• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.3
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• pp.189-204
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• 2018

This study has developed a forest fire occurrence probability model for inaccessible areas such as North Korea and Demilitarized Zone and we have developed a real-time forest fire danger rating system that can be used in fire-related works. There are limitations on the research that it is impossible to conduct site investigation for data acquisition and verification for forest fire weather index model and system development. To solve this problem, we estimated the fire spots in the areas where access is impossible by using MODIS satellite data with scientific basis. Using the past meteorological reanalysis data(5㎞ resolution) produced by the Korea Meteorological Administration(KMA) on the extracted fires, the meteorological characteristics of the fires were extracted and made database. The meteorological factors extracted from the forest fire ignition points in the inaccessible areas are statistically correlated with the forest fire occurrence and the weather factors and the logistic regression model that can estimate the forest fires occurrence(fires 1 and non-fores 0). And used to calculate the forest fire weather index(FWI). The results of the statistical analysis show that the logistic models(p<0.01) strongly depends on maximum temperature, minimum relative humidity, effective humidity and average wind speed. The logistic regression model constructed in this study showed a relatively high accuracy of 66%. These findings may be beneficial to the policy makers in Republic of Korea(ROK) and Democratic People's Republic of Korea(DPRK) for the prevention of forest fires.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.1
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• pp.1-10
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• 2012

Digital Forecast of the Korea Meteorological Administration (KMA) represents 5 km gridded weather forecast over the Korean Peninsula and the surrounding oceanic regions in Korean territory. Digital Forecast provides 12 weather forecast elements such as three-hour interval temperature, sky condition, wind direction, wind speed, relative humidity, wave height, probability of precipitation, 12 hour accumulated rain and snow, as well as daily minimum and maximum temperatures. These forecast elements are updated every three-hour for the next 48 hours regularly. The objective of this study was to construct Forest Fire Danger Rating Systems on the Korean Peninsula (FFDRS_KORP) based on the daily weather index (DWI) and to improve the accuracy using the digital forecast data. We produced the thematic maps of temperature, humidity, and wind speed over the Korean Peninsula to analyze DWI. To calculate DWI of the Korean Peninsula it was applied forest fire occurrence probability model by logistic regression analysis, i.e. $[1+{\exp}\{-(2.494+(0.004{\times}T_{max})-(0.008{\times}EF))\}]^{-1}$. The result of verification test among the real-time observatory data, digital forecast and RDAPS data showed that predicting values of the digital forecast advanced more than those of RDAPS data. The results of the comparison with the average forest fire danger rating index (sampled at 233 administrative districts) and those with the digital weather showed higher relative accuracy than those with the RDAPS data. The coefficient of determination of forest fire danger rating was shown as $R^2$=0.854. There was a difference of 0.5 between the national mean fire danger rating index (70) with the application of the real-time observatory data and that with the digital forecast (70.5).

• 전기의세계
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• v.31 no.5
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• pp.368-374
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• 1982

This paper describes a combined algorithm for short-term-load forecating. One of the specific features of this algorithm is that the base, weather sensitive and residual components are predicted respectively. The base load is represented by the exponential smoothing approach and residual load is represented by the Box-Jenkins methodology. The weather sensitive load models are developed according to the information of temperature and discomfort index. This method was applied to Korea Electric Company and results for test periods up to three years are given.

• Bulletin of the Korean Space Science Society
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• 2008.10a
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• pp.24.2-24.2
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• 2008

We have examined the relative contributions of representative space weather proxies (geomagnetic aa index) to global warming (Global temperature anomaly) and compared them with that of green house effect characterized CO2 content from 1868 to 2005. For this we used Hadcrut3 temperature anomaly (Ta) data, aa index taken at two anti-podal subauroral stations (Canberra Australia and hartland England), and the CO2 data come from historical ice core records. From the comparison between Ta and aa index, we found several interesting results: (1) the linear correlation coefficient between two parameters increases until 1990 and then decreases rapidly, and (2) the scattered plots between two parameters shows different patterns before and after 1990. A partial correlation of Ta and two quantities (aa, CO2) also shows that the geomagnetic effect (aa index) is dominant until about 1990 and the CO2 effect becomes much more important after then. These results imply that the green house effect become very important since at least 1990. For a further analysis, we simply assume that Ta (total) = Ta (aa) + Ta (CO2) and made a linear regression between Ta and aa index from 1868 to 1990. A linear model is then made from the linear regression between energy consumption (a proxy of CO2 effect) and Ta (total) - Ta (aa) since 1990. This linear model makes it possible to predict the temperature anomaly in 2030, about 1 degree higher than the present temperature, which is much larger than in the previous century.

• Journal of Astronomy and Space Sciences
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• v.36 no.3
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• pp.149-157
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• 2019

In this study, we investigate the associations between the solar variability and teleconnection indices, which influence atmospheric circulation and subsequently, the spatial distribution of the global pressure system. A study of the link between the Sun and a large-scale mode of climate variability, which may indirectly affect the Earth's climate and weather, is crucial because the feedbacks of solar variability to an autogenic or internal process should be considered with due care. We have calculated the normalized cross-correlations of the total sunspot area, the total sunspot number, and the solar North-South asymmetry with teleconnection indices. We have found that the Southern Oscillation Index (SOI) index is anti-correlated with both solar activity and the solar North-South asymmetry, with a ~3-year lag. This finding not only agrees with the fact that El $Ni{\tilde{n}}o$ episodes are likely to occur around the solar maximum, but also explains why tropical cyclones occurring in the solar maximum periods and in El $Ni{\tilde{n}}o$ periods appear similar. Conversely, other teleconnection indices, such as the Arctic Oscillation (AO) index, the Antarctic Oscillation (AAO) index, and the Pacific-North American (PNA) index, are weakly or only slightly correlated with solar activity, which emphasizes that response of terrestrial climate and weather to solar variability are local in space. It is also found that correlations between teleconnection indices and solar activity are as good as correlations resulting from the teleconnection indices themselves.

