면에 수분이 공급될 때 형성되며 기온, 구름량, 풍속, 풍향, 상대습도, 강수량 등의 기상인자들이 복합적으 로 작용하여 결빙이 발생한다는 점을 고려하였을 때, 기상 특성은 도로 결빙의 주요 인자로 판단된다. 따라서 국내 결빙 취약구간 평 가기준의 개선이 필요하며 본 연구의 목적은 국내 결빙 교통사고 데이터를 분석하고 결빙이 형성되는 기상 조건을 구체화하는 것이다. 분석을 위한 데이터로 2018년~2022년까지 5년동안 발생한 결빙사고 사례와 기상청 방재기상관측소(AWS)에서 제공하는 기상 데이터 를 적용하였다. 이후, 박스도표, 확률밀도함수 등의 통계분석을 적용하여 결빙 형성 기상 조건을 구체화하였다. 이를 통하여 기존 결빙 취약구간 평가기준의 기상학적 개선 방향성을 제시할 수 있으며 더 나아가 도로 결빙 예측 로직 개발을 기대할 수 있다.

PURPOSES : The purpose of this study is to statistically analyze the meteorological factors that contribute to the formation of road surface icing based on actual cases of icing accidents and provide directions for improving icing evaluation criteria. METHODS : In this study, we collected cases of domestic road icing accidents by searching news articles with the keyword ‘icing collision accidents’. Subsequently, we determined the latitude, longitude, and altitude of accident locations using satellite map service. We applied the Inverse Distance Weighting (IDW) method and temperature lapse rate to estimate meteorological data at each location. Finally, statistical analysis was conducted for temperature, humidity, and precipitation occurrence using probability density functions. RESULTS : As a result, road icing accident data points with identifiable location coordinates were collected. Among these, temperature, humidity, and precipitation occurrence from Automated Weather Stations (AWS) data were selected for analysis. During the process of correcting meteorological factors using the Inverse Distance Weighting (IDW) method, the optimal Weighting Exponent (p) that minimizes the error was determined and applied. The results showed that accidents occurring in the morning indicated the highest accident occurrence rate. The average temperature at the time of the accidents was -1.4°C, with a humidity level of 85.1%. Precipitation was observed at the time of the accident in 19 cases. CONCLUSIONS : Icing on pavement can occur not only under extreme weather conditions but also under typical meteorological conditions. Typically, icing can occur when the relative humidity is above 70%. Accordingly, for future improvements in the evaluation criteria for icing-prone areas by the Ministry of Land, Infrastructure and Transport, it is possible to incorporate the temperature and humidity ranges that generally lead to icing, taking into account climate characteristics.

불안은 주의 시스템의 균형을 깨트려 목표 지향 시스템보다 자극 주도 시스템을 우선하게 만드는 것으로 알려져 있으나, 자기 교시는 자기 조절의 효과로 목표 지향적 행동을 유도하게 한다. 본 연구는 가상현실 환경에서 현직 조종사를 대상으로 기상 및 자기 교시 조건이 조종사에게 발생하는 불안과 비행 과제의 수행에 미치는 영향을 검증 하였다. 기상 조건은 시계비행 기상 상황과 악기상 상황으로 구분하였고 자기 교시의 수행 여부를 달리하여 비행 과제를 수행하게 하였다. 실험 결과 악기상 상황에서 불안과 심박수가 더 높고 비행 과제의 수행도가 더 낮은 것으로 나타났으나, 자기 교시를 수행하는 조건에서는 불안과 심박수가 더 낮고 비행 과제의 수행도가 더 높은 것으로 나타 났다. 이 결과는 불안의 영향으로 비행에 어려움을 겪어 사고로 연결될 가능성이 증가할 수 있으나, 자기 교시에 의한 비행 수행의 향상으로 사고로 연결될 가능성이 감소할 수 있음을 시사한다.

