• Journal of Biosystems Engineering
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• v.25 no.2
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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.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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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.

• Journal of The Korean Society of Grassland and Forage Science
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• v.36 no.3
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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.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.1
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• pp.73-77
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• 2004

The accumulation of salt contamination could be the source of electrical accident. The degree of contamination on outdoor insulator is represented by ESDD(equivalent salt deposit density). The salt is transported by the wind to the outdoor insulator and cleaned by the rain. So, we can estimate the relation between climatic data and ESDD by using the multiple regression method. The results of pre-study presented the high relation coefficient(0.874). But these results couldn't apply to another period So, this paper improves above problem by mathematical process of climatic data The main method is accumulation of passed climatic data and conversion of the rainfall by the wind. Then, we confirmed the increasement of relation coefficient(0.898).

• Journal of the Korean association of regional geographers
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• v.3 no.1
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• pp.1-12
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• 1997

The climatic characteristics of Korea are analized with the data observed from 1972 to 1995 in 66 stations, using the climatic year method expressed by the $K{\ddot{o}}ppen's$ system of climatic classification. The climate of Korea is composed of the six climatic year types : Cfa, Cwa, Cwb, Dfa, Dwa and Dwb type. The Cwa and Dwa type occupy 95% occurrence frequency. The Cwa climatic year type predominates in the greater part of the Southern Area, the east slopes of the Taebaek Range and Cheju-do, the Dwa type does in Yongso Area and the northeastern part of Kyonggi Province. and the Cfa type does in Ullung-do. Such dominant climatic year regions become the stable climatic regions, while the regions where the various climatic types appear become the unstable climatic regions which are distributed in the northern part of the Southern Area and in the southern part of the Central Area owing to the shifts of the border between C type and D type. The border between C and D type is located in the Central Inland Area in the first half of the 1990's which the Cwa type predominates most. On the other hand the border is located in the middle part of the Southern Area in the first half of the 1980's which the Dwa type prevails most. Therefore the extents of the climatic year regions vary each year. In the interannual change of the main climatic year types, the Cwa type shows the increasing trend, while the Cfa and Dwa type reflets the decreasing tendency. The extending trend of the Cwa climatic year region appears during the period of the first half of the 1970's and the period between the latter half of the 1980's and the first half of the 1990's centering around the Southern Area. The Dwa climatic year region which was predominant in the Central Area in the first half of the 1980's has been reduced up till the recent years.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.27 no.11
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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.

• Journal of Korea Water Resources Association
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• v.42 no.9
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• pp.681-690
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• 2009

In this research, design precipitation was calculated by reflecting the climatic indices and its uncertainty assessment was evaluated. Climatic indices used the sea surface temperature and moisture index which observed globally. The correlation coefficients were calculated between the annual maximum precipitation and the climatic indices. and then climatic indices which have the larger correlation coefficient were selected. Therefore, the regression relationship was established by a locally weighted polynomial regression. Next, climatic indices were generated by montecarlo simulation using kernel function. Finally, the design rainfall was calculated by the locally weighted polynomial regression using generated climatic indices. At the result, the comparison of design rainfall between the reflection of the climatic indices and the frequency analysis did not indicate a significant difference. Also, this result can be used as basic data for calculation of probability precipitation to reflect climate change.

• Journal of the Society of Naval Architects of Korea
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• v.51 no.3
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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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• v.39 no.2
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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.

• Journal of the Korean Geographical Society
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• v.45 no.3
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• pp.319-341
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• 2010

The purposes of this study are to classify heavy snowfall types in the Republic of Korea based on fresh snowfall data and atmospheric circulation data during the last 36(1973/74-2008/09) snow seasons and to identify typical surface synoptic climate patterns that characterize each heavy snowfall type. Four synoptic climate categories and seventeen regional heavy snowfall types are classified based on sea level pressure/surface wind vector patterns in East Asia and frequent spatial clustering patterns of heavy snowfall in the Republic of Korea, respectively. Composite analyses of multiple surface synoptic weather charts demonstrate that the locations and intensity of pressure/wind vector mean and anomaly cores in East Asia differentiate each regional heavy snowfall type in Korea. These differences in synoptic climatic fields are primarily associated with the surge of the Siberian high pressure system and the appearance of low pressure systems over the Korean Peninsula. In terms of hemispheric atmospheric circulation, synoptic climatic patterns in the negative mode of winter Arctic Oscillation (AO) are also associated with frequent heavy snowfall in the Republic of Korea at seasonal scales. These results from long-term synoptic climatic data could contribute to improvement of short-range or seasonal prediction of regional heavy snowfall.