• Bulletin of the Korean Space Science Society
• /
• 2008.10a
• /
• pp.33.1-33.1
• /
• 2008

As a part of the construction of Korean Space Weather Prediction Center (K-SWPC), Korea Astronomy and Space Science Institute (KASI) installed a Scintillation Monitor (SCINTMON) and an All-Sky Camera to observe upper atmospheric/ionospheric phenomena. The SCINTMON is installed in KASI building in Daejeon in cooperation with Cornell university and is monitoring the ionospheric scintillations on GPS L-band signals. All-Sky Camera is installed at Mt. Bohyun in Youngcheon in cooperation with Korea Polar Research Institute. It is used to take the photograph for upper atmospheric layer through appropriate filters with specific airglow or auroral emission wavelengths and to observe upper atmospheric disturbance, propagation of gravity wave and aurora. The integrated data from the instruments including SCINTMON and All-Sky Camera will be used for giving nowcast on the space weather and making confidential forecast based on some space weather prediction models.

• International Journal of Highway Engineering
• /
• v.20 no.2
• /
• pp.127-135
• /
• 2018

PURPOSES : This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS : Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS : Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.64 no.1
• /
• pp.107-112
• /
• 2015

This paper introduces an evolutionary nonlinear regression based compensation technique for the short-range prediction of wind speed using AWS(Automatic Weather Station) data. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, but a linear regression based MOS is hard to manage an irregular nature of weather prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP(Genetic Programming) is suggested for a development of MOS wind forecast guidance. Also FCM(Fuzzy C-Means) clustering is adopted to mitigate bias of wind speed data. The purpose of this study is to evaluate the accuracy of the estimation by a GP based nonlinear MOS for 3 days prediction of wind speed in South Korean regions. This method is then compared to the UM model and has shown superior results. Data for 2007-2009, 2011 is used for training, and 2012 is used for testing.

• Journal of Environmental Science International
• /
• v.23 no.12
• /
• pp.1999-2014
• /
• 2014

A meteo-tsunami occurred along the coastline of South Korea on 31 March 2007, with an estimated maximum amplitude of 240 cm in Yeonggwang (YG). In this study, we investigated the synoptic weather systems around the Yellow sea including the Bohai Bay and Shandong Peninsula using a weather research and forecast model and weather charts of the surface pressure level, upper pressure level and auxiliary analysis. We found that 4-lows passed through the Yellow sea from the Shandung Peninsula to Korea during 5 days. Moreover, the passage of the cold front and the locally heavy rain with a sudden pressure change may make the resonance response in the near-shore and ocean with a regular time-lag. The sea-level pressure disturbance and absolute vorticity in 500 hPa projected over the Yellow sea was propagated with a similar velocity to the coastline of South Korea at the time that meteo-tsunami occurred.

• Atmosphere
• /
• v.18 no.2
• /
• pp.147-158
• /
• 2008

Domestic IOP (intensive observing period) has mostly been represented by the KEOP (Korea Enhanced Observing Period), which started the 5-yr second phase in 2006 after the first phase (2001-2005). During the first phase, the KEOP had focused on special observations (e.g., frontal systems, typhoons, etc.) around the Haenam supersite, while extended observations have been attempted from the second phase, e.g., mountain and downstream meteorology in 2006 and heavy rainfall in the mid-central region and marine meteorology in 2007. So far the KEOP has collected some useful data for severe weather systems in Korea, which are very important in understanding the development mechanisms of disastrous weather systems moving into or developing in Korea. In the future, intensive observations should be made for all characteristic weather systems in Korea including the easterly in the central-eastern coastal areas, the orographically-developed systems around mountains, the heavy snowfall in the western coastal areas, the upstream/downstream effect around major mountain ranges, and the heavy rainfall in the mid-central region. Enhancing observations over the seas around the Korean Peninsula is utmost important to improve forecast accuracy on the weather systems moving into Korea through the seas. Observations of sand dust storm in the domestic and the source regions are also essential. Such various IOPs should serve as important components of international field campaign such as THORPEX (THe Observing system Research and Predictability EXperiment) through active international collaborations.

