• 통합자연과학논문집
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• 제17권3호
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• pp.93-103
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• 2024

Mongolia's diverse geographical landscape and harsh climate make it particularly susceptible to various natural disasters, including forest fires, heavy rains, dust storms, and heavy snow. This study aims to explore the relationships between key climatic variables and the frequency of these disasters. We collected monthly data from January 2022 to April 2024, encompassing average temperature, temperature variability (absolute temperature difference), average humidity, and precipitation across the capitals of Mongolia's 21 provinces and the capital city Ulaanbaatar. The data were analyzed using multiple statistical models: Linear Regression, Poisson Regression, and Negative Binomial Regression. Descriptive statistics provided initial insights into the variability and distribution of the climatic variables and disaster occurrences. The models aimed to identify significant predictors and quantify their impact on disaster frequencies. Our approach involved standardizing the predictor variables to ensure comparability and interpretability of the regression coefficients. Our findings indicate that climatic variables significantly affect the frequency of natural disasters. The Negative Binomial Regression model was particularly suitable for our data, which exhibited overdispersion common characteristic in count data such as disaster occurrences. Understanding these relationships is crucial for developing targeted disaster management strategies and policies to mitigate the adverse effects of climate change on Mongolian communities. This research provides valuable insights into how climatic changes impact disaster occurrences, offering a foundation for informed decision-making and policy development to enhance community resilience.

• Journal of Biosystems Engineering
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• 제25권2호
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• pp.123-130
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• 2000

A forcasting scheme for daily solar irradiance on agricultural field sis proposed by application of chaos theory to a long term observation data. It was conducted by reconstruction of phase space, attractor analysis, and Lyapunov analysis. Using the methodology , it was determined whether evolution of the five climatic data such as daily air temperature , water temperature , relative humidity, solar radiation, and wind speed are chaotic or not. The climatic data were collected for three years by an automated weather station at Hwasung-gun, Kyonggi-province. The results showed that the evolution of solar radiation was chaotic , and could be predicted. The prediction of the evolution of the solar radiation data was executed by using ' local optimal linear reconstruction ' algorithm . The RMS value of the predicting for the solar radiation evolution was 4.32 MJ/$m^2$ day. Therefore, it was feasible to predict the daily solar radiation based on the chaos theory.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2010년도 학술발표회
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• pp.1596-1599
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• 2010

Group method of data handling neural networks model (GMDH-NNM) is used to estimate daily pan evaporation (PE) using limited climatic variables such as max temperature ($T_{max}$), min temperature ($T_{min}$), mean wind speed ($W_{mean}$), mean relative humidity ($RH_{mean}$) and sunshine duration (SD). And, for the performances of GMDH-NNM, it is composed of training and test performances, respectively. The training and test performances are carried out using daily time series data, respectively. From this research, we evaluate the impact of GMDH-NNM for the modeling of the nonlinear time series data. We should, thus, construct the credible data of the daily PE data using GMDH-NNM, and can suggest the methodology for the irrigation and drainage networks system. Furthermore, this research represents that the strong nonlinear relationship such as pan evaporation modeling can be generalized using GMDH-NNM.

• 한국초지조사료학회지
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• 제36권3호
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• pp.223-236
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• 2016

The objective of this study was to construct Italian ryegrass (IRG) dry matter yield (DMY) estimation models in South Korea based on climatic data by locations. Obviously, the climatic environment of Jeju Island has great differences with Korean Peninsula. Meanwhile, many data points were from Jeju Island in the prepared data set. Statistically significant differences in both DMY values and climatic variables were observed between south areas of Korean Peninsula and Jeju Island. Therefore, the estimation models were constructed separately for south areas of Korean Peninsula and Jeju Island separately. For south areas of Korean Peninsula, a data set with a sample size of 933 during 26 years was used. Four optimal climatic variables were selected through a stepwise approach of multiple regression analysis with DMY as the response variable. Subsequently, via general linear model, the final model including the selected four climatic variables and cultivated locations as dummy variables was constructed. The model could explain 37.7% of the variations in DMY of IRG in south areas of Korean Peninsula. For Jeju Island, a data set containing 130 data points during 17 years were used in the modeling construction via the stepwise approach of multiple regression analysis. The model constructed in this research could explain 51.0% of the variations in DMY of IRG. For the two models, homoscedasticity and the assumption that the mean of the residuals were equal to zero were satisfied. Meanwhile, the fitness of both models was good based on most scatters of predicted DMY values fell within the 95% confidence interval.

