• Korean Journal of Remote Sensing
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• v.6 no.1
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• pp.11-24
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• 1990

This is the study about the meteorological satellite cloud image classification by objective methods. For objective cloud classification, linear discriminant analysis was tried. In the linear discriminant analysis 27 cloud characteristic parameters were retrieved from GMS infrared image data. And, linear cloud classification model was developed from major parameters and cloud type coefficients. The model was applied to GMS IR image for weather forecasting operation and cloud image was classified into 5 types such as Sc, Cu, CiT, CiM and Cb. The classification results were reasonably compared with real image.

• The Korean Journal of Quaternary Research
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• v.7 no.1
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• pp.27-39
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• 1993

The shingle beach as a typical pocket beach located in Jeongdo-ri, Wando, Cheolanam-do, Korea has been investigated in terms of textural characteristics, mainly gravel shape and roundness. In the Jeongdo-ri gravel beach, changes of beach profile after storm weather and textural parameters of gravels were observed and measured from May 1992 to March 1993. Beach profile is divided into two different Fair-weather zone and Storm-weather zone influenced by dynamic condition of wave energy. The former is affected by wave and tide under fair-weather condition, the latter seems to be formed under storm-weather condition. Each zone comprises a series of beach faces and berms formed by continuous sedimentary processes of swash, overwash and backwash. Storm-weather zone is subdivided into three groups having a pair of beach face and berm respectively. Mean sizes of berm gravel(45.5 mm -123.6 mm) are coarser than gravels of beach face (36.8 mm - 78.3 mm) in fair-weather zone. On the other hand, in storm-weather zone, gravels of berms (33.1 mm -82.5 mm) are finer than those of beachfaces (46.2 mm - 105.2 mm). The proportion of disc shaped gravels of berm (50.0% - 58.5 %) is higher than that of beachface (45.9 % - 51.3 %) in each subzone except C-group of storm-weather zone. And the proportion of the equant shaped gravel increases about up to 10% seaward. Therefore, shore-normal distribution of gravels seems to be affected by shape and size sorting effects. Shore-parallel distribution pattern of gravel shape is more distinctive than size distribution patterns. That is, disc and blade shaped particles decrease up to 20% and 13% respectively, and equants increase up to 34% to the westward. Gravels plotted on Sneed and Folk's triangular diagram are more compacted and elongated with decreasing size. Therefore primary gravels are shaped by characteristics of country rock e.g. cleavage, joint etc., and secondary are affected by sorting and size-controlled process evolution by wave action.

• Journal of the Optical Society of Korea
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• v.14 no.3
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• pp.221-227
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• 2010

Aerosol size distribution provides good information for predicting weather changes and understanding cloud formation. Aerosol extinction coefficient and backscattering coefficient are measured by many scientists, but these parameters depend not only on aerosol size but on aerosol concentrations. An algorithm has been developed to measure aerosol parameters such as ${\AA}$ngstr$\ddot{o}$m exponent, color ratio, and LIDAR ratio without any assumptions by using two wavelength rotational Raman LIDAR signals. These parameters are good indicators for the aerosol size. And we can find ${\AA}$ngstr$\ddot{o}$m exponent, color ratio, and LIDAR ratio under various weather conditions. Finally, it can be seen that the ${\AA}$ngstr$\ddot{o}$m exponent has an inverse relationship to the particle size of the aerosol and the color ratio is linearly dependent on the aerosol size. An ${\AA}$ngstr$\ddot{o}$m exponent from 1.2 to 3.1, a color ratio from 0.28 to 1.04, and a LIDAR ratio 66.9 sr at 355 nm and 32.6 sr at 532 nm near the cloud were obtained.

• Journal of The Korean Society of Emergency Medicine
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• v.29 no.6
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• pp.603-615
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• 2018

Objective: We analyzed the association between regional weather and temporal changes on the daily occurrence of trauma emergencies and their severity. Methods: In this cross-sectional prospective study, we investigated daily atmospheric patterns in trauma episodes in 1,344 patients in Cheongju city, South Korea, from January 2016 to December 2016 and analyzed the association of trauma occurrence and Injury Severity Scores (ISS) with weather conditions on a daily scale. Results: The mean age of trauma patients was $53.0{\pm}23.8years$ and average ISS was $9.0{\pm}2.0$. Incidence of trauma was positively correlated with average temperature (r=0.512, P<0.001) and atmospheric pressure (r=0.332, P=0.010) and negatively correlated with air pollutants (particulate matter less than $2.5{\mu}m^3$ [PM2.5], r=-0.629, P<0.001; particulate matter less than $10{\mu}m^3$ [PM10], r=-0.679, P<0.001). ISS was not significantly correlated with climate parameters and air pollutants, and variability was observed in the frequency and severity of trauma by time of day (highest occurrence, 16-20 pm; highest ISS, 4-8 am), day of the week (highest occurrence and highest ISS, Saturday), month of the year (highest occurrence, July; highest ISS, November), and season (highest incidence, summer; highest ISS, autumn). Conclusion: The study shows a positive relationship between trauma occurrence and specific weather conditions, such as atmospheric temperature and pressure. There was a negative relationship between concentrations of PM2.5 or PM10, and trauma occurrence. However, no correlation was observed between weather conditions or the concentrations of air pollutants and ISS. In addition, seasonal, circaseptan, and circadian variations exist in trauma occurrence and severity. Thus, we suggest that evaluation of a larger, population-based data set is needed to further investigate and confirm these relationships.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.64-71
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• 2017