This study was conducted with the aim of confirming the impact and relative contribution of extreme weather to dry matter yield (DMY) of silage corn in the central inland region of Korea. The corn data (n=1,812) were obtained from various reports on the new variety of adaptability experiments conducted by the Rural Development Administration from 1978 to 2017. As for the weather variables, mean aerial temperature, accumulated precipitation, maximum wind speed, and sunshine duration, were collected from the Korean Meteorological Administration. The extreme weather was detected by the box plot, the DMY comparison was carried out by the t-test with a 5% significance level, and the relative contribution was estimated by R2 change in multiple regression modeling. The DMY of silage corn was reduced predominantly during the monsoon in summer and autumn, with DMY damage measuring 1,500-2,500 kg/ha and 1,800 kg/ha, respectively. Moreover, the relative contribution of the damage during the monsoons in summer and autumn was 40% and 60%, respectively. Therefore, the impact of autumn monsoon season should be taken into consideration when harvesting silage corn after late August. This study evaluated the effect of extreme weather on the yield damage of silage corn in Korea and estimated the relative contribution of this damage for the first time.

In this study, we focus on the improvement of data quality transmitted from a weather buoy that guides a route of ships. The buoy has an Internet-of-Thing (IoT) including sensors to collect meteorological data and the buoy’s status, and it also has a wireless communication device to send them to the central database in a ground control center and ships nearby. The time interval of data collected by the sensor is irregular, and fault data is often detected. Therefore, this study provides a framework to improve data quality using machine learning models. The normal data pattern is trained by machine learning models, and the trained models detect the fault data from the collected data set of the sensor and adjust them. For determining fault data, interquartile range (IQR) removes the value outside the outlier, and an NGBoost algorithm removes the data above the upper bound and below the lower bound. The removed data is interpolated using NGBoost or long-short term memory (LSTM) algorithm. The performance of the suggested process is evaluated by actual weather buoy data from Korea to improve the quality of ‘AIR_TEMPERATURE’ data by using other data from the same buoy. The performance of our proposed framework has been validated through computational experiments based on real-world data, confirming its suitability for practical applications in real- world scenarios.

Recently, the importance of impact-based forecasting has increased along with the socio-economic impact of severe weather have emerged. As news articles contain unconstructed information closely related to the people’s life, this study developed and evaluated a binary classification algorithm about snowfall damage information by using media articles text mining. We collected news articles during 2009 to 2021 which containing ‘heavy snow’ in its body context and labelled whether each article correspond to specific damage fields such as car accident. To develop a classifier, we proposed a probability-based classifier based on the ratio of the two conditional probabilities, which is defined as I/O Ratio in this study. During the construction process, we also adopted the n-gram approach to consider contextual meaning of each keyword. The accuracy of the classifier was 75%, supporting the possibility of application of news big data to the impact-based forecasting. We expect the performance of the classifier will be improve in the further research as the various training data is accumulated. The result of this study can be readily expanded by applying the same methodology to other disasters in the future. Furthermore, the result of this study can reduce social and economic damage of high impact weather by supporting the establishment of an integrated meteorological decision support system.

We present a novel method that can enhance the detection success rate of interstellar objects. Interstellarobjects are objects that are not gravitationally bound to our solar system and thus are believed to haveoriginated from other planetary systems. Since the nding of two interstellar objects, 1l/`Oumuamua in2017 and 2l/Borisov in 2019, much attention has been paid to nding new interstellar objects. In thispaper, we propose the use of Heliospheric Imagers (HIs) for the survey of interstellar objects. In particular,we show HI data taken from Solar TErrestrial RElation Observatory/Sun Earth Connection Coronal andHeliospheric Investigation and demonstrate their ability to detect `Oumuamua-like interstellar objects. HIs are designed to monitor and study space weather by observing the solar wind traveling throughinterplanetary space. HIs provide the day-side observations and thus it can dramatically enlarge theobservable sky range when combined with the traditional night-side observations. In this paper, we rstreview previous methods for detecting interstellar objects and demonstrate that HIs can be used for thesurvey of interstellar objects.