• 조명전기설비학회논문지
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• 제18권1호
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• pp.73-77
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• 2004

전력 시스템의 오손에 의한 사고 방지를 위한 가장 효과적인 방법은 오손도를 정확하게 예측하는 것이다. 전력시스템은 옥외에 노출되어 있으므로 오손 및 열화가 불가피하며, 오손의 증가는 사고의 위험성을 악화시킨다. 한편, 오손의 주요소는 염분이며, 오손도는 등가 염분 부착 밀도(ESDD)로서 나타낼 수 있다. 기후 조건은 지속적으로 오손도를 증감시키고 있다. 기후와 오손도의 상관관계를 해석하여 오손도를 예측할 수 있으며, 다중 회귀 분석방법를 통하여 분석이 가능하다. 이와 관련된 선행연구에서는 높은 신뢰도를 확인할 수 있었다(0.874). 그러나 이러한 방법은 다른 시기에 적용한 경우 상관성이 상당히 하강하였다. 본 연구는 이와 같은 신뢰도를 더욱 향상 시키고(0.898), 정밀한 오손도 예측을 위한 통계처리를 수행하였다.

• 한국지역지리학회지
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• 제3권1호
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• pp.1-12
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• 1997

본 연구는 전국의 64개 관측지점을 대상으로 $1972{\sim}1995$년의 자료를 해마다 $K{\ddot{o}}ppen$ 구분방법에 적용시켜 기후 특성을 고찰하였다. 우리나라의 기후는 Cfa Cwa Cwb Dfa Dwa Dwb의 연후형 으로 구성되고, Cwa Dwa형의 출현빈도가 전체의 95%를 점한다. Cwa형의 출현은 대부분의 남부 지방 동사면 제주도에서 우세하고, Dwa형은 영서 경기 복동부에서, Cfa형은 울릉도에서 탁월하게 나타난다. 이런 우세지역은 안정된 기후지역을 형성하지만 다양한 기후형이 출현하는 남부 진방의 북부와 중부 지방의 남부는 C와 D형이 상접하는 경계이므로 불안정 기후지역을 이룬다. 또한 Cwa형이 가장 우세했던 1990년대 전반기에는 C D형의 경계선이 중부 내륙 지방에 위치하였고, Dwa형이 가장 탁월했던 1980년대 전반기에는 C D형의 경계선이 남부 지방의 중위에 위치하기도 하여 해에 따라서 연후지역의 범위가 변한다. 주요 연후형의 경년변화에서 Cwa형은 증가경향을 보이지만 Dwa형은 감소추세를 나타낸다. Cwa 연후지역의 확장추세는 1970년대 전반기와 1980년대 후반기 이후 최근까지 남부 지방을 중심으로 계속되고, 1980년대 전반기에 중부지방에서 우세했던 Dwa 연후지역은 최근까지 축소되고 있다.

• 설비공학논문집
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• 제27권11호
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• pp.574-580
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• 2015

Climatic zone in building code is an administrative district classification reflecting regional climatic characteristics. Use of Degree-Days is a fundamental method that can be used in various building design codes, analysis of building energy performance, and establishment of minimum thermal transmittance of building envelopes. Many foreign countries, such as the USA, the EU, Australia, Italy, India, China, etc., have already adapted climatic zone classification with degree-days, precipitation or amount of water vapor based on the characteristics of their own country's climate. In Korea, however, the minimum requirements for regional thermal transmittance are classified separately for the Jungbu area, Nambu area and Jeju Island with no definite criterion. In this study, degree-days of 255 Korean cities were used for climatic zone classification. Outdoor dry-bulb temperature data from the Korea Meteorological Administration for 1981~2010 was used to calculate degree-days. ArcGIS and the calculated degree-days were utilized to analyze and visualize climatic zone classification. As a result, depending on the distribution and distinctive differences in degree-days, four climatic zones were derived : 1) Central area, 2) Mountain area of Gyeonggi and Gangwon provinces, 3) Southern area, and 4) Jeju Island. The climatic zones were suggested per administrative district for easy public understanding and utilization.