Considering the volatility, intermittent and random of photovoltaic (PV) generation systems, accurate forecasting of PV power output is important for the grid scheduling and energy management. In order to improve the accuracy of short-term power forecasting of PV systems, this paper proposes a prediction model based on environmental factors and support vector machine optimized by genetic algorithm (GA-SVM). In order to improve the prediction accuracy of this model, weather conditions are divided into three types, and the gray correlation coefficient algorithm is used to find out a similar day of the predicted day. To avoid parameters optimization into local optima, this paper uses genetic algorithm to optimize SVM parameters. Example verification shows that the prediction accuracy in three types of weather will remain at between 10% -15% and the short-term PV power forecasting model proposed is effective and promising.

• 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.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.95.1-95.1
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• 2012

Halo coronal mass ejections (HCMEs) are major cause of geomagnetic storms and their three dimensional structures are important for space weather. In this study, we compare three cone models: an elliptical cone model, an ice-cream cone model, and an asymmetric cone model. These models allow us to determine the three dimensional parameters of HCMEs such as radial speed, angular width, and the angle (${\gamma}$) between sky plane and cone axis. We compare these parameters obtained from three models using 62 well-observed HCMEs from 2001 to 2002. Then we obtain the root mean square error (RMS error) between maximum measured projection speeds and their calculated projection speeds from the cone models. As a result, we find that the radial speeds obtained from the models are well correlated with one another (R > 0.84). The correlation coefficients between angular widths are less than 0.53 and those between ${\gamma}$ values are less than 0.47, which are much smaller than expected. The reason may be due to different assumptions and methods. The RMS errors of the elliptical cone model, the ice-cream cone model, and the asymmetric cone model are 213 km/s, 254 km/s, and 267 km/s, respectively. Finally, we discuss their strengths and weaknesses in terms of space weather application.

• International Journal of Industrial Entomology and Biomaterials
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• v.22 no.1
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• pp.11-15
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• 2011

Impact of pruning date, shoot age and weather parameters on the severity and development of grey leaf spot (Pseudocercospora mori) of mulberry was studied. The disease severity (%) increased with increase in shoot age irrespective of pruning date. Maximum disease severity was observed in plants pruned during second week of October and minimum in plants pruned during last week of December. Significant (P<0.05) influence of date of pruning, shoot age and their interaction was observed on the severity of the disease. Apparent infection rate (r) was significantly higher during plant growth period from day-48 to day-55. Average apparent rate was higher in plants pruned during first week of September and least in plants pruned during third and fourth week of December. Multiple regression analysis revealed contribution of various combinations of weather parameters on the disease severity. A linear prediction model [$Y=66.05+(-1.39)x_1+(-0.219)x_4$] with significant $R^2$ was developed for prediction of the disease under natural epiphytotic condition.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.90.1-90.1
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• 2011

We are developing empirical space weather (geomagnetic storms, solar proton events, and solar flares) forecast models based on solar information. These models have been set up with the concept of probabilistic forecast using historical events. Major findings can be summarized as follows. First, we present a concept of storm probability map depending on CME parameters (speed and location). Second, we suggested a new geoeffective CME parameter, earthward direction parameter, directly observable from coronagraph observations, and demonstrated its importance in terms of the forecast of geomagnetic storms. Third, the importance of solar magnetic field orientation for storm occurrence was examined. Fourth, the relationship among coronal hole-CIR-storm relationship has been investigated, Fifth, the CIR forecast based on coronal hole information is possible but the storm forecast is challenging. Sixth, a new solar proton event (flux, strength, and rise time) forecast method depending on flare parameters (flare strength, duration, and longitude) as well as CME parameter (speed, angular width, and longitude) has been suggested. Seventh, we are examining the rates and probability of solar flares depending on sunspot McIntosh classification and its area change (as a proxy of flux change). Our results show that flux emergence greatly enhances the flare probability, about two times for flare productive sunspot regions.

• Wind and Structures
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• v.15 no.2
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• pp.87-109
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• 2012

A substantial part of South Africa is subject to more than one strong wind source. The effect of that on extreme winds is that higher quantiles are usually estimated with a mixed strong wind climate estimation method, compared to the traditional Gumbel approach based on a single population. The differences in the estimated quantiles between the two methods depend on the values of the Gumbel distribution parameters for the different strong wind mechanisms involved. Cluster analysis of the distribution parameters provides a characterization of the effect of the relative differences in their values, and therefore the dominance of the different strong wind mechanisms. For gusts, cold fronts tend to dominate over the coastal and high-lying areas, while other mechanisms, especially thunderstorms, are dominant over the lower-lying areas in the interior. For the hourly mean wind speeds cold fronts are dominant in the south-west, south and east of the country. On the West Coast the ridging of the Atlantic Ocean high-pressure system dominate in the south, while the presence of a deep trough or coastal low pressure system is the main strong wind mechanism in the north. In the central interior cold fronts tend to share their influence almost equally with other synoptic-scale mechanisms.