• Journal of applied mathematics & informatics
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• v.9 no.2
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• pp.771-785
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• 2002

Many industries like energy, utilities, ice cream and leisure sports are closely related to the weather. In order to hedge weather related risks, they invest their assets with portfolios like option, coupons, future, and other weather derivatives. Among weather related derivatives, CDD and HDD index options are mainly transacted between companies. In this paper, the autocorrelation system of temperature will be checked for several cities in Korea and the parameter estimation will be carried based on the maximum likelihood estimation. Since the log likelihood increase as the number of parameters increases, we adopt the Schwarz information criterion .

• Journal of Korea Water Resources Association
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• v.44 no.11
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• pp.889-902
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• 2011

In this study, frequency analysis using drought index had implemented for the derivation of drought severity-duration-frequency (SDF) curves to enable quantitative evaluations of past historical droughts having been occurred in Korean Peninsular. Seoul, Daejeon, Daegu, Gwangju, and Busan weather stations were selected and precipitation data during 1974~2010 (37 years) was used for the calculation of Standardized Precipitation Index (SPI) and frequency analysis. Based on the results of goodness of fit test on the probability distribution, Generalized Extreme Value (GEV) was selected as most suitable probability distribution for the drought frequency analysis using SPI. This study can suggest return periods for historical major drought events by using newrly derived SDF curves for each stations. In case of 1994~1995 droughts which had focused on southern part of Korea. SDF curves of Gwangju weather station showed 50~100 years of return period and Busan station showed 100~200 years of return period. Besides, in case of 1988~1989 droughts, SDF of Seoul weather station were appeared as having return periods of 300 years.

• Journal of Korean Society of Transportation
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• v.28 no.1
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• pp.157-163
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• 2010

It is well known that weather conditions are closely related with the number and severity of traffic accidents. At present, installation of safety countermeasures including systems is common approach to reduce the damage of traffic accidents at expressways. In this study, the differences of causation factors to influence traffic accidents considering road alignment characteristics and weather conditions. In order to identify the relationship between road and weather conditions, discriminant analysis has been performed with 500 traffic accident data at expressways. Weather conditions are divided into several categories such as snow, sunny, rain, fog, and cloud. Also, road conditions such as types of pavements, grades are analyzed. As the results, major impacting road conditions to traffic accidents are concrete pavement and 3% or more down grades. In these road conditions, visible distance will be reduced and actual braking distances will be increased. This study shows that the expressway sections under concrete pavement and down grades should be more cautious than other sections. It also shows that fog condition is the mose dangerous situation in terms of traffic accidents.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.136-136
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• 2021

In this study, a deep convolutional neural network (DCNN) model is proposed for short-term precipitation forecasting using weather radar-based images. The DCNN model is a combination of convolutional neural networks, autoencoder neural networks, and U-net architecture. The weather radar-based image data used here are retrieved from competition for rainfall forecasting in Korea (AI Contest for Rainfall Prediction of Hydroelectric Dam Using Public Data), organized by Dacon under the sponsorship of the Korean Water Resources Association in October 2020. This data is collected from rainy events during the rainy season (April - October) from 2010 to 2017. These images have undergone a preprocessing step to convert from weather radar data to grayscale image data before they are exploited for the competition. Accordingly, each of these gray images covers a spatial dimension of 120×120 pixels and has a corresponding temporal resolution of 10 minutes. Here, each pixel corresponds to a grid of size 4km×4km. The DCNN model is designed in this study to provide 10-minute predictive images in advance. Then, precipitation information can be obtained from these forecast images through empirical conversion formulas. Model performance is assessed by comparing the Score index, which is defined based on the ratio of MAE (mean absolute error) to CSI (critical success index) values. The competition results have demonstrated the impressive performance of the DCNN model, where the Score value is 0.530 compared to the best value from the competition of 0.500, ranking 16^th out of 463 participating teams. This study's findings exhibit the potential of applying the DCNN model to short-term rainfall prediction using weather radar-based images. As a result, this model can be applied to other areas with different spatiotemporal resolutions.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.3
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• pp.1-14
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• 2019

North Korea has frequently suffered from extreme agricultural crop droughts, which have led to food shortages, according to the Food and Agriculture Organization (FAO). The increasing frequency of extreme droughts, due to global warming and climate change, has increased the importance of enhancing the national capacity for drought management. Historically, a meteorological drought index based on data collected from weather stations has been widely used. But it has limitations in terms of the distribution of weather stations and the spatial pattern of drought impacts. Satellite-based data can be obtained with the same accuracy and at regular intervals, and is useful for long-term change analysis and environmental monitoring and wide area access in time and space. The Evaporative Stress Index (ESI), a satellite-based drought index using the ratio of potential and actual evaporation, is being used to detect drought response as a index of the droughts occurring rapidly over short periods of time. It is more accurate and provides faster analysis of drought conditions compared to the Standardized Precipitation Index (SPI), and the Palmer Drought Severity Index (PDSI). In this study, we analyze drought events during 2015-2017 in North Korea using the ESI satellite-based drought index to determine drought response by comparing with it with the SPI and SPEI drought indices.