• 한국수자원학회논문집
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• 제42권9호
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• pp.681-690
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• 2009

본 연구에서는 기상인자를 반영하여 확률강수량을 산정하고 불확실성을 평가하였다. 기상인자는 범지구적으로 관측되고 있는 해수면온도와 습윤지수 자료를 이용하였다. 분석 방법은 기상인자와 연최대시간강수량 사이의 지체상관계수를 산정하여 비교함으로써, 우리나라의 시간최대강수량과 상관관계가 큰 기상인자의 관측지역과 지체시간을 선정하고 지역가중다항식을 이용하여 회귀관계를 설정하였다. 다음으로 기상인자를 변동핵밀도함수를 이용하여 확률 밀도함수를 추정하여 모의발생을 수행하였다. 마지막으로 모의된 기상인자를 지역가중다항식을 통해 강수량을 추정하여 확률강수량을 산정하였다. 분석 결과에서 기상인자를 반영한 확률강수량은 강수자료를 빈도해석한 확률강수량과 큰 차이를 보이지 않는 것으로 나타났다. 또한 지구온난화와 같은 기후변화를 반영하는 기상인자를 반영한 확률강수량 산정의 기초자료로 활용할 수 있을 것으로 판단된다.

• 대한조선학회논문집
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• 제51권3호
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• pp.193-202
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• 2014

This paper presents a new method of sampling the climatic data for infrared signature analysis. Historical hourly data from a stationary marine buoy of KMA(Korean Meteorological Administration) are used to select a small number of sample points (N=100) to adequately cover the range of statistics(PDF, CDF) displayed by the original data set (S=56,670). The method uses a coarse bin to subdivide the variable space ($3^5$=243 bins) to make sample points cover the original data range, and a single-point ranking system to select individual points so that uniform coverage (1/N = 0.01) is obtained for each variable. The principal component analysis is used to calculate a joint probability of the coupled climatic variables. The selected sample data show good agreement to the original data set in statistical distribution and they will be used for statistical analysis of infrared signature and susceptibility of naval ships.

• Journal of Forest and Environmental Science
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• 제39권2호
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• pp.105-117
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• 2023

Climate change and invasive alien plant species (IAPs) are having a significant impact on mountain ecosystems. The combination of climate change and socio-economic development is exacerbating the invasion of IAPs, which are a major threat to biodiversity loss and ecosystem functioning. Species distribution modelling has become an important tool in predicting the invasion or suitability probability under climate change based on occurrence data and environmental variables. MaxEnt modelling was applied to predict the current suitable distribution of most noxious weed A. adenophora (Spreng) R. King and H. Robinson and analysed the changes in distribution with the use of current (year 2000) environmental variables and future (year 2050) climatic scenarios consisting of 3 representative concentration pathways (RCP 2.6, RCP 4.5 and RCP 8.5) in Bhutan. Species occurrence data was collected from the region of interest along the road side using GPS handset. The model performance of both current and future climatic scenario was moderate in performance with mean temperature of wettest quarter being the most important variable that contributed in model fit. The study shows that current climatic condition favours the A. adenophora for its invasion and RCP 2.6 climatic scenario would promote aggression of invasion as compared to RCP 4.5 and RCP 8.5 climatic scenarios. This can lead to characterization of the species as preferring moderate change in climatic conditions to be invasive, while extreme conditions can inhibit its invasiveness. This study can serve as reference point for the conservation and management strategies in control of this species and further research.