• Atmosphere
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• v.28 no.4
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• pp.403-414
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• 2018

Predictability of Northern Hemisphere blocking in the Korea Meteorological Administration (KMA) Global Data Assimilation and Prediction System (GDAPS) is evaluated for the period of July 2016 to May 2017. Using the operational model output, blocking is defined by a meridional gradient reversal of 500-hPa geopotential height as Tibaldi-Molteni Index. Its predictability is quantified by computing the critical success index and bias score against ERA-Interim data. It turns out that Northwest Pacific blockings, among others, are reasonably well predicted with a forecast lead time of 2~3 days. The highest prediction skill is found in spring with 3.5 lead days, whereas the lowest prediction skill is observed in autumn with 2.25 lead days. Although further analyses are needed with longer dataset, this result suggests that Northern Hemisphere blocking is not well predicted in the operational weather prediction model beyond a short-term weather prediction limit. In the spring, summer, and autumn periods, there was a tendency to overestimate the Western North Pacific blocking.

• Journal of Climate Change Research
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• v.5 no.4
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• pp.367-374
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• 2014

The monthly mean air temperature datasets of 61 stations in South Korea from 1973 to 2012 were collected to calculate trends in the warmth index (WI) and to analyze the potential enlargement of safety cultivation limit for sweet persimmon. The WI averaged over the last 40 years was 104.1 (℃·Month) at 61 stations, with the highest at Seogwipo station (WI=137.9) and the lowest at Daegwallyeong station (WI=60.9). It has increased by 1.8 (℃·Month) per 10 years over the last 40 years, with the highest in the year 1994 (WI=112.0) and the lowest in the year 1976 (WI=94.7). When the possible stations for sweet persimmon cultivation were classified by the basis on WI≥100, 38 out of the 61 weather stations were included in the safety cultivation zone for sweet persimmon for the last 40 years. On the other hand, the number of weather stations within the safety cultivation zones for sweet persimmon for the last 10 years (from 2003 to 2012) were 47 by adding additional 9 stations (Socho, Wonju, Chungju, Seosan, Uljin, Yangpyeong, Icheon, Cheonan, and Geochang stations). A further study of the climate conditions and soil characteristics is required for a better assessment of the safety cultivation zones for sweet persimmon.

• International Journal of Contents
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• v.7 no.1
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• pp.45-51
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• 2011

To protect water pollution and scarcity in lake and river, water quality monitoring applications have become important tools to understand the change of aquatic ecosystem. KLEON (Korean Lake Ecological Observatory Network) is designed to manage and share the ecological observations. The various kinds of water quality and phytoplankton observations are collected from the selected observatories such as seven lakes/rivers/wetlands. To deeply understand the collected observations with weather, KLEON also manages the observatory information such as lake, dam, floodgate, and weather. The accumulated observation and analyzed results are used to improve the water quality index of the observatories and encourage the ecologists' cooperation.

• Journal of computational fluids engineering
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• v.10 no.1
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• pp.87-93
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• 2005

Macao, a city with three sides bounded by water, is hot and humid in weather in more than six months of a year. This uncomfortable weather induces the frequency of operating air-conditioners. Choice of location for installation of air-conditioner in a building will affect the performance of cooling effect and thermal comfort on the occupants, which in turn will affect the indoor air quality (IAQ) of the building. In the paper, investigation of distribution on carbon dioxide, room air temperature and velocity, as well as air diffusion performance index (ADPI) of a single bedroom in Macao is studied by using the computational fluid dynamics (CFD) software FLOVENT 3.2. Simulations of locating the air-conditioner at 4 different walls will be done and comparisons and analyses of the results will be performed to decide a proper location for the air-conditioner for obtaining good thermal comfort.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.116-130
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• 2019

In South Korea, forest fire occurrences are increasing in size and duration due to various factors such as the increase in fuel materials and frequent drying conditions in forests. Therefore, it is necessary to minimize the damage caused by forest fires by appropriately providing the probability of forest fire risk. The purpose of this study is to improve the Daily Weather Index(DWI) provided by the current forest fire forecasting system in South Korea. A new Fire Risk Index(FRI) is proposed in this study, which is provided in a 5km grid through the synergistic use of numerical weather forecast data, satellite-based drought indices, and forest fire-prone areas. The FRI is calculated based on the product of the Fine Fuel Moisture Code(FFMC) optimized for Korea, an integrated drought index, and spatio-temporal weighting approaches. In order to improve the temporal accuracy of forest fire risk, monthly weights were applied based on the forest fire occurrences by month. Similarly, spatial weights were applied using the forest fire density information to improve the spatial accuracy of forest fire risk. In the time series analysis of the number of monthly forest fires and the FRI, the relationship between the two were well simulated. In addition, it was possible to provide more spatially detailed information on forest fire risk when using FRI in the 5km grid than DWI based on administrative units. The research findings from this study can help make appropriate decisions before and after forest fire occurrences.

