• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.2
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• pp.53-62
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• 2012

Demand for high resolution weather data grows in the agriculture and forestry fields. Local Analysis and Prediction System (LAPS) can be used to analyze the local weather at high spatial and temporal resolution, utilizing the data from various sources including numerical weather prediction models, wind or temperature profilers, Automated Weather Station (AWS) networks, radars, and satellites. LAPS has been set to analyze weather elements such as air temperature, relative humidity, wind speed, and wind direction every hour at the spatial resolution of $100m{\times}100m$ for Gyeonggi-do on near real-time basis. The AWS data were revised by adding the agricultural field AWS data (33 stations) in addition to the KMA data. The analysis periods were from 1 to 31 August 2009 and from 15 to 21 February 2010. The comparison of the LAPS output showed the smaller errors when using the agricultural AWS observation data together with the KMA data as its input data than using only either the agricultural or KMA AWS data. The accuracy of the current system needs improvement by further optimization of analyzing options of the system. However, the system is highly applicable to various fields in agriculture and forestry because it can provide site specific data with reasonable time intervals.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.271-276
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• 2009

An local area weather condition monitoring system to minimize many disasters from the sudden change of weather condition in local and mountain area is proposed. Firstly, the comparison of present state of the related monitoring systems and the possibility of realization with some merits are investigated. Moreover, this paper present direction of local area weather condition monitoring system based on integration of wireless sensor network and CDMA network following some case study. Through the efficient integration of both networks, the measured weather condition data from sensors can be transmitted to the server or mobile to monitor with high reliability. The proposed monitoring system will guide new type of project in wireless sensor network and support alarm service of the sudden change of weather condition to mobile user from central official regulations.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.28 no.1
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• pp.45-58
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• 2020

According to the Ministry of Land, Infrastructure and Transport press release ('18.12.21.) The amendment of the Aviation Business Act will reduce the capital requirements for aviation leisure operators and make it easier to enter aviation leisure businesses by improving regulations on small air transportation business. In addition, as the scale of the UAV(Unmanned Aerial Vehicle) sector is expected to increase globally, the dramatic increase in low altitude operating aircraft, including this, must be taken into account. The low altitude aircraft category is divided into small airplanes, helicopters, light aircrafts and ultra-light aircrafts, and instructors include school instructor pilots and student pilots, military and national helicopter pilots, and aviation leisure operators. In case of low altitude aircraft, there are cases of canceling operations due to low visibility and low clouds, and aircraft accidents due to excessive operation and sudden weather phenomenon. Therefore, in order to prevent low-altitude aircraft accidents, a safe flight plan based on weather conditions and weather forecasts and more accurate and local weather forecasts and weather forecast data are needed to prepare for the rapidly changing weather conditions.

• Atmosphere
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• v.29 no.2
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• pp.183-196
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• 2019

High-quality and high-resolution meteorological information is essential to reduce damages due to disastrous weather phenomena such as flash flood, strong wind, and heat/cold waves. There are many meteorological observation stations operated by Korea Meteorological Administration (KMA) in Seoul Metropolitan Area (SMA). Nonetheless, they are still not enough to represent small-scale weather phenomena like convective storm cells due to its poor resolution, especially over urban areas with high-rise buildings and complex land use. In this study, feasibilities to use additional pre-existing networks (e.g., operated by local government and private company) are tested by investigating the effects of network density on the gridded horizontal distribution of two meteorological variables (temperature and precipitation). Two heat wave event days and two precipitation events are chosen, respectively. And the automatic weather station (AWS) networks operated by KMA, local-government, and SKTechX in Incheon area are used. It is found that as network density increases, correlation coefficients between the interpolated values with a horizontal resolution of 350 m and observed data also become large. The range of correlation coefficients with respect to the network density shows large in nighttime rather than in daytime for temperature. While, the range does not depend on the time of day, but on the precipitation type and horizontal distribution of convection cells. This study suggests that temperature and precipitation sensors should be added at points with large horizontal inhomogeneity of land use or topography to represent the horizontal features with a resolution higher than 350 m.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1492-1499
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• 2020

A weather radar system generally shows the weather phenomena related with rainfall and wind velocity. These systems are usually very helpful to monitor the relatively high altitude weather situation for the wide and long range area. However, since the weather hazards due to the strong hail and heavy rainfall occurring locally are observed frequently in recent days, it is important to detect these wether phenomena. For this purpose, it is necessary to detect the fast varying low altitude weather conditions. In this environment, the effect of surface clutter is more evident and the antenna dwell time is much shorter. Therefore, the conventional Doppler spectrum estimation method may cause serious problems. In this paper, the AR(autoregressive) Doppler spectrum estimation methods were applied to solve these problems and the results were analyzed. Applied methods show that improved Doppler spectra can be obtained comparing with the conventional FFT(Fast Fourier Transform) method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.5
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• pp.38-48
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• 2010

