• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.562-568
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• 2014

There exist precipitation echo and non-precipitation echo in the meteorological radar. It is difficult to effectively issue the right weather forecast because of a difficulty in determining these ambiguous point. In this study, Data is extracted from UF data of meteorological radar used. Input and output data for designing two classifier were built up through the analysis of the characteristics of precipitation and non-precipitation. Selected input variables are considered for better performance and echo classifier is designed using fuzzy relation-based nueral network. Comparative studies on the performance of echo classifier are carried out by considering both echo judgement module 1 and module 2.

• Magazine of the Korean Society of Agricultural Engineers
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• v.41 no.6
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• pp.44-53
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• 1999

The statistical characteristics of the factors related to the daily rainfall prediction model are analyzed . Records of daily precipitation, mean air temperature, relative humidity , dew-point temperature and air pressure from 1973∼1998 at 8 meteorological sttions in south-western part of Korea were used. 1. Serial correlatino of daily precipitaiton was significant with the lag less than 1 day. But , that of other variables were large enough until 10 day lag. 2. Crosscorrelation of air temperature, relative humidity , dew-point temperature showed similar distribution wiht the basin contrours and the others were different. 3. There were significant correlation between the meteorological variables and precipitation preceded more than 2 days. 4. Daily preciption of each station were treated as a truncated continuous random variable and the annual periodic components, mean and standard deviation were estimated for each day. 5. All of the results could be considered to select the input variables of regression model or neural network model for the prediction of daily precipitation and to construct the stochastic model of daily precipitation.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.210-211
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• 2019

• Atmosphere
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• v.15 no.4
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• pp.213-227
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• 2005

The global mean surface temperature has already increased by $0.6{\pm}0.2^{\circ}C$ over the last century, and warming in Korea is approximately twice as large as the global average. The Intergovernmental Panel on Climate Change (IPCC) has concluded that the majority of warming over the past 50 years could be attributed to human activities (IPCC, 2001a). In addition, the global surface temperature is expected to increase by 1.4 to $5.8^{\circ}C$ depending on the greenhouse gas emission scenarios during the $21^{st}$ century.Climate change resulting from increased greenhouse gas concentrations has the potential to harm societies and ecosystems. Reductions in emissions of greenhouse gases and their concentration in the atmosphere will reduce the degree and likelihood of significant adverse conditions due to the anticipated climate change. Mitigation policy has generally been the primary focus of public attention and policy efforts on climate change. However, some degree of climate change is inevitable due to the combination of continued increases in emissions and the inertia of the global climate system. Adaptation actions and strategies are needed for a complementary approach to mitigation. The United Nations Framework Convention on Climate Change (UNFCCC) currently addresses vulnerability and adaptation in the context of climate change negotiations and in future adaptation may be an important element of work under the Kyoto Protocol. There are several on-going programs to develop effective adaptation strategies and their implementation. But in general, many other countries are still on an initiating stage. The climate change science programs of the United States, Japan, England, and Germany are initiated to understand the current status of climate change science and adaptation researches in the developed countries. In this study, we propose the improvement on systems in policy and research aspects to effectively perform the necessary functions for development of nation-wide adaptation measures and their implementation. In policy aspect, the Korean Panel on Climate Change (KPCC) is introduced as a coordinating mechanism between government organizations related with climate change science, impact assessment and adaptation. Also in research aspect, there is a strong consensus on the need for construction of a national network on climate change research as trans-disciplinary research network.

• Journal of Korean Society for Atmospheric Environment
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• v.20 no.2
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• pp.215-224
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• 2004

We report the high concentration episodes for PM$_{10}$, SO$_2$, NO$_2$, and $O_3$ in many urban areas Korea during 2002. The high concentration episodes are identified based on the National Ambient Air Quality Standards and the observations obtained from the Regional Air Monitoring Network composed of approximately 160 air pollution monitoring stations located in a number of major or big cities in South Korea including Seoul, Pusan, Daegu, and Incheon cities. The results show that the twenty cases of high concentration episodes in 2002 consists of both ozone warning episodes (6 cases) and high PM$_{10}$ concentration cases (14 cases), and one half of the latter are found to occur in association with the Yellow Sand (Asian Dust) phenomena. The most outstanding characteristics of the reported episodes are the excessively high levels of maximum PM$_{10}$ concentrations during the Yellow Sand period (i.e., exceeding 3,000$\mu\textrm{g}$/㎥ in April, 2002) and their variable occurrence frequencies across seasons. The high ozone concentration episode days are mainly resulting from both the high photochemical reactions and poor ventilations. The high PM$_{10}$ concentration days during non Yellow Sand periods, however, mostly occurred under the influence of synoptic meteorological conditions such as stagnant or slowly passing high pressure centers, and consequently prevailing weak wind speeds over the Korean peninsula. The overall results of our study thus suggest the importance of both synoptic and local meteorological factors for high concentration levels in the major and/or big cities in Korea.n Korea.

• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.491-499
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• 2018

Runway visual range (RVR), one of the important indicators of aircraft takeoff and landing, is affected by meteorological conditions such as temperature, humidity, etc. It is important to estimate the RVR at the time of arrival in advance. This study estimated the RVR of the local airport after 1 hour by upgrading the RVR estimation model using the proposed deep learning network. To this end, the advancement of the estimation model was carried out by changing the time interval of the meteorological data (temperature, humidity, wind speed, RVR) as input value and the linear conversion of the results. The proposed method generates estimation model based on the past measured meteorological data and estimates the RVR after 1 hour and confirms its validity by comparing with measured RVR after 1 hour. The proposed estimation model could be used for the RVR after 1 hour as reference in small airports in regions which do not forecast the RVR.

• Atmosphere
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• v.20 no.3
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• pp.343-353
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• 2010

A regional wind network with complex surface conditions must be designed with sufficient space and time resolution to resolve the local circulations. In this study, the spatial variations of the wind field observed in the Seoul and Jeju regional networks were evaluated in terms of annual, seasons, and months to assess the spatial homogeneity of wind fields within the regional networks. The coherency of the wind field as a function of separation distance between stations indicated that significant coherency was sometimes not captured by the network, as inferred by low correlations between adjacent stations. A meso-velocity scale was defined in terms of the spatial variability of the wind within the network. This problem is predictably most significant with weak winds, dull prevailing wind, clear skies and significant topography. The relatively small correlations between stations imply that the wind at a given point cannot be estimated by interpolating winds from the nearest stations. For the Seoul and Jeju regional network, the meso-velocity scale has typically a same order of magnitude as the speed of the network averaged wind, revealing the large spatial variability of the Jeju network station imply topography and weather. Significant scatter in the relationship between spatial variability of the wind field and the wind speed is thought to be related to thermally-generated flows. The magnitude of the mesovelocity scale was significantly different along separation distance between stations, wind speed, intensity of prevailing wind, clear and cloudy conditions, topography. Resultant wind vectors indicate much different flow patterns along condition of contributing factors. As a result, the careful considerations on contributing factors such as prevailing wind in season, weather, and complex surface conditions with topography and land/sea contrast are required to assess the spatial variations of wind field on a regional network. The results in the spatial variation from the mesovelocity scale are useful to represent the characteristics of regional wind speed including lower surface conditions over the grid scale of large scale atmospheric model.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1208-1215
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• 2017

Recently, people's attention and worries about fine particulate matter have been increasing. Due to the construction and maintenance costs, there are insufficient air quality monitoring stations. As a result, people have limited information about the concentration of fine particulate matter, depending on the location. Studies have been undertaken to estimate the fine particle concentrations in areas without a measurement station. Yet there are limitations in that the estimate cannot take account of other factors that affect the concentration of fine particle. In order to solve these problems, we propose a framework for estimating the concentration of fine particulate matter of a specific area using meteorological data and traffic data. Since there are more grids without a monitor station than grids with a monitor station, we used a domain adversarial neural network based on the domain adaptation method. The features extracted from meteorological data and traffic data are learned in the network, and the air quality index of the corresponding area is then predicted by the generated model. Experimental results demonstrate that the proposed method performs better as the number of source data increases than the method using conditional random fields.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.6
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• pp.1327-1332
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• 2011

In the mobile meteorological information services, an existing sensor system should be expanded to serve precise information. This is because of large cost problem to add the existing sensors. This thesis proposes a system architecture to construct scalable mobile meteorological information services and suggests mobile phone, sensor network, and public traffic vehicle as expended sensors. The proposed scalable sensors are compared each other and analysed the results to evaluated their strength and weakness. In this system, based on the characteristics of the sense data collected at server, a data processing methods are proposed to support efficient query processing. The proposing data processing methods have several benefits. They compress some data volume sensed by various sources on some area at some time to a logical unit. Still it could preserve same services quality.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.99-106
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• 2015

In this study, we propose Radial basis function Neural Network(RBFNN) using Recursive Weighted Least Square Estimation(RWLSE) to effectively deal with big data class meteorological radar data. In the condition part of the RBFNN, Fuzzy C-Means(FCM) clustering is used to obtain fitness values taking into account characteristics of input data, and connection weights are defined as linear polynomial function in the conclusion part. The coefficients of the polynomial function are estimated by using RWLSE in order to cope with big data. As recursive learning technique, RWLSE which is based on WLSE is carried out to efficiently process big data. This study is experimented with both widely used some Machine Learning (ML) dataset and big data obtained from meteorological radar to evaluate the performance of the proposed classifier. The meteorological radar data as big data consists of precipitation echo and non-precipitation echo, and the proposed classifier is used to efficiently classify these echoes.