• Journal of Korean Society of Forest Science
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• v.80 no.3
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• pp.257-264
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• 1991

Korea has accomplished the afforestation of its forest land in the early 1980's. To meet the increasing demand for forest products and forest recreation, a development of scientific forest management system is needed as a whole. For this purpose the development of efficient forestfire management system is essential. In this context, the purpose of this study is to develop a theoretical foundation of forestfire danger rating system. In this study, it is hypothesized that the degree of forestfire risk is affected by Weather Factor and Man-Caused Risk Factor. (1) To accommodate the Weather Factor, a statistical model was estimated in which weather variables such as humidity, temperature, precipitation, wind velocity, duration of sunshine were included as independent variables and the probability of forestfire occurrence as dependent variable. (2) To account man-caused risk, historical data of forestfire occurrence was investigated. The contribution of man's activities make to risk was evaluated from three inputs. The first, potential risk class is a semipermanent number which ranks the man-caused fire potential of the individual protection unit relative to that of the other protection units. The second, the risk sources ratio, is that portion of the potential man-caused fire problem which can be charged to a specific cause. The third, daily activity level is that the fire control officer's estimate of how active each of these sources is, For each risk sources, evaluate its daily activity level ; the resulting number is the partial risk factor. Sum up the partial risk factors, one for each source, to get the unnormalized Man-Caused Risk. To make up the Man-Caused Risk, the partial risk factor and the unit's potential risk class were considered together. (3) At last, Fire occurrence index was formed fire danger rating estimation by the Weather Factors and the Man-Caused Risk Index were integrated to form the final Fire Occurrence Index.

• Journal of Preventive Medicine and Public Health
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• v.38 no.3
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• pp.283-290
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• 2005

Objectives : The aim of this paper was to examine the relationship between the summertime (June to August) heat index, which quantifies the bioclimatic apparent temperature in sultry weather, and the daily disease-related mortality in Seoul for the period from 1991 to 2000. Methods : The daily maximum (or minimum) summertime heat indices, which show synergetic apparent temperatures, were calculated from the six hourly temperatures and real time humidity data for Seoul from 1991 to 2000. The disease-related daily mortality was extracted with respect to types of disease, age and sex, etc. and compared with the time series of the daily heat indices. Results : The summertime mortality in 1994 exceeded the normal by 626 persons. Specifically, blood circulation-related and cancer-related mortalities increased in 1994 by 29.7% (224 persons) and 15.4% (107 persons), respectively, compared with those in 1993. Elderly persons, those above 65 years, were shown to be highly susceptible to strong heat waves, whereas the other age and sex-based groups showed no significant difference in mortality. In particular, a heat wave episode on the 22nd of July 2004 ($>45^{\circ}C$ daily heat index) resulted in double the normal number of mortalities after a lag time of 3 days. Specifically, blood circulation-related mortalities, such as cerebral infraction, were predominant causes. Overall, a critical mortality threshold was reached when the heat index exceeded approximately $37^{\circ}C$, which corresponds to human body temperature. A linear regression model based on the heat indices above $37^{\circ}C$, with a 3 day lag time, accounted for 63% of the abnormally increased mortality (${\geq}+2$ standard deviations). Conclusions : This study revealed that elderly persons, those over 65 years old, are more vulnerable to mortality due to abnormal heat waves in Seoul, Korea. When the daily maximum heat index exceeds approximately $37^{\circ}C$, blood circulation-related mortality significantly increases. A linear regression model, with respect to lag-time, showed that the heat index based on a human model is a more dependable indicator for the prediction of hot weather-related mortality than the ambient air temperature.

• Journal of Astronomy and Space Sciences
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• v.26 no.4
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• pp.521-528
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• 2009

While it is well known that space environment can produce spacecraft anomaly, defining space environment effects for each anomalies is difficult. This is caused by the fact that spacecraft anomaly shows various symptoms and reproducing it is impossible. In this study, we try to find the conditions of when spacecraft failures happen more frequently and give satellite operators useful information. Especially, our study focuses on the geosynchronous satellites which cost is high and required high reliability. We used satellite anomaly data given by Satellite News Digest which is internet newspaper providing space industry news. In our analysis, 88 anomaly cases occurred from 1997 to 2008 shows bad corelation with Kp index. Satellite malfunctions were likely to happen in spring and fall and in local time from midnight to dawn. In addition, we found the probability of anomaly increase when high energy electron flux is high. This is more clearly appeared in solar minimum than maximum period.

• Journal of Advanced Navigation Technology
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• v.21 no.6
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• pp.643-650
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• 2017

Photovoltaic generation which has unlimited energy sources are very intermittent because they depend on the weather. Therefore, it is necessary to get accurate generation prediction with reducing the uncertainty of photovoltaic generation and improvement of the economics. The Meteorological Agency predicts weather factors for three days, but doesn't predict the sunshine and solar radiation that are most correlated with the prediction of photovoltaic generation. In this study, we predict sunshine and solar radiation using weather, precipitation, wind direction, wind speed, humidity, and cloudiness which is forecasted for three days at Meteorological Agency. The photovoltaic generation forecasting model is proposed by using predicted solar radiation and sunshine. As a result, the proposed model showed better results in the error rate indexes such as MAE, RMSE, and MAPE than the model that predicts photovoltaic generation without radiation and sunshine. In addition, DNN showed a lower error rate index than using SVM, which is a type of machine learning.

• Convergence Security Journal
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• v.22 no.2
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• pp.99-104
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• 2022

This study provided a prediction model for solar energy production in Yeongam province, Jeollanam-do. The model was derived from the correlation between climate changes and solar power production in Yeongam province, Jeollanam-do, and presented a prediction of solar power generation through the regression analysis of 6 parameters related to weather and solar power generation. The data used in this study were the weather and photovoltaic production data from January in 2016 to December in 2019 provided by public data. Based on the data, the machine learning technique was used to analyzed the correlation between weather change and solar energy production and derived to the prediction model. The model showed that the photovoltaic production can be categorized by the three-stage production index and will be used as an important barometer in the agriculture activity and the use of photovoltaic electricity.