PURPOSES : Due to the frequent occurrence of accidents on icy roads during nighttime, it would be advantageous to notify road managers and drivers about the most perilous areas. This would allow road managers to treat the icy roads with de-icing chemicals and enable drivers to be better prepared for potential hazards. Essential information about pavement temperature is required to identify icy spots on the road. METHODS : With the goal of estimating nighttime pavement temperature on the National Highways in Korea using atmospheric data, the current study investigated a widely recognized forecasting method known as deep neural network (DNN). To achieve this objective, the input data for the models were gathered from the weather agency's website. The dataset comprised of relative humidity, air temperature, dew point temperature, as well as the differences in air temperature and humidity between two consecutive days. RESULTS : In order to assess the effectiveness of the built DNN model, a comparison was made using baseline pavement temperature data gathered through an infrared-based pavement temperature sensor installed in a highway patrol car. The results indicated that the DNN model achieved a mean absolute error (MAE) of 0.42 and a root mean square error (RMSE) of 0.62. In comparison, a conventional regression model yielded an MAE of 2.07 and an RMSE of 2.64. Thus, the DNN model demonstrated superior performance in comparison to the conventional regression model. CONCLUSIONS : Considering the increasing focus on preventive maintenance, these newly developed prediction models can be implemented proactively as a preventive measure against icing. This proactive approach has the potential to significantly improve traffic safety on winter roads.

This research was conducted to analyze the cultivation performance and meteorological data of winter rye in Suwon, Gyeonggi Province, and Daegu for 11 years. The objective was to compare the growth and yield of domestically cultivated Korean rye cultivar “Gogu” and identify the factors influencing them, to determine suitable cultivation areas for Korean rye cultivar in the country. The results of the study showed that both Daegu and Suwon regions possess favorable climatic conditions for winter rye cultivation, with Suwon exhibiting a superior moisture supply compared to Daegu. Furthermore, the analysis of climate suitability revealed that rainfall days and precipitation were significant factors affecting rye cultivation. Through correlation and principal component analysis, the research evaluated the interrelationship between climate, cultivation factors, and winter rye crop performance, as well as identified variations among winter rye cultivation regions. This study provides valuable insights and information for winter rye cultivation in the country, thereby assisting in the decision-making process for selecting optimal cultivation areas.

본 연구는 우리나라에서 수수-수단그라스 교잡종 (sorghum bicolor L.: SSH)에 대해 극단기상과 정상기상 간 생산량을 비교할 목적으로 수행하였다. SSH 데이터 (n=1,025)는 농촌진흥청의 신품종 적응성 실험보고서(1979 ―2019)로부터 수집하였다. 기상자료는 기상청으로부터 평균기온, 최저기온, 최고기온, 최대 강수량, 누적 강수량, 최대풍속, 평균풍속 및 일조시간을 10일 기준으로 계산하 여 수집하였다. 극단기상과 정상기상 간 구별을 위해 상 자 그림을 이용하여 탐색하였다. 극단기상과 정상기상 간 생산량 차이는 5% 유의수준 하에서 t-검정 및 ANOVA를 통해 확인하였다. 그 결과, 극단기상은 극단적으로 강한 바람을 동반한 봄 가뭄, 극단적으로 높은 강우량을 기록 하는 여름장마와 가을장마가 두드러졌다. 예측 생산량 피 해(kg/ha)는 각각 1,961―6,541, 2,161―4,526 및 508― 5,582로 나타났다. 본 연구는 우리나라의 SSH에 대한 취 약성 및 피해 산정에 도움이 되는 기초자료로서 극단기상 과 정상기상 사이의 생산량 차이를 확인하는 데 의의가 있다.