Adverse weather (e.g. strong winds, snow and ice) will probably appear as a more serious and frequent threat to road traffic than in clear climate. Another consequence of climate change with a natural disastrous on road traffic is respond to traffic accident more the large and high-rise bridge zone, tunnel zone, inclined plane zone and de-icing zone than any other zone, which in turn calls for continuous adaption of monitoring procedures. Accident mitigating measures against this accident category may consist of intense winter maintenance, the use of road weather information systems for data collection and early warnings, road surveillance and traffic control. While hazard from reduced road friction due to snow and ice may be eliminated by snow removal and de-icing measures, the effect of strong winds on road traffic are not easily avoided. The purpose of the study described here, was to design of amber information the relationship between traffic safety, weather, user information on road weather and driving conditions in local-scale Geographic. The most applications are the optimization of the amber information definition, improvements to road surveillance, road weather monitoring and improved accuracy of user information delivery. Also, statistics on wind gust, surface condition, vehicle category and other relevant parameters for wind induced accidents provide basis for traffic control, early warning policies and driver education for improved road safety at bad weather-exposed locations.

• Atmosphere
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• v.23 no.4
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• pp.413-424
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• 2013

Urban Heat Island (UHI) intensity is one of vital parameters in studying urban boundary layer meteorology as well as urban planning. Because the UHI intensity is defined as air temperature difference between urban and rural sites, an objective sites selection criterion is necessary for proper quantification of the spatial variations of the UHI intensity. This study quantified the UHI intensity and its spatial pattern, and then analyzed their connections with urban structure and metabolism in Seoul metropolitan area where many kinds of land use and land cover types coexist. In this study, screen-level temperature data in non-precipitation day conditions observed from 29 automatic weather stations (AWS) in Seoul were analyzed to delineate the characteristics of UHI. For quality control of the data, gap test, limit test, and step test based on guideline of World Meteorological Organization were conducted. After classifying all stations by their own local climatological properties, UHI intensity and diurnal temperature range (DTR) are calculated, and then their seasonal patterns are discussed. Maximum UHI intensity was $4.3^{\circ}C$ in autumn and minimum was $3.6^{\circ}C$ in spring. Maximum DTR appeared in autumn as $3.8^{\circ}C$, but minimum was $2.3^{\circ}C$ in summer. UHI intensity and DTR showed large variations with different local climate zones. Despite limited information on accuracy and exposure errors of the automatic weather stations, the observed data from AWS network represented theoretical UHI intensities with difference local climate zone in Seoul.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.1
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• pp.48-55
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• 2009

In order to keep safe driving conditions under road networks, there are several formations such as road structure, road surface condition, traffic occupancy and supplement of an accurate information of traffic status ahead To support safe-driving on each road formation, each formation is supplied with various information to help the driver. However, in some cases like rapid status change at local-scale geography, traffic information systems often displays insufficient information because of the lack of information correlation. In order to accurately aware the driver, all road formation must be in sync. It is important to supply accurate information to the driver because this information directly impacts the drivers on the road. This paper discusses the amber information to keep the least safety driving over road formations including tunnels and bridges. This paper also will propose the informations for safe-driving conditions, information linkage on the road and rule-base safety information, as ITS technology, being displayed for all drivers under the worst weather conditions.

• Atmosphere
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• v.26 no.3
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• pp.445-459
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• 2016

Air temperature deviation (ATD) is one of major indicators to represent spatial distribution of urban heat island (UHI), which is induced from the urbanization. The purpose of this study is to evaluate the accuracy of air temperature deviation about Climate Analysis Seoul (CAS) workbench, which had developed by National Institute Meteorological Science and TU Berlin. Comparison and correlation analysis for CAS ATD including meso-scale air temperature deviation, local-scale air temperature deviation, total air temperature deviation, surface heat flux deviation, cold air production deviation among meso-scale numerical modelling variable in 'Seoul Region', micro-scale numerical modelling in 'Detail Region', and CAS workbench variable using observation data in ground stations. Comparison between night time OBS ATD and CAS ATD show that have most close values. Most of observations ($dT_{max}$ and $dT_{min}$) have highly positive ($dT_{SHP}$, $dT_{CA}$, MD, TD, $f_{BS}$, $f_{US}$, $f_{WS}$, $h_B$) and negative ($f_{VS}$, $f_{TV}$, $h_V$, Z) correlations. However, CAS workbench needs further improvement of both observational framework and analytical framework to resolve the problems; (1) night time OBS ATD of has closer values in compare with at high rise mountain area and (2) correlations are very dependable to meteorological scale.