• Solar Energy
• /
• v.12 no.2
• /
• pp.1-8
• /
• 1992

Identifying characteristics of heating and cooling systems requires estimation of thermal load of specific time interval, especially in cases that its system is operated intermittently, by using thermal storage, of in a partial load condition. Estimating the thermal load, however, needs to forecast hourly weather data variation. Hence, this paper attempts to examine characteristics of hourly ourdoor temperature variation as a preliminary research for the mathematical modeling of the hourly weather variation. Speculating characteristics of daily minimum and maximum temperature occurances, hourly outdoor temperature variation, and daily temperature differences in the increasing range ($07h{\sim}15h$) and decreasing range($15h{\sim}07h$), we were able to analyze changing patterns of daily temperature differences in each range in terms of daily solar amount, cloud ratio, and other weather data. Results from the multiple regression analysis enables us to conclude that daily differences in the increasing range are strongly affected last night temperature itself while the other range's differences are influenced by many weather data, which are solar amount, the variation of cloud, and the maximum temperature of the previous day.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.6 no.4
• /
• pp.242-249
• /
• 2004

This study was conducted to develop a forest fire occurrence model using meteorological characteristics for the practical purpose of forecasting forest fire danger. Forest fire in South Korea is highly influenced by humidity, wind speed, and temperature. To effectively forecast forest fire occurrence, we need to develop a forest fire danger rating model using weather factors associated with forest fire. Forest fore occurrence patterns were investigated statistically to develop a forest fire danger rating index using time series weather data sets collected from 8 meteorological observation centers. The data sets were for 5 years from 1997 through 2001. Development of the forest fire occurrence probability model used a logistic regression function with forest fire occurrence data and meteorological variables. An eight-province probability model by was developed. The meteorological variables that emerged as affective to forest fire occurrence are effective humidity, wind speed, and temperature. A forest fire occurrence danger rating index of through 10 was developed as a function of daily weather index (DWI).

• Atmosphere
• /
• v.16 no.3
• /
• pp.247-257
• /
• 2006

The Yeong-dong heavy snowfall forecast supporting system has been developed during the last several years. In order to construct the conceptual model, we have examined the characteristics of heavy snowfalls in the Yeong-dong region classified into three precipitation patterns. This system is divided into two parts: forecast and observation. The main purpose of the forecast part is to produce value-added data and to display the geography based features reprocessing the numerical model results associated with a heavy snowfall. The forecast part consists of four submenus: synoptic fields, regional fields, precipitation and snowfall, and verification. Each offers guidance tips and data related with the prediction of heavy snowfalls, which helps weather forecasters understand better their meteorological conditions. The observation portion shows data of wind profiler and snow monitoring for application to nowcasting. The heavy snowfall forecast supporting system was applied and tested to the heavy snowfall event on 28 February 2006. In the beginning stage, this event showed the characteristics of warm precipitation pattern in the wind and surface pressure fields. However, we expected later on the weak warm precipitation pattern because the center of low pressure passing through the Straits of Korea was becoming weak. It was appeared that Gangwon Short Range Prediction System simulated a small amount of precipitation in the Yeong-dong region and this result generally agrees with the observations.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_2
• /
• pp.781-791
• /
• 2022

It is crucial to provide forest fire risk forecast information to minimize forest fire-related losses. In this research, forecast models of forest fire risk at a mid-range (with lead times up to 7 days) scale were developed considering past, present and future conditions (i.e., forest fire risk, drought, and weather) through random forest machine learning over South Korea. The models were developed using weather forecast data from the Global Data Assessment and Prediction System, historical and current Fire Risk Index (FRI) information, and environmental factors (i.e., elevation, forest fire hazard index, and drought index). Three schemes were examined: scheme 1 using historical values of FRI and drought index, scheme 2 using historical values of FRI only, and scheme 3 using the temporal patterns of FRI and drought index. The models showed high accuracy (Pearson correlation coefficient >0.8, relative root mean square error <10%), regardless of the lead times, resulting in a good agreement with actual forest fire events. The use of the historical FRI itself as an input variable rather than the trend of the historical FRI produced more accurate results, regardless of the drought index used.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.3
• /
• pp.617-624
• /
• 2017

The production of cabbage, which is mainly cultivated in open field, varies greatly depending on weather conditions, and the price fluctuation is largely due to the presence of a substitute crop. Previous studies predicted the production of cabbage using actual weather data, but in this study, we predicted the wholesale price using unstructured agricultural meteorological data on the web. From January 2009 to October 2016, we collected documents including the cabbage on the portal site, and extracted keywords related to weather in the collected documents. We compared the forecast wholesale prices of simple models and unstructured agricultural weather models at the time of shipment. The simple model is AR model using only wholesale price, and the unstructured agricultural weather model is AR model using unstructured agricultural weather data additionally. As a result, the performance of unstructured agricultural weather model was has been found to be more accurate prediction ability.