항공기는 대표적인 인간-기계시스템이다. 인간의 조작과 기계의 작동 완료 시점 사이에는 기계가 작동되기 시작하 는 시간과 기계에 힘이 전달되기 시작하여 완료되는 시간 등의 지연이 발생하며 항공기 조종은 시스템의 지연을 예 측한 타이밍 작업을 통해 이루어진다. 시간지각은 타이밍 작업의 중요한 요소이며, 높은 각성작용과 관련된 불안에 영향을 받는 것으로 알려져 있다. 본 연구는 가상현실 환경에서 현직 조종사를 대상으로 기상, 비행 및 시간 조건이 조종사에게 발생하는 불안과 시간지각에 미치는 영향을 검증하였다. 기상조건은 시계비행 기상 상황과 악기상 상황 으로 구분하였고 비행 및 시간 조건을 달리하여 실험 1, 2를 실시하였다. 실험 1은 비교적 운동량의 변화가 적고 지연이 적은 제자리비행과 수평비행 상황에서 조종간에 추가된 버튼을 사용하여 시간지각을 측정하였다. 실험 2는 운동량의 변화가 많고 지연이 많이 발생하는 이륙상황에서 조종간만을 사용하게 하여 자연스럽게 헬리콥터를 이륙 시키는 과정에서 시간지각을 측정하였다. 실험결과 악기상 상황에서 불안과 심박수가 증가하는 것으로 나타났으며, 특히 실험 1, 2의 모든 비행조건 중 불안이 증가한 상황에서 시간을 과대 추정하는 것으로 나타났다. 이 결과는 불안 의 영향으로 시간을 과대 추정하여 타이밍 작업을 실패할 수 있으며, 이로 인해 조종에 어려움을 겪고 사고로 연결될 가능성이 있음을 시사한다.

This study was conducted to estimate the damage of Whole Crop Corn (WCC; Zea Mays L.) according to abnormal climate using machine learning as the Representative Concentration Pathway (RCP) 4.5 and present the damage through mapping. The collected WCC data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. The machine learning model used DeepCrossing. The damage was calculated using climate data from the automated synoptic observing system (ASOS, 95 sites) by machine learning. The calculation of damage was the difference between the dry matter yield (DMY)normal and DMYabnormal. The normal climate was set as the 40-year of climate data according to the year of WCC data (1978-2017). The level of abnormal climate by temperature and precipitation was set as RCP 4.5 standard. The DMYnormal ranged from 13,845-19,347 kg/ha. The damage of WCC which was differed depending on the region and level of abnormal climate where abnormal temperature and precipitation occurred. The damage of abnormal temperature in 2050 and 2100 ranged from -263 to 360 and -1,023 to 92 kg/ha, respectively. The damage of abnormal precipitation in 2050 and 2100 was ranged from -17 to 2 and -12 to 2 kg/ha, respectively. The maximum damage was 360 kg/ha that the abnormal temperature in 2050. As the average monthly temperature increases, the DMY of WCC tends to increase. The damage calculated through the RCP 4.5 standard was presented as a mapping using QGIS. Although this study applied the scenario in which greenhouse gas reduction was carried out, additional research needs to be conducted applying an RCP scenario in which greenhouse gas reduction is not performed.

To understand functional changes of forest ecosystems due to climate change, correlation between decomposition rate of leaf litter, an important function of forest ecosystems, and microclimatic factors was analyzed. After 48 months elapsed, percent remaining weight of Quercus mongolica leaf litter was 27.1% in the east aspect and 37.0% in the west aspects. Decay constant of Q. mongolica leaf litter was 0.33 in the east aspect and 0.25 in the west aspect after 48 months elapsed. Initial C/N ratio of Q. mongolica leaf litter was 38.5. After 48 months elapsed, C/N ratio of decomposing Q. mongolica leaf litter decreased to 13.43 in the east aspect and 16.72 in the west aspect. Average air temperature and soil temperature during the investigation period of the research site were 8.2±9.0 and 9.1±9.3 in the east and 8.5±7.4 and 9.3±7.3°C in the west aspect, respectively, with the west aspect showing higher air and soil temperatures. Soil moisture showed no significant difference between east and west aspects (average soil moisture: 19.4±11.0% vs. 20.5±5.7%). However, as a result of analyzing the correlation between decomposition rate and microclimatic factors, it was found that the decomposition rate and soil moisture has a positive correlation (r=0.426) in the east aspect but not in the west aspect. Our study shows that the correlation between decomposition rate and microclimatic factors can be significantly different depending on the direction of the aspect.